2-intro-geopandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to geopandas for analyzing spatial data\n",
    "\n",
    "[*Geopandas*](https://github.com/geopandas/geopandas) is an excellent extension to the [*Pandas*](https://github.com/pandas-dev/pandas) package for working with tabular data that is georeferenced - for example, Points, Lines, and Polygons that have associated attributes. Geopandas builds upon other great libraries including [*Shapely*](https://github.com/Toblerity/Shapely) and [*Fiona*](https://github.com/Toblerity/Fiona).\n",
    "\n",
    "This notebook is intended as an introduction to Geopandas for the [Data Science for Social Good (DSSG 2018)](http://escience.washington.edu/get-involved/incubator-programs/data-science-for-social-good/) summer project.\n",
    "\n",
    "There are a lot of tutorials out there: Check out this lesson from a [eScience DSSG tutorial](https://uwescience.github.io/SQL-geospatial-tutorial/) or the [eScience Geohackweek tutorial](https://geohackweek.github.io/vector/).\n",
    "\n",
    "\n",
    "\n",
    "## This notebook covers the following topics:\n",
    "\n",
    "* dataset exploration\n",
    "* basic statistical analysis\n",
    "* simple geometric operations (convex_hull)\n",
    "* exporting data for GIS software\n",
    "* converting coordinates and calculating distance and area\n",
    "* attribute and spatial joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the libraries we are going to use\n",
    "import geopandas as gpd\n",
    "import shapely\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geopandas version:  0.3.0\n",
      "Pandas version:  0.23.0\n"
     ]
    }
   ],
   "source": [
    "# First thing to note is that many geospatial python packages are under active development\n",
    "# and updated regularly. So the version you're using today will likely be updated in the near \n",
    "# future!\n",
    "print('Geopandas version: ', gpd.__version__)\n",
    "print('Pandas version: ', gpd.pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 valentina  staff   2.6M Jun 19 10:41 ./Data/Dist_Out.csv\r\n",
      "-rw-r--r--  1 valentina  staff   181K Jun 19 10:41 ./Data/Places_Full.csv\r\n"
     ]
    }
   ],
   "source": [
    "# Geopandas has convenient methods for reading tabluar data, in this case we have 2 CSV files:\n",
    "!ls -lh ./Data/*csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "head: Places_Full.csv: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "# Note that the 'places' information has 9 columns with labels in the first row\n",
    "!head Places_Full.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "head: Dist_Out.csv: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "# And the distance data has information about transit between two places\n",
    "!head Dist_Out.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's work with the smaller 'Places_Full.csv' first\n",
    "# All Pandas methods are accessed via the 'pd' attribute.\n",
    "# Since the file is well-formatted, it is easily read into memory:\n",
    "filePath = './data/Places_Full.csv'\n",
    "df = gpd.pd.read_csv(filePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>place_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>class</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trader Joe's</td>\n",
       "      <td>1700 East Madison Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.615866</td>\n",
       "      <td>-122.309913</td>\n",
       "      <td>ChIJx0M1ztNqkFQRtgspEllQxk8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hillcrest Market</td>\n",
       "      <td>110 Summit Avenue East</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.618850</td>\n",
       "      <td>-122.325005</td>\n",
       "      <td>ChIJt5emCTMVkFQR-BCgFDTvu9o</td>\n",
       "      <td>3.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uwajimaya</td>\n",
       "      <td>600 5th Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.596843</td>\n",
       "      <td>-122.326929</td>\n",
       "      <td>ChIJq9nX27xqkFQRu05rxkrN7f4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kress IGA Supermarket</td>\n",
       "      <td>1427 3rd Avenue</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.609396</td>\n",
       "      <td>-122.337822</td>\n",
       "      <td>ChIJbdIhoLNqkFQRMTiHCN6W4nU</td>\n",
       "      <td>3.9</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Double Dorjee</td>\n",
       "      <td>1501 Pike Street # 511</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.608822</td>\n",
       "      <td>-122.339570</td>\n",
       "      <td>ChIJkX3s-LJqkFQRQRexqCN0jQY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name                   address     city        lat  \\\n",
       "0           Trader Joe's  1700 East Madison Street  Seattle  47.615866   \n",
       "1       Hillcrest Market    110 Summit Avenue East  Seattle  47.618850   \n",
       "2              Uwajimaya      600 5th Avenue South  Seattle  47.596843   \n",
       "3  Kress IGA Supermarket           1427 3rd Avenue  Seattle  47.609396   \n",
       "4          Double Dorjee    1501 Pike Street # 511  Seattle  47.608822   \n",
       "\n",
       "          lng                     place_id  rating        class type  \n",
       "0 -122.309913  ChIJx0M1ztNqkFQRtgspEllQxk8     4.5  supermarket  NaN  \n",
       "1 -122.325005  ChIJt5emCTMVkFQR-BCgFDTvu9o     3.5  supermarket  NaN  \n",
       "2 -122.326929  ChIJq9nX27xqkFQRu05rxkrN7f4     4.5  supermarket  NaN  \n",
       "3 -122.337822  ChIJbdIhoLNqkFQRMTiHCN6W4nU     3.9  supermarket  NaN  \n",
       "4 -122.339570  ChIJkX3s-LJqkFQRQRexqCN0jQY     5.0  supermarket  NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'df' stands for 'Data Frame'. It is essentially a spreadsheet:\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1616 entries, 0 to 1615\n",
      "Data columns (total 9 columns):\n",
      "name        1616 non-null object\n",
      "address     1520 non-null object\n",
      "city        1540 non-null object\n",
      "lat         1616 non-null float64\n",
      "lng         1616 non-null float64\n",
      "place_id    1616 non-null object\n",
      "rating      1449 non-null float64\n",
      "class       1616 non-null object\n",
      "type        15 non-null object\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 113.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# The dataframe has a lot of convenient methods for fast data exploration\n",
    "# Start with info to confirm that things were read in correctly\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1616.000000</td>\n",
       "      <td>1616.000000</td>\n",
       "      <td>1449.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.619907</td>\n",
       "      <td>-122.321132</td>\n",
       "      <td>4.194134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.056363</td>\n",
       "      <td>0.049129</td>\n",
       "      <td>0.638952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>47.455556</td>\n",
       "      <td>-122.411493</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.586566</td>\n",
       "      <td>-122.347183</td>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.614611</td>\n",
       "      <td>-122.328568</td>\n",
       "      <td>4.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.661334</td>\n",
       "      <td>-122.309480</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>47.790207</td>\n",
       "      <td>-122.114213</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               lat          lng       rating\n",
       "count  1616.000000  1616.000000  1449.000000\n",
       "mean     47.619907  -122.321132     4.194134\n",
       "std       0.056363     0.049129     0.638952\n",
       "min      47.455556  -122.411493     1.000000\n",
       "25%      47.586566  -122.347183     3.900000\n",
       "50%      47.614611  -122.328568     4.300000\n",
       "75%      47.661334  -122.309480     4.600000\n",
       "max      47.790207  -122.114213     5.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple statistics are obtained for numerical columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Seattle' 'Medina' 'Mercer Island' 'Shoreline' 'Edmonds' 'Tukwila'\n",
      " 'Kenmore' 'Kirkland' 'Lake Forest Park' 'Bothell' 'Renton' 'Bellevue'\n",
      " 'Newcastle' 'SeaTac' 'Burien' nan]\n",
      "['Seattle' 'Medina' 'Mercer Island' 'Shoreline' 'Edmonds' 'Tukwila'\n",
      " 'Kenmore' 'Kirkland' 'Lake Forest Park' 'Bothell' 'Renton' 'Bellevue'\n",
      " 'Newcastle' 'SeaTac' 'Burien' nan]\n"
     ]
    }
   ],
   "source": [
    "# Columns can be accessed as dictionary items:\n",
    "print(df['city'].unique())\n",
    "# Or accessed as attributes for faster typing:\n",
    "print(df.city.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['supermarket' 'library' 'hospital' 'pharmacy' 'post_office' 'school'\n",
      " 'cafe' 'urban village' 'destination park' 'citywide']\n"
     ]
    }
   ],
   "source": [
    "# NOTE: seems there is bug in v 0.3 since the attribute 'class' \n",
    "# is reserved for internal use, but it is also a column heading\n",
    "print(df['class'].unique())\n",
    "#print(df.class.unique()) # This causes an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['supermarket', 'library', 'hospital', 'pharmacy', 'post_office',\n",
       "       'school', 'cafe', 'urban village', 'destination park', 'citywide'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's change the name of class to avoid that error\n",
    "df.rename(columns={'class':'place_class'}, inplace=True)\n",
    "df.place_class.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Seattle' 'Medina' 'Mercer Island' 'Shoreline' 'Edmonds' 'Tukwila'\n",
      " 'Kenmore' 'Kirkland' 'Lake Forest Park' 'Bothell' 'Renton' 'Bellevue'\n",
      " 'Newcastle' 'SeaTac' 'Burien' nan]\n"
     ]
    }
   ],
   "source": [
    "# Another common issue with tabular data - \n",
    "# certain measurements don't always fit into the defined columns \n",
    "# or are missing data, and therefore filled with 'not-a-number (nan)'\n",
    "# For example, some entries don't have a listed city:\n",
    "print(df.city.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>1540</th>\n",
       "      <td>12th Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.608315</td>\n",
       "      <td>-122.317345</td>\n",
       "      <td>12th-Avenue</td>\n",
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       "      <td>urban village</td>\n",
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       "      <th>1541</th>\n",
       "      <td>23rd &amp; Union-Jackson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.603145</td>\n",
       "      <td>-122.306682</td>\n",
       "      <td>23rd-&amp;-Union-Jackson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>Admiral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.582350</td>\n",
       "      <td>-122.386420</td>\n",
       "      <td>Admiral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>Aurora-Licton Springs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.696854</td>\n",
       "      <td>-122.345977</td>\n",
       "      <td>Aurora-Licton-Springs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>Ballard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.670593</td>\n",
       "      <td>-122.382603</td>\n",
       "      <td>Ballard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>Ballard-Interbay-Northend</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.659726</td>\n",
       "      <td>-122.372020</td>\n",
       "      <td>Ballard-Interbay-Northend</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>Belltown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.614435</td>\n",
       "      <td>-122.347341</td>\n",
       "      <td>Belltown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>Bitter Lake Village</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.728560</td>\n",
       "      <td>-122.350653</td>\n",
       "      <td>Bitter-Lake-Village</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.620316</td>\n",
       "      <td>-122.319866</td>\n",
       "      <td>Capitol-Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>Chinatown-International District</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.597980</td>\n",
       "      <td>-122.325308</td>\n",
       "      <td>Chinatown-International-District</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>Columbia City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.559494</td>\n",
       "      <td>-122.288529</td>\n",
       "      <td>Columbia-City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>Commercial Core</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.607792</td>\n",
       "      <td>-122.335763</td>\n",
       "      <td>Commercial-Core</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>Crown Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.690125</td>\n",
       "      <td>-122.377021</td>\n",
       "      <td>Crown-Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>Denny Triangle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.616836</td>\n",
       "      <td>-122.331932</td>\n",
       "      <td>Denny-Triangle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>Eastlake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.640854</td>\n",
       "      <td>-122.325625</td>\n",
       "      <td>Eastlake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>First Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.607908</td>\n",
       "      <td>-122.326042</td>\n",
       "      <td>First-Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.651807</td>\n",
       "      <td>-122.347581</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557</th>\n",
       "      <td>Greater Duwamish</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.575167</td>\n",
       "      <td>-122.333526</td>\n",
       "      <td>Greater-Duwamish</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>Green Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.679496</td>\n",
       "      <td>-122.325003</td>\n",
       "      <td>Green-Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>Greenwood-Phinney Ridge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.691479</td>\n",
       "      <td>-122.355887</td>\n",
       "      <td>Greenwood-Phinney-Ridge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>Lake City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.717466</td>\n",
       "      <td>-122.294905</td>\n",
       "      <td>Lake-City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>MLK at Holly St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.539179</td>\n",
       "      <td>-122.288571</td>\n",
       "      <td>MLK-at-Holly-St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>Madison-Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.619732</td>\n",
       "      <td>-122.307232</td>\n",
       "      <td>Madison-Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>Morgan Junction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.544011</td>\n",
       "      <td>-122.386541</td>\n",
       "      <td>Morgan-Junction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>North Beacon Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.585549</td>\n",
       "      <td>-122.314352</td>\n",
       "      <td>North-Beacon-Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>North Rainier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.585429</td>\n",
       "      <td>-122.297574</td>\n",
       "      <td>North-Rainier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>Northgate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.706097</td>\n",
       "      <td>-122.326685</td>\n",
       "      <td>Northgate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>Outside Seattle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.560094</td>\n",
       "      <td>-122.184359</td>\n",
       "      <td>Outside-Seattle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>Outside Villages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.638456</td>\n",
       "      <td>-122.335240</td>\n",
       "      <td>Outside-Villages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>Pike/Pine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.614596</td>\n",
       "      <td>-122.321012</td>\n",
       "      <td>Pike/Pine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>Madison Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.635897</td>\n",
       "      <td>-122.276901</td>\n",
       "      <td>Madison-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587</th>\n",
       "      <td>Gasworks Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.646570</td>\n",
       "      <td>-122.335308</td>\n",
       "      <td>Gasworks-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>Seward Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.550034</td>\n",
       "      <td>-122.257778</td>\n",
       "      <td>Seward-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.530419</td>\n",
       "      <td>-122.393117</td>\n",
       "      <td>Lincoln-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1590</th>\n",
       "      <td>Volunteer Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.631061</td>\n",
       "      <td>-122.315451</td>\n",
       "      <td>Volunteer-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>Lake Union Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.626200</td>\n",
       "      <td>-122.337323</td>\n",
       "      <td>Lake-Union-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>Jefferson Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.568994</td>\n",
       "      <td>-122.308655</td>\n",
       "      <td>Jefferson-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>Magnusan Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.680326</td>\n",
       "      <td>-122.253198</td>\n",
       "      <td>Magnusan-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>Cal Anderson Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.617320</td>\n",
       "      <td>-122.319570</td>\n",
       "      <td>Cal-Anderson-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>Greenlake Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.680250</td>\n",
       "      <td>-122.328499</td>\n",
       "      <td>Greenlake-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>Olympic Sculpture Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.616675</td>\n",
       "      <td>-122.355396</td>\n",
       "      <td>Olympic-Sculpture-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>Madrona Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.610256</td>\n",
       "      <td>-122.283617</td>\n",
       "      <td>Madrona-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Kubota Garden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.513127</td>\n",
       "      <td>-122.266398</td>\n",
       "      <td>Kubota-Garden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>Carkeek Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.711609</td>\n",
       "      <td>-122.372217</td>\n",
       "      <td>Carkeek-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>Myrtle Edwards Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.619871</td>\n",
       "      <td>-122.361605</td>\n",
       "      <td>Myrtle-Edwards-Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>destination park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>Seattle Central College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.616587</td>\n",
       "      <td>-122.321611</td>\n",
       "      <td>Seattle-Central-College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>North Seattle College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.699252</td>\n",
       "      <td>-122.333520</td>\n",
       "      <td>North-Seattle-College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>South Seattle College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.548410</td>\n",
       "      <td>-122.352194</td>\n",
       "      <td>South-Seattle-College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>King Street Station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.598939</td>\n",
       "      <td>-122.329158</td>\n",
       "      <td>King-Street-Station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Transit Hub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>Westlake Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.611603</td>\n",
       "      <td>-122.336910</td>\n",
       "      <td>Westlake-Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Transit Hub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>Northgate Transit Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.702358</td>\n",
       "      <td>-122.327210</td>\n",
       "      <td>Northgate-Transit-Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Transit Hub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>Downtown Bellevue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.615523</td>\n",
       "      <td>-122.199049</td>\n",
       "      <td>Bellevue-Square</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>Tukwila</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.455556</td>\n",
       "      <td>-122.254492</td>\n",
       "      <td>Tukwila</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>SODO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.583574</td>\n",
       "      <td>-122.333555</td>\n",
       "      <td>SODO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>Overlake-Redmond</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.641193</td>\n",
       "      <td>-122.128259</td>\n",
       "      <td>Overlake-Redmond</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>Downtown Pioneer Square</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.601651</td>\n",
       "      <td>-122.334226</td>\n",
       "      <td>Downtown-Pioneer-Square</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>Downtown Commercial Core</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.608001</td>\n",
       "      <td>-122.334658</td>\n",
       "      <td>Downtown-Commercial-Core</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>University of Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.655614</td>\n",
       "      <td>-122.305211</td>\n",
       "      <td>University-of-Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>First Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.609422</td>\n",
       "      <td>-122.325120</td>\n",
       "      <td>First-Hill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1615</th>\n",
       "      <td>South Lake Union</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.622933</td>\n",
       "      <td>-122.337105</td>\n",
       "      <td>South-Lake-Union</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name address city        lat         lng  \\\n",
       "1540                       12th Avenue     NaN  NaN  47.608315 -122.317345   \n",
       "1541              23rd & Union-Jackson     NaN  NaN  47.603145 -122.306682   \n",
       "1542                           Admiral     NaN  NaN  47.582350 -122.386420   \n",
       "1543             Aurora-Licton Springs     NaN  NaN  47.696854 -122.345977   \n",
       "1544                           Ballard     NaN  NaN  47.670593 -122.382603   \n",
       "1545         Ballard-Interbay-Northend     NaN  NaN  47.659726 -122.372020   \n",
       "1546                          Belltown     NaN  NaN  47.614435 -122.347341   \n",
       "1547               Bitter Lake Village     NaN  NaN  47.728560 -122.350653   \n",
       "1548                      Capitol Hill     NaN  NaN  47.620316 -122.319866   \n",
       "1549  Chinatown-International District     NaN  NaN  47.597980 -122.325308   \n",
       "1550                     Columbia City     NaN  NaN  47.559494 -122.288529   \n",
       "1551                   Commercial Core     NaN  NaN  47.607792 -122.335763   \n",
       "1552                        Crown Hill     NaN  NaN  47.690125 -122.377021   \n",
       "1553                    Denny Triangle     NaN  NaN  47.616836 -122.331932   \n",
       "1554                          Eastlake     NaN  NaN  47.640854 -122.325625   \n",
       "1555                        First Hill     NaN  NaN  47.607908 -122.326042   \n",
       "1556                           Fremont     NaN  NaN  47.651807 -122.347581   \n",
       "1557                  Greater Duwamish     NaN  NaN  47.575167 -122.333526   \n",
       "1558                        Green Lake     NaN  NaN  47.679496 -122.325003   \n",
       "1559           Greenwood-Phinney Ridge     NaN  NaN  47.691479 -122.355887   \n",
       "1560                         Lake City     NaN  NaN  47.717466 -122.294905   \n",
       "1561                   MLK at Holly St     NaN  NaN  47.539179 -122.288571   \n",
       "1562                    Madison-Miller     NaN  NaN  47.619732 -122.307232   \n",
       "1563                   Morgan Junction     NaN  NaN  47.544011 -122.386541   \n",
       "1564                 North Beacon Hill     NaN  NaN  47.585549 -122.314352   \n",
       "1565                     North Rainier     NaN  NaN  47.585429 -122.297574   \n",
       "1566                         Northgate     NaN  NaN  47.706097 -122.326685   \n",
       "1567                   Outside Seattle     NaN  NaN  47.560094 -122.184359   \n",
       "1568                  Outside Villages     NaN  NaN  47.638456 -122.335240   \n",
       "1569                         Pike/Pine     NaN  NaN  47.614596 -122.321012   \n",
       "...                                ...     ...  ...        ...         ...   \n",
       "1586                      Madison Park     NaN  NaN  47.635897 -122.276901   \n",
       "1587                     Gasworks Park     NaN  NaN  47.646570 -122.335308   \n",
       "1588                       Seward Park     NaN  NaN  47.550034 -122.257778   \n",
       "1589                      Lincoln Park     NaN  NaN  47.530419 -122.393117   \n",
       "1590                    Volunteer Park     NaN  NaN  47.631061 -122.315451   \n",
       "1591                   Lake Union Park     NaN  NaN  47.626200 -122.337323   \n",
       "1592                    Jefferson Park     NaN  NaN  47.568994 -122.308655   \n",
       "1593                     Magnusan Park     NaN  NaN  47.680326 -122.253198   \n",
       "1594                 Cal Anderson Park     NaN  NaN  47.617320 -122.319570   \n",
       "1595                    Greenlake Park     NaN  NaN  47.680250 -122.328499   \n",
       "1596            Olympic Sculpture Park     NaN  NaN  47.616675 -122.355396   \n",
       "1597                      Madrona Park     NaN  NaN  47.610256 -122.283617   \n",
       "1598                     Kubota Garden     NaN  NaN  47.513127 -122.266398   \n",
       "1599                      Carkeek Park     NaN  NaN  47.711609 -122.372217   \n",
       "1600               Myrtle Edwards Park     NaN  NaN  47.619871 -122.361605   \n",
       "1601           Seattle Central College     NaN  NaN  47.616587 -122.321611   \n",
       "1602             North Seattle College     NaN  NaN  47.699252 -122.333520   \n",
       "1603             South Seattle College     NaN  NaN  47.548410 -122.352194   \n",
       "1604               King Street Station     NaN  NaN  47.598939 -122.329158   \n",
       "1605                   Westlake Center     NaN  NaN  47.611603 -122.336910   \n",
       "1606          Northgate Transit Center     NaN  NaN  47.702358 -122.327210   \n",
       "1607                 Downtown Bellevue     NaN  NaN  47.615523 -122.199049   \n",
       "1608                           Tukwila     NaN  NaN  47.455556 -122.254492   \n",
       "1609                              SODO     NaN  NaN  47.583574 -122.333555   \n",
       "1610                  Overlake-Redmond     NaN  NaN  47.641193 -122.128259   \n",
       "1611           Downtown Pioneer Square     NaN  NaN  47.601651 -122.334226   \n",
       "1612          Downtown Commercial Core     NaN  NaN  47.608001 -122.334658   \n",
       "1613          University of Washington     NaN  NaN  47.655614 -122.305211   \n",
       "1614                        First Hill     NaN  NaN  47.609422 -122.325120   \n",
       "1615                  South Lake Union     NaN  NaN  47.622933 -122.337105   \n",
       "\n",
       "                              place_id  rating       place_class  \\\n",
       "1540                       12th-Avenue     NaN     urban village   \n",
       "1541              23rd-&-Union-Jackson     NaN     urban village   \n",
       "1542                           Admiral     NaN     urban village   \n",
       "1543             Aurora-Licton-Springs     NaN     urban village   \n",
       "1544                           Ballard     NaN     urban village   \n",
       "1545         Ballard-Interbay-Northend     NaN     urban village   \n",
       "1546                          Belltown     NaN     urban village   \n",
       "1547               Bitter-Lake-Village     NaN     urban village   \n",
       "1548                      Capitol-Hill     NaN     urban village   \n",
       "1549  Chinatown-International-District     NaN     urban village   \n",
       "1550                     Columbia-City     NaN     urban village   \n",
       "1551                   Commercial-Core     NaN     urban village   \n",
       "1552                        Crown-Hill     NaN     urban village   \n",
       "1553                    Denny-Triangle     NaN     urban village   \n",
       "1554                          Eastlake     NaN     urban village   \n",
       "1555                        First-Hill     NaN     urban village   \n",
       "1556                           Fremont     NaN     urban village   \n",
       "1557                  Greater-Duwamish     NaN     urban village   \n",
       "1558                        Green-Lake     NaN     urban village   \n",
       "1559           Greenwood-Phinney-Ridge     NaN     urban village   \n",
       "1560                         Lake-City     NaN     urban village   \n",
       "1561                   MLK-at-Holly-St     NaN     urban village   \n",
       "1562                    Madison-Miller     NaN     urban village   \n",
       "1563                   Morgan-Junction     NaN     urban village   \n",
       "1564                 North-Beacon-Hill     NaN     urban village   \n",
       "1565                     North-Rainier     NaN     urban village   \n",
       "1566                         Northgate     NaN     urban village   \n",
       "1567                   Outside-Seattle     NaN     urban village   \n",
       "1568                  Outside-Villages     NaN     urban village   \n",
       "1569                         Pike/Pine     NaN     urban village   \n",
       "...                                ...     ...               ...   \n",
       "1586                      Madison-Park     NaN  destination park   \n",
       "1587                     Gasworks-Park     NaN  destination park   \n",
       "1588                       Seward-Park     NaN  destination park   \n",
       "1589                      Lincoln-Park     NaN  destination park   \n",
       "1590                    Volunteer-Park     NaN  destination park   \n",
       "1591                   Lake-Union-Park     NaN  destination park   \n",
       "1592                    Jefferson-Park     NaN  destination park   \n",
       "1593                     Magnusan-Park     NaN  destination park   \n",
       "1594                 Cal-Anderson-Park     NaN  destination park   \n",
       "1595                    Greenlake-Park     NaN  destination park   \n",
       "1596            Olympic-Sculpture-Park     NaN  destination park   \n",
       "1597                      Madrona-Park     NaN  destination park   \n",
       "1598                     Kubota-Garden     NaN  destination park   \n",
       "1599                      Carkeek-Park     NaN  destination park   \n",
       "1600               Myrtle-Edwards-Park     NaN  destination park   \n",
       "1601           Seattle-Central-College     NaN          citywide   \n",
       "1602             North-Seattle-College     NaN          citywide   \n",
       "1603             South-Seattle-College     NaN          citywide   \n",
       "1604               King-Street-Station     NaN          citywide   \n",
       "1605                   Westlake-Center     NaN          citywide   \n",
       "1606          Northgate-Transit-Center     NaN          citywide   \n",
       "1607                   Bellevue-Square     NaN          citywide   \n",
       "1608                           Tukwila     NaN          citywide   \n",
       "1609                              SODO     NaN          citywide   \n",
       "1610                  Overlake-Redmond     NaN          citywide   \n",
       "1611           Downtown-Pioneer-Square     NaN          citywide   \n",
       "1612          Downtown-Commercial-Core     NaN          citywide   \n",
       "1613          University-of-Washington     NaN          citywide   \n",
       "1614                        First-Hill     NaN          citywide   \n",
       "1615                  South-Lake-Union     NaN          citywide   \n",
       "\n",
       "                       type  \n",
       "1540                    NaN  \n",
       "1541                    NaN  \n",
       "1542                    NaN  \n",
       "1543                    NaN  \n",
       "1544                    NaN  \n",
       "1545                    NaN  \n",
       "1546                    NaN  \n",
       "1547                    NaN  \n",
       "1548                    NaN  \n",
       "1549                    NaN  \n",
       "1550                    NaN  \n",
       "1551                    NaN  \n",
       "1552                    NaN  \n",
       "1553                    NaN  \n",
       "1554                    NaN  \n",
       "1555                    NaN  \n",
       "1556                    NaN  \n",
       "1557                    NaN  \n",
       "1558                    NaN  \n",
       "1559                    NaN  \n",
       "1560                    NaN  \n",
       "1561                    NaN  \n",
       "1562                    NaN  \n",
       "1563                    NaN  \n",
       "1564                    NaN  \n",
       "1565                    NaN  \n",
       "1566                    NaN  \n",
       "1567                    NaN  \n",
       "1568                    NaN  \n",
       "1569                    NaN  \n",
       "...                     ...  \n",
       "1586                    NaN  \n",
       "1587                    NaN  \n",
       "1588                    NaN  \n",
       "1589                    NaN  \n",
       "1590                    NaN  \n",
       "1591                    NaN  \n",
       "1592                    NaN  \n",
       "1593                    NaN  \n",
       "1594                    NaN  \n",
       "1595                    NaN  \n",
       "1596                    NaN  \n",
       "1597                    NaN  \n",
       "1598                    NaN  \n",
       "1599                    NaN  \n",
       "1600                    NaN  \n",
       "1601                College  \n",
       "1602                College  \n",
       "1603                College  \n",
       "1604            Transit Hub  \n",
       "1605            Transit Hub  \n",
       "1606            Transit Hub  \n",
       "1607  Major Employment Area  \n",
       "1608  Major Employment Area  \n",
       "1609  Major Employment Area  \n",
       "1610  Major Employment Area  \n",
       "1611  Major Employment Area  \n",
       "1612  Major Employment Area  \n",
       "1613  Major Employment Area  \n",
       "1614  Major Employment Area  \n",
       "1615  Major Employment Area  \n",
       "\n",
       "[76 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract data entries without a city\n",
    "dfNan = df[df.city.isna()]\n",
    "dfNan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From Pandas to Geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<shapely.geometry.point.Point object at 0x127f6efd0>, <shapely.geometry.point.Point object at 0x127f6e978>, <shapely.geometry.point.Point object at 0x127f6e940>, <shapely.geometry.point.Point object at 0x127f6e908>, <shapely.geometry.point.Point object at 0x127f6ea58>, <shapely.geometry.point.Point object at 0x127f6ec18>, <shapely.geometry.point.Point object at 0x127f6e828>, <shapely.geometry.point.Point object at 0x127f7ee10>, <shapely.geometry.point.Point object at 0x127f7e1d0>, <shapely.geometry.point.Point object at 0x127f7e208>, <shapely.geometry.point.Point object at 0x127f7e240>, <shapely.geometry.point.Point object at 0x127f7e278>, <shapely.geometry.point.Point object at 0x127f7e2b0>, <shapely.geometry.point.Point object at 0x127f7e2e8>, <shapely.geometry.point.Point object at 0x127f7e320>, <shapely.geometry.point.Point object at 0x127f7e358>, <shapely.geometry.point.Point object at 0x127f7e390>, <shapely.geometry.point.Point object at 0x127f7e3c8>, 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<shapely.geometry.point.Point object at 0x127f99e80>, <shapely.geometry.point.Point object at 0x127f99eb8>, <shapely.geometry.point.Point object at 0x127f99ef0>, <shapely.geometry.point.Point object at 0x127f99f28>, <shapely.geometry.point.Point object at 0x127f99f60>, <shapely.geometry.point.Point object at 0x127f99f98>, <shapely.geometry.point.Point object at 0x127f99fd0>, <shapely.geometry.point.Point object at 0x127f9d048>, <shapely.geometry.point.Point object at 0x127f9d080>, <shapely.geometry.point.Point object at 0x127f9d0b8>, <shapely.geometry.point.Point object at 0x127f9d0f0>, <shapely.geometry.point.Point object at 0x127f9d128>, <shapely.geometry.point.Point object at 0x127f9d160>, <shapely.geometry.point.Point object at 0x127f9d198>, <shapely.geometry.point.Point object at 0x127f9d1d0>, <shapely.geometry.point.Point object at 0x127f9d208>, <shapely.geometry.point.Point object at 0x127f9d240>, <shapely.geometry.point.Point object at 0x127f9d278>, <shapely.geometry.point.Point object at 0x127f9d2b0>, <shapely.geometry.point.Point object at 0x127f9d2e8>, <shapely.geometry.point.Point object at 0x127f9d320>, <shapely.geometry.point.Point object at 0x127f9d358>, <shapely.geometry.point.Point object at 0x127f9d390>, <shapely.geometry.point.Point object at 0x127f9d3c8>, <shapely.geometry.point.Point object at 0x127f9d400>, <shapely.geometry.point.Point object at 0x127f9d438>, <shapely.geometry.point.Point object at 0x127f9d470>, <shapely.geometry.point.Point object at 0x127f9d4a8>, <shapely.geometry.point.Point object at 0x127f9d4e0>, <shapely.geometry.point.Point object at 0x127f9d518>, <shapely.geometry.point.Point object at 0x127f9d550>, <shapely.geometry.point.Point object at 0x127f9d588>]\n"
     ]
    }
   ],
   "source": [
    "# Commonly, there at latitude, longitude columns for places, but we must tell geopandas \n",
    "# explicity what the geometries (Points) and coordinate reference is (EPSG:4326)\n",
    "# https://geopandas.readthedocs.io/en/v0.3.0/projections.html#coordinate-reference-systems\n",
    "\n",
    "# Once identified, we'll have a \"Geodataframe (gf)\":\n",
    "geometries = [shapely.geometry.Point(xy) for xy in zip(df.lng, df.lat)]\n",
    "print(geometries)\n",
    "crs = {'init': 'epsg:4326'}\n",
    "gf = gpd.GeoDataFrame(df, crs=crs, geometry=geometries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>place_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>place_class</th>\n",
       "      <th>type</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trader Joe's</td>\n",
       "      <td>1700 East Madison Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.615866</td>\n",
       "      <td>-122.309913</td>\n",
       "      <td>ChIJx0M1ztNqkFQRtgspEllQxk8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3099133 47.6158665)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hillcrest Market</td>\n",
       "      <td>110 Summit Avenue East</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.618850</td>\n",
       "      <td>-122.325005</td>\n",
       "      <td>ChIJt5emCTMVkFQR-BCgFDTvu9o</td>\n",
       "      <td>3.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3250047 47.6188496)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uwajimaya</td>\n",
       "      <td>600 5th Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.596843</td>\n",
       "      <td>-122.326929</td>\n",
       "      <td>ChIJq9nX27xqkFQRu05rxkrN7f4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.326929 47.596843)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kress IGA Supermarket</td>\n",
       "      <td>1427 3rd Avenue</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.609396</td>\n",
       "      <td>-122.337822</td>\n",
       "      <td>ChIJbdIhoLNqkFQRMTiHCN6W4nU</td>\n",
       "      <td>3.9</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3378216 47.60939570000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Double Dorjee</td>\n",
       "      <td>1501 Pike Street # 511</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.608822</td>\n",
       "      <td>-122.339570</td>\n",
       "      <td>ChIJkX3s-LJqkFQRQRexqCN0jQY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3395702 47.608822)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name                   address     city        lat  \\\n",
       "0           Trader Joe's  1700 East Madison Street  Seattle  47.615866   \n",
       "1       Hillcrest Market    110 Summit Avenue East  Seattle  47.618850   \n",
       "2              Uwajimaya      600 5th Avenue South  Seattle  47.596843   \n",
       "3  Kress IGA Supermarket           1427 3rd Avenue  Seattle  47.609396   \n",
       "4          Double Dorjee    1501 Pike Street # 511  Seattle  47.608822   \n",
       "\n",
       "          lng                     place_id  rating  place_class type  \\\n",
       "0 -122.309913  ChIJx0M1ztNqkFQRtgspEllQxk8     4.5  supermarket  NaN   \n",
       "1 -122.325005  ChIJt5emCTMVkFQR-BCgFDTvu9o     3.5  supermarket  NaN   \n",
       "2 -122.326929  ChIJq9nX27xqkFQRu05rxkrN7f4     4.5  supermarket  NaN   \n",
       "3 -122.337822  ChIJbdIhoLNqkFQRMTiHCN6W4nU     3.9  supermarket  NaN   \n",
       "4 -122.339570  ChIJkX3s-LJqkFQRQRexqCN0jQY     5.0  supermarket  NaN   \n",
       "\n",
       "                                 geometry  \n",
       "0         POINT (-122.3099133 47.6158665)  \n",
       "1         POINT (-122.3250047 47.6188496)  \n",
       "2           POINT (-122.326929 47.596843)  \n",
       "3  POINT (-122.3378216 47.60939570000001)  \n",
       "4          POINT (-122.3395702 47.608822)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "\n",
    "### A fantastic, quick way to visualize spatial data is to save a geojson file and upload to github:\n",
    "\n",
    "gf.to_file('places.geojson', driver='GeoJSON')\n",
    "\n",
    "https://github.com/uwescience/dssg2018-geopandasSQL-tutorial/blob/master/data/places.geojson\n",
    "\n",
    "\n",
    "There are also some Python plotting libraries that work well with Geopandas including [*cartopy*](https://github.com/SciTools/cartopy), and [*folium*](https://github.com/python-visualization/folium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127f9ddd8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bWlwRBMg6LreseYBvrPvD2NywfQjPQsm+TE5Fn9cSvhEYY36/83ZiZob9nkFG0GPRB9ez6x06Pf+Q44ovr+a3V97PQ2fdy+cmr6BWy1IZzhM1QoT1AA3hSr599k2HXE7b0/9bNlohHuo4jbxr4ogBKGwxGLA1Pv/iz4aObRns5fnuXchBXoEeij+1PUPaPryIwZdFn1C2eJ8VYUsmheUeuaaCz6uLPxwYY9LOc8QDo19gNRDgY2ev5531m4kYxTmCd0/YyuXJVkKRZjabt5IwI5yWaEQpRdpuZ092BaYWoTm6BFMrDg/aCj3kXYOufJyDXX49dNb0dQ1tt2X7AZfRtl6RdwweaX+WtzVffEz3q2IfQ/o/w/4hQZ8T4Ctti9hRSKCr2xBM/n7G27ly4pJjuo7P8cM3AmNMUAUoLhMeeEGVgnn1+3jP5M0E9QMegAHNoyqUZ5cTo93poyFUiVKKFd0/ZG3fHSg0lNJA4I2NtzIhfAZ9nIdi++gLl2FGonZUvD+AQogHCrSm+vFE0I7BiUeF3ogkUpD+JkiOL7YtYkc+UQyXFgCP7229i+bIBOZXTDvq6/gcP/zhwBizoPr9aAe5Bys8rl/4PILQ7xrcN1jH/+2bzA/6m3jRihOXdXxn85/5wLPf5ZsbbmNd3124YuFIHtvLYkuWh/d8DldsFtW9FUWICaEB1DDP/6SRZUZkH+fXRofKgnqW5ng/2ogIAUFTHhPiaX7yzC6u/M0v6M8f27BAi7wTVbec9XIdLYXYiHwJALbA3S33H9M1fI4fvhEYY5ZMuIIJejMaHjouGh5RZTEt0UEexW/SE9luR7DQyIjBc/kKluUrmBnqoODZ9Bb+hlPG90rwaM+uoj5Ux1l1X2NeZQ8h3SGoLG6YtJwvzHyAm5qXc3XNb1nX+Uk8sRDxuGziTqZWdBHQbXTlURHKMb+unTp9gHTBY2dfL19e9tdjvm+ldDozq9HV6K6HoNiXazum+lt7+vnxX1/g+w8/y+a9Xa98gs9h4w8HxhjLzZBWDrVagQqVJqJZ5AnyUP9UFiX6sEWNmKhz0Gh3Q5wT38u6bDWqTAZjAEQNuRAvrl7Igsqfsy61ibVd36TBbEdXHiZeMe157jF29H2H6sj1aFhcNGEb2gSYamZp0PL8NVPLqp3FrrnteTy0bQsiVx6zb39tSOHI6O+KqVzOSByd+jLA3ctf4hv3LsMTwfWEH//1Bd7z+gV88i0nRg7+VMPvCYwxm1L3kXX76PDCbHbreNFuZKNdy8rUFNZbiVFdZQARRZrievqOTA2ujLbNHg4NkQMZi0zN5KzK05kcXI+uRhoOT/LsTP2KH2z+MF4pgYmHYqcdYZ0d56aKFl5qORCMtX/58FiJx97HDdVbCA1rj6lc4rrFu6d++Kjq7B7I8PV7l1FwXGzXwxMhbzv89unVrG89skxKPuXxjcAY05pZjlsuIlpBxjHLTtQBrByoB2BfvgpdIujKLJ1moKsg59d9hoBWDO8VEf7zzr+w5EPfwnYHy9bX6+gYpjui1+GhyHgGKc/kssZihmNNKV7f1HxMk4P7mZK8hsmVU/nMxNUsiHQxNTjAFckWPj97KdWho0uL/sSGHaNSmAEUHIeHVm851ib74A8HxpyIUUu5XOSN0T567TgT9b4RC3uegOPp7MhVE1Q2TaFe5gfXMyBJ8moCDbG3Mit5JcnAgS/3l352P08vX83FCzdQyJmEo6NDjnvsWLmkQXhASgzOrNnHE3vnEjZNvnrxsQckQTGxyesm/g+tmRe5Lv4ghpZkesXbqQg0HHWdReNUZskVNSaGy8c3AmPO/Mrr2D34xIiMwAqdiKHIehodVpJqcxBTFZcKs24QyQW4qHITc6P7mBBK8UJhGrboCEKGlcyrfM9QXXnLZvXq5/jKv9yJaTropsugV4oHUArQcURYnWpkVrKLg9MZaUClZoM5gy+cfyFXz55LPDh28QRKKZpjZ9McO/uVDz4MLpw3jVvuGT1xGTB0rjhr9phcY7zjDwfGmNrQXJbUfQpTRTC1CLoKUhWcxtzEuWhoWJ5Je6GS1nw1LflqUnaUD0xYx7vqVzE10s26QiMFMfHQEBR78y38vvVfARDx2LZ3Fde9+Qki4QLBgIOhhLhWwFQudkGnKnw1Tw7MYctgHXnXRA3rkWh4JDWbOt3lI+d8nfeesWBMDcDxoCoW4SvvfANBQydkGgQMnaCh86FLFzNn0uFlUvJ5efyewHFgVvIKpsUvobewjaAeJxloZl9uD2sHXsLy9mceUuh4LAj1UmnYpBxosytHCZQI0FNoYVP/o2zo+Tr5fJaGKX3kxSBSyjOoKyGqbMJBh/m1/8Ev936Yhsggf9k7lwvrtlEdKiYymW0O0qBZaNV/QKmT509/1cK5nDuziUdf2obtelw0bxrNNUe/2uAzkpPnf8JJhqEFqQvPG9qeEJ7Ex2bezN2tP6c1u5OAFmBRsI3rKlqKnoFA1gtQbvxb8Aqs6foc7YUky/rOpOAVJxhnhDt5V90KonpxTkBQ6EqnLjQJS3bheBq/23oWuFATTjMxOUDABK31Dr6/5FNEzJNHL7A2EeM955+yWelPKH4+gTFExKXf2oKGQSIwA9zt4LaDMRel1+CJy9aBh9jQdy9Zp5/50srp8V4Asp7HI9k4O+0avINGaSEKBHD4Q+fZOHIgNFfHZWKwn49NWobrKWrD5zGl/k7W9K/gF7u+R9ayCOkOSgk2BiIKpcD1FFFD5/RYPyE9wmkVb+O0ireh+WG/JwV+PoHXKF25F3h+3+dwpYDgEcTh3GCBpGGCFJDIu1g26NKSeXrII/CRVD3TIykiuktE07g8nOWnjkfeU0MTeiIgSrFucBLuQY44Ljr7rCQdVoKp4QRNtcWETmdWLOKi2kt5pvsPKIS0GwLU0ByhrgkFz6Itb1MXaGNF9+1057dwUcO/vmrPy+e1gz8xOAbknC6eaf8EBa8PR7K4kicrNn/La7heGrDoHbyH3ZknRrgExxN9/Kx9BgO2SdbVUWgks0H68mFsT6Pg6WTcAD1OjJQTHhUSDEVHI0JfYvbEp0cImKTsNjQlowzH0HloDDjFyERH8uwc/Ctpu73ssT6nNn5PYAy4Y8N/8fjAbFJOhKZwL4srdhE3CghCu6vTaLh0OB4cpOqjK0FP9PPd3sk8vPMM+uwQOc/ktgV388eBMwGoDaTRFCSNHB2FBC4ju+wOOgtrLh/l8ttTKL7QqowvfxHBUAcCizQMuvObiZtHv6bvc3JyIrUIpyqlnlNKbVVK3anUGGbPHGNSucfYtO8q1rQtYGvnDWQKBzT/vvf87fyoXbE1W0enleDFVDM/bX09/XYYDyhI8eUMK++Qrrm1gTS2IeS9ok2eHOqn0exHpKhQJAIh00VXMiJyEBFmxyZTF6rAFQ/HO7BvcnQOGlpJUUA42HlJIdQE0geqwiNmlk8Q4nNqcyTDgf1ahEBRi1BEFojIAmA5cM/BJ4jI48OOuQTIckCL8BvAf4vITKAP+NBR3sNxpWfw9+zs/gey1ku4Xi/p/JNs6XwnmcJqPPH4decOPKWx/1F6aFiewVM9M1BAjVZ8MZsMB8szyroNGzq8YeJGQoaNpjx25St4V9VKzo60kbJDzDQH+Xj1Fn4z41FuaXqOKcEUIjAzPoUvzfsQ/7j8Ds784y2ced9/cNPffklbpo+ldW/D1IJoShHRrZK/gGAoHYXQEOgnpheXKzV0EoFGaoK+88145LBWB0pahD+npEUoIlcN2xcHWoDJLydFVlISulBE3lvSGugCJoiIo5R6HfAVEXnjy7Xj1V4dEPFYu2chm9tM7nj8Qlo664iHs7z5vOd58+IYVRU/4OplX6fcpHpYK/C1GY+yOFQAgqCi/P3u17EouaKsJmHOMbjt+ctIxPKcUbObT099gpByqNB1KksvMxTdjG3R2BP8MpMr3smbH/0e7bkUjhSNjYaiMhjh4cs/Qcbp4uF9v2ZXZj0RPc685FKao3MJqQzPdX2brNuDiNAQOYuLG75ISPfX3k8GTtTqwFhrEVYD/SJDg+Q24PB1tI4TIoJkfw6Z28HrRfRpDPRq3HrndVhOMaCnN53gzscvpH9wBY1nP3PIuryCzqKKq8BthcA5qOgNTO99iH57PRVmboQhcAX6nQjnzd7Jrs5qntoxh0nxFLPC3Vxf2TVCukxTYOIRzt3CjzpepNeqHDIAAB5CzrF4sG09b59yFu+d8tmy7Zscu5Os24OhggR1X3FoPHOitAjLRX6U7ZKMpRahKw4duS1oyqA+NKOYumt4Awa/C5kfA8VMO8rdxsIqjTn1XXTZQS67YCUT67vJZEM8u2ou56tPUcvb6HLdod6AQqgwslRpwtJlIdJMR5cU7528ivfNvZzPrl7O6ypfQhPB0ATb03BEY3ehBkMXZk7o4eI6kzfVXcdzfXfhSTccNLmnKajULLanWym4sVH3mXVtdg52jyr3xMHxCphaBKUUUaPmmJ6nz6nBidIi7AYqlFJGqTfQCOwtd+JYaRHuGHyB+/d8DUEQEYJ6lLc2foX68MzSdQqQPWAA9mPqHp+5ahkbDME0i0E/wcAgl12wirwb4Nzqrazpm0V1bDvTY930SxRXivrDjTXt7MkmWds3kTva/0qf/QSfmHkTn1/9K2Yn9lAVyJH1AvQ5sWKsgBRlwd0onN/0PubVLkLvexuMUCYuUhBoCu1DV7NGJfKIGAHmJA9M8nni8OS+b7E+9RiueESMCBfX/xPTE5ce7eP0OYV4xYlBEblZRBpFZArFLv1fj1KLcEiuTIoTEY8D15WK3g/88QjbftgM2J3c1/ZVCl4Gy8tiS45Bp5u7Wj6PXfLlxy2fskpTMBjNYxgjX8SA6eIGFGdUbOQjczfxxrqd5FUQlELXil95XRMmRlJMiqRw0Hm0x+aRvf/G/Kq9mIbCU0V3YVt0Bp0AaSdI2gmyO9tHwbWIB6bQRT3OQabPFWhx4bT4XmqCecxhkxKG0qgIhLl84tyhsgfa/o2V/Y8SUFlqjBRB6eDhvV+lZfC5MXm+Pic3J1KL8HPAJ5VS2yjOEfz4GNtySNb3P4qIN6rcE5cd+18EvRYEdqfjtGcjI2bx97lm2ck8yzMQBVlnDzlxsUoaAMMxNKE5VnQNFhTbc0W3YA+NtBtGlEZcy+GJxn6Nwryr+Mam21nTfSsv5LLscXRcKb78tsBWB9qcALvsWi5vWsu8yjRhXSeiB7iicT53XfS/COjFTl7eTbFlcCWTjD4q9QwhzSGqW9QaKZ7u+NIYPF2fk50jchYSkWXAsmHbF5U5ZgXw4WHbuygz6SciOygOJY47Wbcfl9EiGIJHzi3OZ+5L7aY/FWZyLIWpvBEvfacVozaYHnW+pgQbHU3yeHJoN4f9TjkeClMfbowUrihmRffRGOpDVx7thSRt+SrWD2zl9NByBJu1lslOxyWowAJyXoDNhQY8FJrmsrBuNefWB7mg7gMsrBo5MmvJrCam5TCUO2qCMSCdFNx+gv6qwLhmXLgNT4mejalCZfYITZEz8DyPfPf7mB7vI6S7Q5Jg+3k+NRXbOyiNtqexKVuPKzqgCGvWiNj9/bieYm82ia5cZkU6MLWRPZK6wABR3aI+mKYmkGFutJ3zKrYTUCClpKMOGh6KAsUkpe1ORSnkeFjCUinwdNcvcLyRWYYSxgQiyhoyAI6rUbCN0t0revNrD+MJ+pzKjAu34amxxdSHZ7Ivt2Uo44+pQsxJXER1sJkt+5YxMZTG0MrPOwbQebR3LudXbCOiW3iiWD84kXWDE6kOuEzUB0DlmRrsYnuhruTjX5QezzkmbZkK5kb3sqhmNx1OJXEjj0IoeCbVRnrEF9rQhIhY1AU66HQasfNpbMegOdGFiGKXVcP2fD2G8qgJDBIzDmQwEhEG7E6qgo1DZfXhWXgSIJuHe9edS3umgkI6SMQtcM15z7O4OgxRfMYx48IIaEqnLvQu7mm7hz47T1SHtzUu5PKG9wKQt9pxy65aFqP4bqrdyFf2LOZX7edhKhdHNHTl8caq9dgqSjR0GYP5B4jrOaYF9mGLSUEMKvQsuufwhor1hHSbtBvAUoGhl95QBTISIiH5oeFH1g3QaSfQHJufPnY2dYHHVEB3AAAgAElEQVQ0l8xZS6+Ks8Kdgi3GUKjxYC5IQzBFdSADgIdL1Kgc0X6lFOu3vJFNkX1MmtzDnEALfXaEnb31/GLZxST1AT48NgLFPicp42I48ETni3x789302cVFjIwLd7W+xCMdLwDQXH0BAX30xGHB0dg9GCNq9vHOuueZFOhHQ6g101xTs5ppkS48yZN2hbNqbyUnCaqNLDNDnbw5tpMFwQFqTY2I0jHEI64XRnz1lSp2yQulFOOeqKIBwOWp5WdyZu1u/uGihzh90m7SgdAIAwDFSMD2QhJPFDoGcxIXEdRHf9b3hjNcNnED05LdVIazTE30cMWU1Uyc3cH9W9aMOt5nfDEuegI/3nE/BW/kxGDBs/nJjvs4tyJAMnImL7RezGmxZUSM4ji84Gr0WmF6tAwPp6ezMLKT9zUsHzrfQSPtBqnWB0kaHmHZwtzYNLqyzzJVL/DQ4AwcEaqNLFkvTIfTyJmRXWVad0CwI+eZgEcgpVNIuFw6fw3PdU9FU0KPER+VbMRUDnEth+1pzE5ewCW119PS93P2FjLUR5cyJTYPQThjwkpCmkVE2Qy4IVw0LEwunb2OPz9ZXgLdZ/wwLoxAR763bHmPleGxtg8Q1BQzKpewvDNBg+kR0V1acyGC0X10SYScZ5LxgpjKG4rRd9HQlDDZ7CZu382mvqLAR1AXdrhB5oZbmagPFr/2ApYYPDE4fVTfSwFBLYihdHRl0jUYZXn3DOZO3MO/rXwbWslbcNbEDqqiJU9GhHNj25ke7sArDU16BvP8dOsjbMrXoSsP4UmqAzW8v/ntLIi2HrieWcxl2O0mULrHrIbRHoc+44tT3ghk7RaSRo4+Z/TqQFTPg1IURNg4+DcmxfuwtOIyXLyUhFe3Xfav3zvoI5ybw8omphXISGBYgtBiBh8PocsLIShM5ZJUFosirTyZm4lSBxyPdBXkXVO+hyuD9OR7uH3TX6gwM7zYXRz/779ea28VyfBedE04K7qLaaFODCVQqqs2tIeO7CRctKFEIj2FLjZ2f3yUPmCj2UfGC5ETk3+54E1j8JR9TmZO+TmBDd2fYWnFJgw10uPPUC7nJHaWtoqLe/kya/0Veo6YViDlhkeFAUdVcY7BYb+jz3AUBXQsdDJi0u5Fiep5GkNN6MrEUEGiRhXXNn6ZqmATtaG59NuT0DWdJrMPV0bW15+NsLu7EiUec8Lto5YalYIZoc4RZbVmGk9GuxwrhCo9jY7HnOop5R6bzzjilO4JOF6adGE9p8VsQFjWN5uUGyah57mgYjPNkf6hYwVFXnTCokas9zuicXpoN2vzzUQ0Cx0PXQmeUDbd10gO9A4E6PVCXNf8FSxC2F6OpDlhRBBT3t4OgKm5eDK67n39SU5PtIzICDSckGazNNrC2nwtfW4YXR2cwLzUGgUJLU8oMhPBQ5WyFdluAU9sgoY/RBhPnNJGYHiuhNNi7ZwWO5BDzxMY8A6k3FZAxgvQblXgodFg9hPUHSxPo9bMcKGxmb12kn35JHtzSbal66g2M9zY9AIKOQyDAJboOBIhaiaBA0t5njg81fYOugs7MdQVoAum5mId5KAkKKKRQ6gWAzHN5fTobjKZID/ZMx2vyuXs6M5Rx4kUey85axM/2XoRIVXNXKOb00Nd6AhbrCQq8e/MrLzyFe/J5+TnlB4OmHqCeGAuB3fVixN1B4JuFIKOy12di8lIkIKYPJg6k6xrgqbotmMYyuOPbQv4wbal3NO2kJdSTSzrns3GdN0oA3CoPC2up/Gt5S+MKt/Y8zV6rJ1oSnHjlOVszDSwtH4zQc0u6Ql7mJrDuRN3sCi5C68YbjjMyBVTiDXqNl/ecgm3bj+fmlCaM2v20GEni9GJcqBtdmliM6j6Ec/j/OAu5oe6MJSgKZgWSDEh+ym6c1uP8sn7nEyc0kYA4LTa/8LUKtBU8auvqwixwEy67QkoBA2PATvEXZ3nkJMwq9KTS1qBDrutGgLKJWzYbM/V0Jqt4rqG1Xxs8hMsqdyBQsh4ZjHj7zCUAssdmW7IcnWeaZ3Jb9atx3JHjtN3p/889O9JkRSfnPMo06o7uWnWU1wxaSNXNW7mE/Of5Mapz1CtXJb3zqAtl8ARHU8AUUzR87RlK3myZwpRs8CbmtejKdiZr6XfDVEQvZi92AuQlSD7DWNUudQZecxhk4eaAkMJmzr+Ywz/Ej6vVU7p4QBAxJzKksbH6cz+hbzTRjwwj4rQ+Xxj+T+XzWLSY8fwUBjKJaDcIR99JfDxGY+jGy4uGm9O9nJt4yqiyqJaz2GJzqCYQ72CZ1pmsaR5C46nY2guazuauGPdEpQSOjMvsbHzMyi9DcsxyWDS4yYQFEk9S0hzmJPoAoQ3T3srNeHX87e9H2TfYA17CmEaYr2k3DCtA9WIgqheIBfqYld/A57AjTOXj5iNsMUATysbCRk7hKt0UPMIS9tY/Al8XuOc8kYAQNfCNMSuHdoWEUJ6kIDqw9RcBuxwKQwYgspBIeS9APVmClD0O2Geyczg4spNuKWVABOX2cHe0kQhuGJTSYE2J4qnNFa2T+UPmxYzIdZPTy5Of77oyff6ibvY1PN9dEPwPFjeOR0vpoZSg++hkgazn4ZACoDne36LUnczSR9krxPj9VU7iekFeu0If0mdRmUgx+7+enIVQcKax9IJWwgaDu2DCWqjGXTNI6DcQ85ZdNphIDWqPO9p5LVpY/dH8HnNcsoPB8rRb+9lUcVu5sbbmR7pZGHFbqaEu9FxmB1tZ0e+hqnBriGNvydTc5gV3jcicq/JGMAsGQAAXYGOUKlZeGLy0YWVuF6A7X11QwZA11xuOOtR9i/+780nsSM6KFUa1Rd/7XYFWc+k4Bl4CK5Y7BysYXFFC3GjgFJQHcjy9okvYhoOjZVddHXHaetIcn3tNj4Q7+DaRDeWYxLX87QWKrBFG5ob2P8bdIP0SoBWK4rlHTASrkBBdObV+/kGxgPjzgiICPe0fglPMuiqmAFIU8KEUIrTYp3MCYeo0jNUGYPoooEXwPJ0Ks3s8FpIaoVR3evi0ptFxJjP5Oi9/PsFdzK1potwoEBNYoCPnPskNgzFD2zsaxo1n1CsXdFhJchIeKhsbqyDoD5yLiGoucyLtpMIWkyt7+G5HTPpL1issRxmxnq5sWIvG7ob6cwn2ZyuJ+uaRWMATNI9Ul4cTVPcn6nmmWwNGU+n4GmszVWRSfyEilDT2Dx0n9c042I4MJweazdpuzjeHo6uhNPCfUwwcuxzE6Q96MwnODO4l3+esIy8Z7DdqRjqVguHzpYa8p7D1UySMY0l87fgioYlBlPCKXJegJBmoynK+gLsZ7jSkEII66OTogBUmRn2FKoIGg6VdYN84al3sWjSdm6Ys4ILK3JcHO/hc5vPL94jLpfUbuH6qSuYZBhDT8DTFKvsGKtSRf+AWfELODvuhxaOF8ZdT8D28odU3+3zdFYXEqUkHhqLQm1MNvsJKI+4ZhHCLbn9KPq8UHFmfhiuKHJehKQmiORJuxEQoduK0W3FWJ2tR+eAk9HUZEdZmTANGRIGgaKxyLjlMxd1WXE0Vcxn2FDZT1aZPNE1i488/R4+vXoptcaBxKm65jEp0sduK0FCZQkpl4ONoaFMzqq8+pUfpM8pw7gzAnWh6WXLdTzyopVcgCGqCkwyBor++RS7+jMDvVRoOUBotRPkpNi9dkRhiUbKDdGgmyQ0jblGkFrNJa7lcERDULTZSc4IDrCzUMugG6Qx0ouV1nFdVfRALP0mmb1U6oNDkmNKKe7vnE/BG2m8Cp7O1lwdALanSFlBvJCH6MWv+33tM/joissIYhNQDhdVb+W02nYqA3HWqfdzaaSduHIx8QjgoaOzpOZGmqJnHKen7/Na5LCHA0opHVgB7BGRq5RST3FAjKQOeF5Eri1zXjNFXYImip+dK0Vkl1LqZ8CFHJia/oCIrD7qOzlMdGXypoZP8sDeW3HFQSi+BAnl0CPm0HFJPV9yqD3wpTSUMNUcYLIMsDLXyJ39C2gw0lToueKSXa6GlB3lvxqfYa2t0Y2iw6oc0bX/Ve9cbqxay24nSatVwTn1u2jNJNnVV4+YgqXrTNRd6kN9ZARSbtGAaCb8cs+5XFP3EpWBDF1WnC25epLhYo9BUKxPTRwxRhEUOddgsi586iyTumgTEyLvoDH2RgwtjOd9kar8dlJOKy5hJkXnEdYTx/tP4PMa40jmBPZrESagqEW4f4dS6vccOmX4L4BbROQRpVQMhitq8hkR+d2RNfnYmZW4gOrgZNb0/ZlBp4cpegdzWMYvB2rp84rd7owXKJszEIoTe4sj7TQGJ7C3kCHrmjzSM4e0G+TmiX+jQ9K0ufUklE1ORj7inVYdf0zN4k3JjUzQislFrcEAP941h5xrEjcdbrl6JlXxK2gd+B3b+28lqfLoEcU2vZovb7+KsGZzxcQNxAN5LE+j4Jr8pf00BvOjIyUFDdut5erpHx19H5pGVWQmVcwcg6fqc7JyWEagpEX4ZkpahAfti1MUG/1gmfNOAwwReQRARAaPtcFjQc7pQ+GwtO5DGFoQ8bJI34e4KLKNPw1W4KCR8sL0uSECyiWoXELawdF4Lk3BXhY3/ol/X/vfpN0cYa3AlPCeoQjAoHJKCUNGTiOuzjWxOV/HpZEN3L7xQnama/FEURtJ80/zHiOf6aTfW4lpb2Oa0Y+uhJrYbhZFWwnjcMfehfxm9zkkzBw6Hr2FCFY6gIhWdrayJhQZXejjU+J4axHOAvqVUvcAU4FHgc+LDMW33qKU+hLwWKm8UKaOMcP2cjzR/h+0Zp9Fw0AQFlZ/iNOr3gVVv2Fa/CXeHn2SZwY2kLM20eIkh5J6hJTDDLMPc1gEn2lMwdDr2ZGLIBS4LLkFoCQFDj1OFEN5OOKVnIwOUGFmqY5muHnR/byUaiSsW+gBhY2G7eXIZH8D6EO5ADQFAeVxTdU6/rJvDv1OlAE7hI5QGxkknY6Tc0OILgxfuwwbJh89w5/p9zk0rzgxOFyL8BCHjFAXOggDuAD4NLAYmAZ8oLTvZmBOqbyKohhJuet/RCm1Qim1oqurvErQ4fLkvq/Tmn0WVyxsyeJIjpU9P2LX4JMopVCBM2mq+jhLa99NpZEfygEoKHJisMM+kJ9fqTDJ+CcAaAhXAxDfL/WtQYwCdinBSES3iWg2Qc0hpDnE9QJxPQ8idFhxJsd6mREt3puGMOgFKfYeytvEC+u2EdItkl6OpF2gUsty+WlruHbBCuqiA+jKJapbBDWXfzzzXK6c6kuO+xya461F2Aa8WBIaQSl1L3Ae8GMR2R/XW1BK/ZSioRjFWGkRFtw0LZmncEtyiFk3wN5CkoJn0uv8gn+cdS5BvZhOaGv/r3BHKaspsmJiSZiQHqWq4hZCwXNJ22lmxRO0Zgs8k5nKefFdhHSXGWY/Wyw1tKRoaN7Qw/YEWjKVXJKwqQ8UR0j7dQ08FEHlAAoZ+VEHioInUyN9zEimeOalaQTDNvt6qliVmcmkil4qDIubFz1MjeYwr3YB1fVLjvaR+YwTjrcW4QtApVKqtrR9CbABhpSKUUop4Fpg3VHfxWFQcAeGkmf02FFeTDfRZcfJeEE2Dlr869ovknGKXoGW21e2Dl1Fqa7+BU0Na4hFrqYtu4fPvHQzz/Y+QcgoYCuD2zovptcO0eok6PciXBJdhyuqFO1XFENNWSHeUr2GQS9IwdOxPY0ON47CI6HlCWseXVZwRLjzfhQwO9zGP0x6mu9ftZgGQ6OQMUGBYTi8+cxV9AQdNpvwx9RqVnX/9Hg9Up9ThGP1GHw38PXhBUqpRcBHReTDIuIqpT4NPFZ62VcCPywd+uuScVDAamD09PUYEjPr0ZSBeLAlW09ELwYK7U8EmnP38Mc993L95OuZELmAdKoFOUi6rODYfP6bf2NCYifve8ul/D7//yi4g3gl4+KhkROTn7W8jmldA4QmZ9huVrMwvJerknlq9G7yorHGrqbDMbE8ExBEFGHlEFPCGaE+Um4Vf7/mLVxRu4EbmlfgesVZBk0JBYpxCgqLObG1PPzef2NZ+22s6n+AkCqMkFDzgFW9P6Epeh614bn4+JTj1dAifAQY5X0iIpccybWPFU0ZnFv7cf6y91sElDtkAKDU5RZ4vucxrp98PTMrb6Rl8AEsrx9PikFEjq3z+O8WsOU5jYpFj5PNf4V3JHJ4cY3nM808lp5dSgmu6NMiPPLATEzDZcZZLdxwzVoCWjEl2dP5IBnJMDz1mFKgo6gPn8nkui/zuZWfIuME+c2exTzcNZezk63kPZM1qYl8dPqTLKrajWBhObsA2JZZgyCjNBT383z393lz0/855LMRccBaCdgQWIhS4UMe63PqMa5iB2Ynr6Sn56tsycqol0UpcMnjiUdQr+SyprvYnrqDvZkn2bx3H0/9dgF7t9Yzf+5uPnzjIwSDpTRfyuWc6G50JTw0cBoRzeKtdWuY+sVeRBTpTIjVrVVMbxwk73kUZKSG4AE8OnNr0PR6unMRrJJ3YLcV4+Gu4ldc4dGSrWJR1W40FSEWOo+eQguDTg86csh4hkGn45DPRKzVSN/fwVCvx0US30AL+1mIxwvjyggAhFVdyV9/9OuihpUF9CRzq/4OS83le3f/hZ6tSQCuuWr5AQOw/1jNY1G0hZQYLIm0ENMOCIBWBwaxYjnuS0/D0BziepZDI4i4TIzkMZSLc5CjkaaEkGmjMDG0aoKBpfxs58dxpIAa0fqRNIQXlL+a5JC+m+Bg943UZxHzNJTR/DJt9TlVGHexA5Hkp5gf6OfgwBmA05ML0dTIRxLQQhhBb+j4utrRCTiguLRXZw4SLUUIDkfXPKYEusmKecjIQU8gEZiNoYW5bEKsJI46vI2CqbtIMEBN/APMmXA/L/U/huXlSnu1oZwBI9qlTM6qvrH8w8j/texzAAfJ/aH8OT6nHOPOCMyuWsrMqpuIKwcDr5RnUFEVqOVdzTeNOn5GfAGN5/aimUVD0GXFRkUPQvFV0hWUS/Kt60KVniXjBUdJiUFxYlIBGwcbAIgGGrmgaRu1kcGSl4JQF02zpGkXjppAU+UXMfQK2rLrkWFe2FkJUhBjyBDUBOdwTdMPiJuHkBqTNJTRJQAHvP4y5T6nIuNuOABwycTruKD+Gp7peYa0001zZDLzkmehlwkxNrUA/3zpJ/nyrh+Qag2whgam0I027OWzRWObVcWAFxryFhyOJ1AQjRo9jZKiGfCGhRDvK8SZYA5ybuRuNrRvJqymEDIdFk9qHXqhlSrmFZg1LM5fUxqeQM4NYImBAgLKIaxbnFPzTi6s+9DLP4jA6yjbE1ARVOjilz/X55RhXBoBAFM3ubDuwsM6tjk6mx9+7Bv8YPOX2GtH+PPAPM6J7KZWz5ARk1XZRlZkmlkQbaPdidFgDA65+3oCLoqUa0JXNU92NlEXynDd9A1owUE2OAHihsW6bAMJLYfGGhxZy7zQJNbnm3CHDFMxM3Km8Afu3tXJotr3UheazkupjYAQ0QpoSrA9jbQTJKhGqxMfjDImI5F3Q+4ukP15B8JgLoTA+Uf+UF8jOG4vXYO/Jlt4kVBgNrWxGwkYDSe6Wa9Zxq0ROFIMzaQqUs3eFHS7MR5Izxva5wl46GzPNXBNrJ2oliTjmdzf10SLpXNJ1XpyboD7WqcQ1Dz25Cp4+Kk38NlzHmJ2IoNhWiQUPJmdwfzAIJV6nufS06g0s8T1HHkvgFBUQ9qZi1PwnuSB3U+hlEJX4aFciEqBoTwEBwizK7OBF3oeJu/mOL1iCadXvB5djfyTq/gXIHg+kr0bpIAKXw2hK0YoI51MFJwWNu17C56XQ8gzkH+SrvRPmF7zP8TDl57o5r0mUXIopYzXIIsWLZIVK1acsOtvGljBnS3fwvJG+vS7HhS8AF+pf4mI5gwlH817GsvT9Xx7zxlMD3Wyw65hoBCmwsjxvgnPsSjWSQGPoHLJi0HOi/FQeipzAlnOCvfw2dbzqDALhIyiG3GRYl6i6cF2etwYhiqmHhu+5CkCKSuJoevYYgGCqYJMikznpmn/XnbYc6qwrfP9DOQfH1UeQNEcPo1Y5Q/RjMYT0LKxQym1UkQWjVV9J6e5P0HMji9kfvL1xVQjUurqe5B2w1wU7SCo3CEDABDSPJbEO6gMFlidbmJTRwN7+5Ns6GrgSxvewhP9zYQ0F6UgrDkk9BSLw3tYma+iwShwWeUO9uaTjFzOVORckyf757A+3ciagSZeTDWTcw8kRBFANBtbCuwf89tSYG9uBxtSz74aj+qEULA6yxoAAAuPVTv7+NwvbuUTP/kjf165EdstNyk6/vCHA0eAUoq3N32M6uBM7m37NZa4FFyDsO4wPZgmUEbIwxGNqYE06/J1pczCxRfacnVu3XIpF567fegrbiihMdCLnplM1tOZERzE8jRcUUNzDI6n0WNFR+gI5DyTtQOTqDEHaMtV4YhG0szRGEkRGJYHwfLyrE89y+kVrz9+D2kYPQMZfvzg8zyzbhcVsTDve8NCLj372BOYFNx+unLPo6kA9eHz0LViMpX1+248pC7kn1cs4r7nXofl6Ag7eHZrK/c8t47b/+7tGPrhfwsH7Ty/a3mBv3VtpT6U4Popr2NexaRjvqcTiW8EjoKltW9gbWoba1MvoUry5PucMDMlPULOC4pZjNvtyChZMlBYnsHKgQZmxLsI4hFSHiHloCkXXQn9rkkxmlCGOgMZ1ywzn6+wRWdntmbIwajbipGyI5ye3ItRkjFXaET0V0dxuG8wx7u/+itSmTyO69HS2c+XfvoQ2/Z283dXve6o693x/9k773A7qnL/f9aUPbuX02tOeq8EkhAEQkcQEEQUEfRybYDX+rOg99quvXAv6lVAEQsqgiCgKFKlJYQkkEYK6af3s/fZfdr6/bFPPych5YRQ8n2ePDl79syaNbNn3vWud73v95u4h41dP0Lpe3SlhBkl36DSvxhdbkMVLrkRUvGJtI/7n1+O5Qw+7lnT4uWGNh7fvJPzFkw/qHP3Wlne+8zP6MqnyLs2CoLHW7bwX/Mu5sKasROy3gg4Ph04DChC4Yap13H9lOuZ6pMowuWZdPmoRCDTFezOhdiRiZK39FHtCCTdlh8XQRaFpFTJSY1QnzDoY/EJBLU85Vo3CdOgM+8nY3vG1ioYlY4ssKWgIz+4SqAJjROLzx2v23BA/PHxF0lm8tjO4FJq1rT59cNrSGb2V3R6YGxPPMvKtpt4MVXGqmQZK5PlPBav5Tvbf8cnXvoMv+xYhmnrFMykHMix2NVUgz6KGQoypsVjGw9edPXO3Svp7DMAAC6SnGvx7Zf/hunsXy369Y7jRuAwoQiFhbEFfH72ryg3YsQdDz/rmk6L5cWWAssVrE6V85+NJ5Iz1TH5CgUwLdQ+8MlGsD1fzDsjDdzcMZ36XEG+fG3vZNrzQTrzBTahrK2NygwEcORIKXOFXsuLR3jRhM7Z0RICiffQ23YaudRthcKho4TVW+sx7dEvnq6pvNLYeejtda3hvn3fYXO2CltqKKLgZQW1PMV6GoC46+dnXacRFA4RYVOsSCoVSY3PHLOoQhGCiH80L+P+8GTbVkx37Hu2M7X/+ozXO45PB44QiqLxqRnf556GX7M+/gLf65iHB4eko1Gj9xARWdI+nUTGj+2IgfmqR7G5pGo9EX1wVHSloD4f5Y7WWUih4lElWbuQaiz77HV/MrHlKHi0wiir4JJ31THmwhJNuMz07GCJzyCmrkG6FhLIJX+IY64jUHTrUbkvFUVhNu9tHWWsLNulJPLqOQxDYbs2v917OwY+DG14paQiwFDsvloLlbzUWJ2q5YRgE0gXF42Z1c2oijuqXY+m8q6l8w66H1HP2FyNtusQ1t+4lZfHPYFxQEAL8sFJH+emhb/m63N/StwJkpMGO80KOpwQvTkv1UaC6nAPQU+OumAnn5z2KFfUDmdss6TK3lwZiqJQoie5sOilYQZgEAW/olTrpUrv4bzIRhy3rx562F5wcngntZ4u1iZcGMqPILNYuSdwrIN3hw8F7z/nBAx9+BijqQozakupK48dUlsN6Y0sDmzCFeqouox+eMTgCL0xW0dTrpyd2WqEHcarCv780V9w30dv5drlq4j4VQxN5XMXn86smrKD7sdVk07Gpw5ZhZFg5jSyaYX/2/J32nLdh3RdrxccNwLjCEUoRPQIujLoYno1m7yj09JWQm00wcLqZs6q3srCaNOwY10JWdfDvnwRM4xm3lnyEmF9/3Nny9FYEd7DNF8HTdkoLR1h9AFFIYkqHGb7m3hbeBde1SbpwiPpGuyh8QSh4FgbxvkuFDBvUiVfufocwn4Dv6Hj0VQWTavmf2645JDbSqR+wpZsFbarjlm3AWAPYWFSkJwcyHByIIWhpdGVPH7NpjSY5uql6/j9vz/Dk1/7KFcsPzSRlRXls7hm8tvwKBqqq5NoD5BOGCQSHv64sZGz7/8Z+96A04Lj04FxhiIU3lVzGX+q/xOmtAnqeTyqQ84jSWcMgsEcZ0a3sSdTRK0/gVcURueE6+Nls5oF4QbODW2n1QmgCUmVkaA5HxnmDbguTDc6aEhX8veeiUgkkWiWEjODT8tQ4s9wTskWwnohqcl0VJKOl4StsiZXxsm+/gdVINT9FBeNA85fMpOzFk+joS1OKGBQGjm8lYl4fh0pZwHxhJ9QSW4Y96KUhWXYAhWbRBcOL7VXMeOlJbyndgOfmrlyWO6GpphEPBvwa7uBQ2dbum76mby3bglL//STYcFYKQXJjMYXnr+Xu86+/rCu81jhuCdwFHB2+ZlcO+nfiOlRFCFZUbaXJdVNbNtTRT6j4REu0wOdrE1PZH2mhid753J351I2JieQsf3sy0cGqg3PKNqGT7XQRYEOTZEuJXqGcj3PP3rqMKWKJTVM6WGXU0yZk+WS8vUDBsDte0lyugcHhe1mP2OyiqKUonmWHdJ1eZ4AACAASURBVNV7oasqk6uKD9sAABgC/Eoeqbl0pILDZNZzrkaXGUBBYggLr2Kyu70EkCwubsKvjRHIEwqOteWw+9OQimPaw5chAaRUWN86dqn56xnHPYGjhJNLlnFyyTIs16I18Ucq8t+nZZrKl1aeBJMLI9kUrZVn0nNot4O4UtCdCrAjU8oGTxXvrF1LqZEhpOV5f+Xz7MmWkLS9LPX1UCUyfLr+zFFEpK6isDI7hfh9Ma591+N4hUPG0XklU4Gq9SUbobAuW4SjeMlJl2DTNcwt+jhl/qNrDI4E1aGLmZp5lh47QDwdoLmtmFgkiSYcPLpNwJ8HoaDg0pkIYTl+pkUFU4tsXJRhFZ/9UNTDl13Xlf2/NuINOK4eSy3CScBdFDQHXgSulrKP0O9NBF3RqY1dg5stoib5XWpL02RcHb9ioSgq7XYQ21V4aXct2byB4woUxeVHrRfwmRMepsxfqEic6m/HL1wCbpCVyfJRC466sAmoJqYquOyC1QRx0ISLV89zWmQPu8xiNpmVBEWWuFQQrgmYxPMvs6r1Uywt/yEVgddn5WB19Iu8LfduXLmHTUotgUAeBYlP5MhJHU04hDTJ0pLzOWXOFYjlKhHDi+tcTW/76SOYkzQUtQbVc9Jh92dmtJyARyOVH74EKoTkvLrJh93uscJBFxAJIT4DnAiEpZTvGPHdvcADUsrfjnHcvxihRSilzAgh7gbuk1LeJYS4Bdggpfz5gfpwrAuIjhRSSlw3yaef+BrXTL+XtempPJ+awK7WMho6YwUZscG9mRDs4tMnPoIAfK5DKlnBE04Fp4b2cmvjEvKujkByRvE25ocbkQiCIs8so2sgzbgfthQ8lp5GkZYcUFoeipA+mXMm3Ht0b8ARQEpJMreKLZ3f54V4ghY7SEQzmeANcsGE/yXiqUEIgelkeCX5HFknwQT/AorVHJn4Z3HtwiqIZqzAH/0hilp8RP1Z1bKX9z98N450kVJBES4VwQCPX/pR/PrYMvLjhfEuIDomWoR99ONnAu/r2/U3wNeAAxqBNzqEEKhqmItnf4yvvZjnymnrAUFbPDzCAAAImtIxKm2HJ/bN4Hdrl6Erktq6dk4/cScnxfawqnMqy4t3Mi/ciN63Dl6sZsckNhFAnd5Ndj8/ecraN74XO84QQhD2LWdZ7f0s248n35Ldxj31NyKliyNtFKEyNbSMC6se7vMGdIRy6Ov5lmPznTW3cd/ODvKOwoLSLF9ddilrrvw4d25bx95kN6dXT+XCibPQlDfvdGBctQiBGBCXgylrjcAbuwrjEHBm1TTu2FJGRzKIIaz97ue4Ch/8yzXIvpz3vAN795XzSMV0Tq7ejeY4LIwMGgCgjzBtdIJcwSwURFDGWmv3qiVHelnHFFK63N/wdUx3kMjVlTa7kqvZ2vsvZkcOn+H+3x69iedbbCy3YECeb9G5/G8P8493BvjEwlNf5ejXP46VFuFYKR9jzkvGU4vw9QJFCG49/T+oM1RmqZ3UxLpRxMjglUSxxYAB6Mf0aBfvjTXyvmAHN1TsJqYMj353O2OPdAJod4JkpWdUFp8qvMyMffgIr+rYoi23c4B0dSgsmWNT/OHDbndnvInnWxwsd/B3kCjkHZWfb7z7sNt9PeFgfJd+LcK9FAJ5Zwoh7gQYokX40H6OHdAi7Bv17wdOADqBqBADNDc1QPNYDUgpb5NSniilPLG0tHSsXd6QCHqKOHfWH5kSmMf8yr2U+FLoig1IdMXGr9j4ksOPWVLRxO8vfIAVZQ14FZdKPc8Kf5bokOIYC5W9dpgex0OT7afN9pGyNbZa87FRMaWBX5+LJgIo6OhKmDlFH2di+LLX9gaMMyRjC68AuGOSqR4c1ne8gjrKQIPlamzuOrxCqNcbXnU6IKW8kYKCMEKIFcD/OxwtQillB4U4wFoppRRCPAlcTsGwfAB44Iiu5CjAdJLsS95PV24DYc9kJoUvx6cdfJrpqyGgR3jH9O/x5Esf5h2zXuSVRCXd6SAxb4qPV2zm12uXsbWrhLZMYY39K8ufwacPjvxCFH7AuR6TZ3N9HoCUOAg6XS+qgJyEJB56nS4kBbXjNrOJSyfciU+LoStBxFFiGsrmnqE3dSuO047Pezbh0IdRlUNLGT5YlHunoQgdGO4N6MJgbvTwKyenROpw5apR21XhUBc+ugHA1wrHUovwC8BdQohvAi8Btx9hX8YVGbuNJxuvwnbTODJHa/oZdsZ/z6nVvyBmzB638zRmetjUU8q0SJaZsWaKS9OcF+yhVs+z4MxHUIBH903ka8+dyuTo2EKpxYpLhxUkqOQHcuj7s+T6WYpLtST7LINCibHJQ43X874pfx+36xiJ3uQv6en9NrKPwDRrbqWt9w6S3q8Q8JQzJbgUQz20QqIDQREqF1d/mb80fBWJiy1NdOGl2j+HOZGzD7vdRWWTqQsr7E44OEPyMlRFct38i8ej68ccxzkG94MX2r5EY+oRYLgrGfZM4+za8ZsLfnvzX7m3fi1zQ3uZH23ijGAPc4wk+pCJmukI/tQwlctrd+NTx6iLdwXf6phDVM+gCYeomqFG7+mrJSjAlYImO4Y1oGoked+k+/Hr4x8QdN00DS3zBgzAwHW4Cg90zCWpeogYLpdU/ydTw4dPMDIW0nacbb1PkrHj1AUWUetfgNjfPOEg0ZNLcd0Tt/BCa8HAhg2TaVWC0pCft1cu4+2Vy9CU14638ZgsEb6VIKWkO/METWMYAIBecze7E3+m2LuQiDH1iM+3LdHM/NAerpvwNJpwma8bKCMeWo8quWxCPY1WNZOUelpsHy9ki8i4KtP1JOvNIDE9jSIKQaseJ0Dc8TPN00ZQ7SdFlaNIT/JuD37G3wiY1suM9Wh5FJeFoWa+t+8szq/Zyv1N/83HA3fjHUe2o4AWZXHRpYd8XHu2kQceup/V971Cz2qYf8pM3vefF7PVSJCy8vzgtA9R7PHxxY23sTvTTdy1iKe6qd91P2u7H+f00lL8WoTZkbMJ6+M3ZXwtcNwIDIGUkhebr8GynkUhjDNm3NRhU9dNgEupbxnLKn7QNxc9PJQaec6KvogmCtpF+xuzDGGRC17HX7t+zKZcDKtv772WD79qjlj2KywT7jArqNU7KVbTmFLDYVDDQEUS8Uw67H4fCKpSDIzO2XcldFt+4pafvKOgKQ7ruv/OKaVXHJV+HAxc6XBvw09Y3/ksTq2k5D8k0Q8oPH2zy52ruvD6DVxREHu9aOJE9mQaybuFZV2BZG54Fz4lw4s9hYXZ5zp+x4qyD7O4+I0TaH1LGIG27CbWd/+OhNlImXcOi4qvIeIZnXHSln4UM/8s67J1NJoxYnqGak8cvzo8m9npc3M7sqvZEf8tM2LDlX7Wv9zAb+9dTVNbnLkzqvjg5SdTWzV2QKzO/xIxrcCM40pozvuo9g6Ps7ouxEUN6+KPsCtXREc+SFs+jAAqjDiTAp2MdEalFDgI6s1iwkaONifc/w0CmBs5F0UcnZ9f16ega9PJmBuHZSearsZfO+YCoEmJlLApvuaYGoE1XY+xOf48aC5q3+1wdUnjB0pxUci4g3kcT7VvIuQffBYqvQmiehptYKyQSCRPtt+KJU2Wlbz3tbuQI8Cb1gh05Hbw1/ovYMsOpBR0mEEM1SJpPcre1FNcMuFWYsbwkXB9+8/5Z89J2GggBGnTR7NZxBSjhWojMWoJypE5dvfeO8wIPLFyO9/66T/I5wsjYUt7gmde2Mlt372KiTVjpao2kHENDCXDqq7JPNs+l1/MewKPcNAVSd5RyLsqf1h7BcrsZ+jK++nIh4joWQJ9HkDc9FPkySBEobKuIRejORfFkQoeYdPgKWJ5eCcWDpoAn1ZFq2XRmH6JmsCi8b71AJSV/JYNDecRVDqwpYKK5NfNS9icrqRYT6FrDpZUyNivPTdf0szzUnuBgPWhjjtBDDfynWao4JKNCJflbAijDOg/VnnjQwzAcDzX8VvmRM4mdBRiLuONN6URSOQbuL/+WujPjhOSciNJ0vbQa2uEtBwvdP6c86q/P3CM5VpsTqVwRQmDTrnARbAjV0W5J4k+xnqxO0SIxHUl//PLxwcMQP+2bM7ktj88w7c/P7y+ynZsAkqGl7NVLAvu5u+tc2lrK+Ka3JkUBTOcHGzDzRrcGZ+G1tXAWbafdlNQaqTw9OkVAKRcH37XxKfa1GeLaM5FB0qRTanTmC/iye5ZzIl4yNMAdgZyL7A9+QIzgidyUe23x+W+D4WmljG96nE+sfpz5B3YmSlFIvAqFmdXbMVyFTryIU4IFI37uQ+EX29Zx3deeIqiYJJoSSezIzm8I9wodxRpawHpnIEI5wZsg+3uP83GcWFPag3zY28fv84fJbzxEp0PAk+1fQ+GEE9A4e+QZtKUiwKStuymYcfsSO2k0w6NqRoMkjYrPGqrQKMyMCjc2Z1Ik87kR+0nJWzY2jRqe86NE1byNFlR1qbrECaYMdghi1iZnMCPW0/gz+laIrEUoendvBgvBNf0IQagvye9tg/HZZgB6IeLQgqDPI0wQMdd+Lc9tZb61NERJAl5ivj5yTdRaSgsju1jRel2Lqt5keZsjPXdNRiKxazoeUfl3GNhbVsT313zLyJF7dRVNuKg0mUGRrEVlfmTYxK5ehWDK2suosSIYCg6e5MlOKPHBaCQvq0pxvhfxFHAm9IIdOVf2W/2mKEU5ni6EiHnZNgcX8nm+EpMq4PGVGzMH9+VCo25GAIPCoUEEVX48KrFzC4aZJEJ+vf/oxdHR6+J+7UiLARZ10NDvpgGYthS7QvgCSyp0WqH8SsmigouGrpwxgweZh2DuOkfO/cayLn7D16+2H3Pfr87Uni1CDfM/B5ur5ek6WVt9ySEIjk5ugtPqoxF5UtGHXO0lq1/8/I6tEAvE4p6SDpeJILWXIS8q/VxNBbiMpriMruoGWWgEgP8ms4pFZO4Zspp/GHZV1kQqyYvPOyOl4z5zGhCMCW4dPQXr0O8KacDXjVC2h6dRw6FgJntKiDm8t0t16IIFYmLZWfY3jmHE/31o0ptHamgO5KY/DHlRetJWfsoMuZQ4V+MRy0w0LbndvFU+y+omm/TsKEMxx60r15D570XLWbX9lbCUT+l5QWvQhEKjlJOud5LJudHEXLUPNSSGsmcgRHoIwWRyn4LhKYbLWzPVIx53SODm0Phyv0MZ+OEybEirpn9NW56/k4mxLZCQmVv/F3819s+NLCPI20ea/0jq7sexnRzVPmmcFH1h6n1H7liUT+2xNsIBPN4FXvAKDpSYXOikmJPhrCeI++oSCGIRCwW+RtpTwWp8U7muhkXsLx8IkIIHOmyPbkTVXXZFK8hnvOxuLKxIEwrFRQB76j+0rgmQx1NvCmNwJKSj/FE69eGvShSgiMFUT3NjnQFnvzLKMIceOlcodGb9VIuk8Tx0U8ObkuV9Y1VKLtKedcVNcyMnURT/Dt0JL9OMqkAEp/vah7rXIUmkiy6yMC25tO8pRxdUxBSY/nUam77+t8AsG2XmXOq+a/vX0Ek6mdO9N2Y7h20W/tzyuQwrybn6gVJDTm4XUrwKhYz/a2s7e2m1SkaNiVQcKky9s+Eu6joXYd8jw8Vp9TWsbzmS7Sn0/h0nbAx3Gu6t+GnbEk83yegCk3Zndy+6yt8fPqPKDHGhwex28pgCIhn/IT9WTpNFYmCRKHTDNJpBhASKgOFog2fblMXi6OLjcwtfv+QpCM5MPqXR1N0ZsL8efsJlPsT+A2Xny77FqXe1zbWcSR4UxqBKeGz6MhtZ1P8jwMvueUqPB+fSMIuYXqoHI+yDltC/5KZIuDzsx7ljNKdpFwv+8wYHmEzydPF3T2Lud+uZUFNJa2Jn9CZ+i1SDgaIGpN3ssSXRSIQXsm097ezOV6DmQxyvvcrfPtzD5DPDS41bdnUwNc+exf/c/u1zI5dytbEXyj2x8e8Fl04hL0mOfrdeUGPGaXC6Mbt60FUS/O24Hby6FxWvp4HOhbQasdwpIJPNakyeijyZHFdUEboJdqpMiYGXxttQiEE5cHRiUG9VjcvJ1Zhy+Fl1ba0eLr9L1xWe8O4nD/nWmiuoDETYWk4TtbVSduFegoApMC2Rk+2NEVnd2oPi2IFqTFVqMyLTGdTYjsukrA/T9ifR0Xh5NIlbygDAG9SIwCwrOx6Tiz5EOt7nuTefWtYn4wT0LxcXnMqNf4UT7et5sJgMyf4etCQNNsGFp0oAsJqjnm+loG23jt9LVPKv44iBO3J23BHpMOGlWwfk0/hYarU4uQjKi3BOu6/ZdUwAwDg2C67trfQ3NhNVU0Rl9bdzotdd3DtpFXcvmcOyEJgCWBBpIvq0DT2ZptREDi4zAxP5TPT3sVzjechsDCUApee20cncmKsng6rB7tPPVkIyDgeNGnzxN4ZzC9pRhGSDR01NCSLWBa8i9NnXnHUColeDd1mK5rQRxkBiUtLbs+4nac6GKI5k8LnNXl2zxTmVLRS6+shbXvoSgfY1V7C/AmjA7iudInokWHbrp/6Pj6/8QfknTw518SrGIT0AB+c+MZJEurHm9YIAGiKhxOLz+PE4uER6NbsXipy32Wyp3dAQLRKy9HogCkV6q0IvY4XVbjU6QkMLBzfH/jWxh/Rbs5jTqCJxYF9A1l6I6m8VCGp1XpotKtIdIxNGqJqKj1dKapqivBrJbyt/HOk7KeZn/410gHpCnTdAdXDOZWLmRy8kuZsG7X+Sqq9xdQn/4rLZDxiO7b0oIhCokrSDWJKnYiWw3Uh43pwpKBMzfFY0wz2pUrZlxosydaEw6ruezmh/ecES/6CohZjOl009t5JIr8OnzaZCZEP4NePTnYhQLGncpQBAFBQqPKOH2ff5dNm8L21nai6g/C4rGmoo+DaCxRFUhTJEDPMYbSkCgrlnhA1HhvXzaAohRhQmbeYWxd/g2c719GYaWVysJZlxQvQlcPPHj1WeFMbgf2hXFcp9qSGvbwCyEuFNjvIJD2OpQq67ADNZoCQ0DjPezeWoaAJl38kpvD7jqW8v3T1flchNOFQ4T2R6PLpNOzuwrKG1yE4tsOkqeUDn6WU/Hbfn9A1QX/6n0QFHP7U8BduXfxjJgVqsJweVjZeQMpqxava5B0FgcuzXVPZZ5WwqLiI+WE/hhpCE146c9sxlBg/W62xydRHrQc5KFiA6zSQTdyIEv4qa5ovw3EzSEx6WEtr+n4WlN1KzHd0ot0hPca86Clsjq8ciAlAwYifWjaKu/aw4dEdSotTpDMeEBCKZHAdgRAQ8Gjcsfx6UnYzd+z9DY50caXNJbF9zPU+SkvHvYBDKPAhYpEbEULBUD2cVT6+BVDHAm9JI4CzD1XxDWOhFUKQcrx0mD5+uudUNnTWIpRCUO3CypeZVfc8frUwRpwf2U2H7Wdfvog6o3tMQ9BuhTmz+hMYVxr888H1JBMZbLtwvNerc9WHT8cfGAyO7Ux0oSqjcwwAck4GW9roQmd3/GZyTjNGX18M1cWVMC3Yzj92zyNFHIc6PjfzE8PaqPa0cvFjo6u1DcVmRfFewMbKPUqjHcR2e2FgPHRwZZZtXf/Fsup/HnFF3v5wac31hPViVnc9TN7JUuufxjuqPjRuQUGAmCdIQNdQQ4X7LGUhMcijaHx21rnMilQBVSyKLaQ514Ka+w129lGkzCNl4Zhk+nZUtZhI6GPj1q9jjbemEdCmwMj5p5Q0WX7ub17InkwpNSVxEIUlxSe6p1NqpPhQ5WYUITAUh7dHdvG33pnU+spw7B3ss4rYa5biSIVKPc6+7AVc4CsCH9zyh49x92+fY83KHUSLArzrquUsO3X6sPMnrTx524PPM1aykY4mNEwnw+7exxCIYbUCioBKby/SLfR3ddcWfrD123x25hdRRGHorw1E8TVoMDHDsvJdTA530pULkLa81Pi7+lpy6c49B2Pw9OfsZiy3B496dIJeqtA4p/y9nFP+boQ4OmQdZ1XM46evDHIoiD5lY0NVOKdi4cB2TdGY4K+lvufOUeXQUmZJJH92REbAcvNs732aHrORCf6FTAgsPGrG9WDwljQCQq1Ees+C3BNAoVjHkQIbha3pSiL+HAOksUISDWT5S9t83luxkXBf8CykmkyOXctJtRfxoy2fosfeN6AKvDNfSbGvFcs10RUP0aIAH/nUuXzkU/tnuJlTVE5TdzmTyhpQh0TwHVcwzVjCys47WdN1N1L6cfERUnJM8nQMm9LIvlJhieD57np+ufsXfGTKRwHI5iyq0jmuXvAwhmqhKRIZLnDldzh+2i2HudET0Nw2bLewUtGfSTcY+zg6yrvSTSOT34TsXwEbqc1CRL6B0A9eMfhgENb9/M/ia/nS+jvJOAVjG9AMvrPgaoL6cIlyKV1cObaakOsevvBoZ24vd+79BHafZ7G66y5CWinXTrkd/RhlGL4pMwYPBiLyAwj8G4gooNPFDJK2QdifZyRrtKKA1+vgInGlxJHwSq6Ys8tPoz3XQK/TigSiSo4iNQvCJWl3szmx8qD7Y6gan593ObvaqsmYOq6EvKWT6J3MxXU1rOm6B1uaOAUxMpKul31WoTjFkYJd6RI8HgchJKpwcVF4rG1QbLSsKMipi9fj00y0PiNTGHwETa5OHA9J9XJqQ1cDPpKOQa/rK/xzfISMJYUp1FGAjF/XZwBMwAX7ZWT31UhndKT+cJA3N5BM/4lc/gXmRSbw4Ok38vOTPsotJ32MB067kbnRCaOOEUJB16aP0Rp49DmH3Zd76m8cMAD9SNod3NfwlcNu80jxlvQEAITQEaFPQ+jTACS6/4iR+kEha28MqIpbEL8EHDSi0S8R80RY172WEjXDhcEthFQLSUEf76/JKexObWZRbMUB+9GafobNXTeTsurxaRXctOQqHti7jLZ0knOrp3L50nncXf8fox4ciULC8ZO2dbKOh7sal1AeTPbLY1JIO5ZYro2uaAghmDGzYT/S3oIMCuu6/htL/zSa68chQ39eogs0Z18h7/RgqOPLEZjJb2JV9z62mzWEFZvl/k4mejIgLWT6d4jwFw+7bVdmaet4P6a1vm+LQNPqqCj9MzPCr85wXxT9b9q7PsAghaZACC9F0a8fVn/iZitpZ2wvoiGzAVc6KMdgmfYtawRGotg3DV1x0YVdKCUegYCaZ2VuAlM9MRZW/oxKT2HpKqj6uDy8Ea8YLOrxqCbvDm9nq3bgtNHW9LOsbvscTt8LnrYbyDk38bFp56Ar5/CTDS9y26onOW9RC94xpskCjZ25d/BYZ4riUAZNcQE5QCHm2gZffv4RdiQ6OaG0mjkxDyOJOAcgISMFvZlbiekWYkRisotNfe/fmBa7+oDXdCjI2il+uusHpO0YNgpNSHaaQc4LtrDU3wP29iNqP574PnlzLQUPowDL2kZXzxcoK77tVY/3eU+jouRe4skfYVqv4NFnEw1/BsOz4LD6I8dgqhr6rS1NPEdpynUgvBZahA7QX7JXL6W8uG/7r4HTgf6J1wellOtHHv9aocR7AkEtxvvLVvPb9pOHpd0KXJbG9pKVHraaLnNFkP7Zm5t7HGVEam/hGMkCX4oDYXPXzQMGoB+ONHk58TfO8t3DdVM8TK6I0ZEP0j+6D4WheLl+5repa3yAO+sfHcIfCOmshz1tFWyWm3CkZHN3K6dVzubCurWj2kFKXkxNJOV6qfT0ENE6hsl5A7gyP+4qRSs7/0bayWEP3GuBheCfqQoW+TJ49MN72QAaknE+9WwnmxP/jisFi6KN3DDtGeoCPWSyDyGljTgIUhXDOIFy4/eH3Y+hiOpVqELHGSMnwiMC6MI7xlFHH4cSE/gksLX/g5TyVCnlQinlQmAVcN9+jsv279dvAIbgc0O+O2YGAApzwCmGn1nBFj5YsZIaoxufkmei0cHZxVsoMwr55LbM82jLt0haBSGU5syaUclCALoicZwxpRQGkLLrx9yepxC1rtTznOhLUuJN9RUA959HouGy3NhBJvcCl9VeygcnXoy/jxrHp+jE4xOwXInTl+RuuS5PNk2hNT2Z/tx3KQHpsjY1iYTrx0El4YztvajCR7Fv4ZjfHS62J9dhy9GkIoqAVjuE8F91WO2+2NHEGQ/cxrqeKvKuB0vqvNAzkY+ufS/N2TBSuph2y6s3NM4QQnB2xSfG+oZzKz95zFYIjqoW4RsJrpul19oECGb425jhbytsl9DhhGiy+5fGXFoym/jVrg9xae3XaXeK+lJ8hxsCUyqgzT/gOf1aBSlrtCHw0Ff1L2C6J8uLZhC/m6PdiuBRbJAFgdG442VT20dYNnEjF1efz8XV5+NIl6xtsWDXzaOvEcHvdizkipkmcRO6zABhT46QliXZJ7GVdHwkbD8RLYMrBfXZYiQaM0MeqgPnHOJdPTACWmTM7S4qgaKbEerhic18buXfsaXLcI9HkHM1/rDvRD414yny9h4M/fDlyQ8X86LnEtHLeaL1ZySsNiJ6BWdUXEdd4PC9niPF0dYiBPAKIdZSiKl9V0p5/5DvviWE+ArwOPBFKeXY2TKvAXL2DgQCOeJlVgSElEHOPynBoiBv9VDz95gXfi+N2Rep1eMDKciWVOh2QkwLXn7Ac86O3cC6jq/iDNFuUZHM0M2B6UW/uKgiJD12AMtVcaQg6+psz5azIFDPPKuVgF4oIVaFgqFqqELgjJIbczmpdgu3b1tM1tYLtCJCUhFOsLxiN5k+Q7AxU4tHmrzQPQkhQEHj7+0aQd8rnFkxfst2p5RcxJ7Uy1hDfnaBQql3EqWBw5MOT1l59ib3t4Sn8HJvJWkMPFrNYbU/HpgQWMAHp9x6zM4/EkdbixBgQh9H+vuA/xVCTOnbfiMwk4JGYREFMZKxzv+aaBFqSimOHNsdSzreAfdZIjD75t6mk2F6eB7r7At5JjOZNttHh+3n+Wwd3qLfo75KHnlN6FwWlXwZb9+I50EyWzeZ3Kcy5Eiot42+ntOD2wAAIABJREFUvxVsqQwkuBQiFQqbMrXsSDwyrF1dUblk0mwMdXikeVZxJ4/VzyRr6/ST6HlwaE5E2dhVhS5s/EqeSXqc1d2TsaSG6WrkXMi7Nt/Y/CfacmNXOx4OpoYWcHb5pRhCw1D86MKg0jeRqyfeeNht6oo6irJ9EJIKXxKf5yS8+vjVJLzRcTCeQL8W4QWAFwgLIe6UUr5/iBbhfonepZTNff/vFkL8C1gE7JJS9k/K8kKIOyiIlo51/G3AbVAQHzm4yzp02IRps4KU6clhc3xbKrySL8dFEFYtTvX1Uqe3kXFVVmeL0YWXf5/yLXYk17MnvZmwXsSZVafu19W1nA6a498jnv0nAg/FgSs4v+5BpLUDuq8BFARgycKUYk0uiCsFjfkYw93bgiGQEnak97BwBJ/lN5acS2cuzcrWejyKSt6xKQ8ZbEuqBFWLPyx4mJnBgqJR3lV4PFlMi1DQhcu2ZMWY8uaulDzasoH3Tzr9iO41QHd2Iw82fJMdGQuvYjM/pLOk9EtUBo7M0zBUjfNrZ/DQzi3opgRHwfJIXK9AxeWaaQGmlP7oiPv/ZsJR1SIUQsSAjJQyL4QooWBQvt/3XaWUsqVPnuydwOYjvZgjwfr4RtakJ7I0uIcSLVUgmxSwKVtLux2hWM1xVbgRT5+BiKg2KwLtNPX8F7HK25kRPoHpoYW8HL+XB+v/HdPppdw3n6WlNwywGjtuhm2tF2E57fTz8rcnf0nafInp5Xchyx5HZv6MZb3MvnyKdalmqtQeipUs1SLOyoxOr+uhULQ8aBCCY+gj+jSdO868goZUnIZUgppgmO9v+g3IJP848X7KjSxCFGIeiuLy9nAHj2ajNDkGOVfDHsMrsqVD2j5yEc6U1cI3t36HuO3vo1Jz2ZOTdJlf49+m/fmIA2QiZ2KEBwljDAmi1cv/XPJOzqsbTPRZ07GNe+pvIeEk0ISHhbGTuaz2IoqNsVih37w4qlqEwCzgViGES2Hq8V0p5Za+XX8vhCilMLytB45pRcb/bl3N0qI8z6WnYQgbQ9gkXS8uhSq9qZ4s2ojR0SMkNfJZTKcbj1rE6o7/Y1viQew+m9iYeYG2+k1cWncHYU8V3en7cdw4Q4U5JHky5kuk8xsIGAsQwY9gANOcNqaIS7GcBB7FYZa3lzOCrfykcybb81H6vQJFwGllBd5+V7o0Z3dhuSY1/mnoiofaYJSIYfC+Z39OZzbBvFAP5UYW0xW8nPEzL5BG7+MdPdWb4P5MCbW+HtbFJ4yiOjNUnVNKZx3xvf57423EbWOIGIqCAzwbD3FBZiUVgcMnOXmqYQeP9r5MyJ8n6M0jhCRvayRUlwnqoLv0RPMq/th4K6owaUgXk7M19qXX8kzXs1xSdSHvqj10FaM3Kg7JCEgp/wX8a8jnFWPssxb4UN/fK4Ex/Tsp5ZmHcu6jifp0F6a9B58wSQofOamTk4XsHNG3HDfb08FGsxAULFWgRgNNFKrQMrltBL3z2Jq4H0cO5fKT2DLPhp7fc2r558iYL+HKzKjzSyBrbSFgDEaIZfJHCNmDp092XBcSXUiujO7lK60nFEgwheAjk64ioBfTmt3Lb/Z+i5yTHghwvqvmP5gbPZlf7nyKtmwvlnRZEmvDdBUe7y7m7SUdw/IbfIrkkkAnd0vBzGAbr6TKsGSB9NSr6pxRNpc5kSOPqG9KNg8xAINQhOSV5MtHZARu2/wcsWAGn2ENZEd6NRsj1sutLz3F/9YUDOY/mn+OIh2ea5mKK0UfN6BLY6+DkI8wOzKLWeGZh92PNxKOZwwC3fk0fs1BCkGlJ07c9pNyvAgkATXHBI9Dg7QHXPCsI2l14URP4cENeqaTMOtRhD7MCPhEHr9iksw+i5SfxatPRQgvI2dPAgVDG5G/nn+SsbQQqz153l19Nh41yiklK/BrfmzX4vbdXyXjJIft++eGm6nw1fF4yxYsWWhrbaaUvzRP4rLqXaMTnAT4kNRoJicX7WJFuJ5X0hU4BLlg4qdYXjpnXNayQ1oR0MbIpCWJIGYcOrGodFOFo5UQWZnB77VG0c0DtHgKyU45xyIhJZu7qvu0A/qZhhXyjmBPIshTHU8fNwJvJUwPl9OSjaAKF1VIivU0xXpBGgwXyvXuYZ6xi8CUkkZLYKsL6Uy00Jrdh+0WDIBAMkVvJ9DHD6CIXra0nMnkkl/QmvgxDkONgIaulhM0RpBTCC+MUcUmULig6lLEkPTSnakNOGMk3TjSYW33Y3jVwVWKVzpLecrK8p6aXcP27bYMdqSjVBspIsJmfa6YeD6CokjeFt7LSeJLIO8C4T+IO3pgXFB1Bdt3/HhE3EHiU1TmRlccdDvSaWZ3y43c3uTQavmYF3I5qeIUHoszpoELhgq/oq6ogCSe9zOat1nQnfVjuvtnZ36z4bgRAPyawbVTLmB1Zz1zIvXoSqGe3nYVKowwqsjgyOEpwC6CejfKLY0z6c7+kZwFy0uLmBzsoFrrJqjkhhTr2OTtepri32R6+b3s6/4sGXMrAgh5T6Ou+AcIMWK11v8eSN1KIX+wHxoYpw4zAAAZOzkqv6HQR4eUneA9dUu4ads/yTkW0hGsaqvBKbSGK+Fbu07iT83T8SguplQI6HkWVjWgKy6qAg/2VvB40uHrnjsIRI6c9HNudBEXVb6dB1v+2RcNAK/i5cZZXzpoT0NKi3u3fZKbmiYPXPm2DPi1TahCDgucFg6AucUTgUIuRZm2/0dfIFla9MbQDBgPHDcCfbh6yilMDpXxYOMfieqbKPKoLCq6kGmhmWxq/8gYRyjsylawvkElbxkI4E8di5lR3MrXZj84RrWeTTL3LIZWx8yKh3DcJAINZT/luSLwEaS1GfIrQRRGLtQJiMhoybBJwTm4cvTUwaN4mRlazKzISazvqWdTfCXzq/bRmzF4qK2ai8ub+EPzDO5pmUZeauT7mjDzCts6K1hYUUh71hRJxlV4sOUprhwHIwDwzporOKv87exI7cSv+pkemjZAgHIw6E09yv82TRr2sksgY3so9ebJuwrOEHIUr+rh8toVA58/M/O/ear1ZjpzQYZ6AwKXaVGDk4oWH8nlvaFw3AgMwSll0zilbHhdt5QSQy0nY+9jKOOOIjz8adckcmYh8aZ/NNreXY7lagMBveGQSCzAh6qMlXxZgOWm6M3vxAh+mUAoT3f6eRryCn7PMqaJGI69l1z612QdE+l9B8W+JSwpPp+1XY9g9mXf6cKgwlvHrMhSVKHwoWl1PND4WxxpEvOm+X39NKQU/KpxNtkR6kQShdZUGMdtGSA4URV4Nmlw5SHe0wMhpIc4IXZ4gqgvdO0YU21JAilb4ZzKhTzTsRHbdZgQKOeT099NrX9wKbXIV8sdp/wHVz51O1mnEOBVBFT5w/xm+fWHZJDe6DhuBF4FQggWVtzBxrbryNh7+oi9FGL+z7CjZx+jgltSYU3XJFaU76R/KTBhe7m79UQ2p2tQXvk+K8rm8OmZFxP1jC7W2d7zK7b23IaCjotF2onxcNdkHGkgWcP1ZZuo9WwZWGQU6V+zwa5gVvFNTA5+hhe6/onp5lgQPZUTYmei9tWn/6vtF8OClssm7GOjGaAlP3bBUEGsRUEdEpzsl+p6PUDVaoGxSUcEcMPUy/jczKtwXAePOnbm5tRwHSsv/CpPte6gPt3NzEgFy0onHSDj8M2J40bgIODVKllY8Ssaeu8k73QxIfxBuvJRhLgFKV0Mj41Hs8lbOqalc1/D2zi3Ko5PJnBdi5saV7CnXyhU2jzRtoltvU384ZTPoA4ZcZrTT7Kt55e4Mo/bFwvwiDaWhbM81jOHE3ztRNUd/CtZh1AkSctLIufn7cXbWNf+Zc6s/Q0fmPSfY15Djzn6hQl6TGpD3ezpLWZkBrlXs9GV4QYg5DjctusBPjz54mPKiQewtPwC1K3PjxITFcDMSDkhvWDcVPXAI7quqJxd9dZYBdgf3jo+zxGgqfcenm04hb3x/6MleRerm87ndy9+hUAwzZyKVk6sbWBRTRMn1DQwpaKNyyeUMkuT1GkeJugGv5z6NB8o2zbQni1dOvO9rOocTpqxI/47nBHElqqQlHp68Skmpwd3scUqIajnCOl5qvwJpkTb+VXzEqLEebnnzwC40qIrt57u3CZkX6wgqI0mCJUS5pa2oCsF6RLor0hwmV7chiMFjlv4l8kYlGh5/tq0kqc7Noxq67VGQPfyzflXoiIRFMhUBJJSr8o35n70WHfvDYXjnsCrIGs1sr37q4A7sOzUmI3yQDpIrZYiFsigaoXhyPA4VKu9XFt1K0JmBuTNEHBV6Q42pEt4MV2Yl5quzd5UO28bkoGX3w/1lJQCQ7FodzRUzRrGgehRHJaX7WJtTy0LvG20ZZ7nhbYvIKWDlJLWZBmzo59hSdH7eKrjlmE0ZXlXI+AxefvkLWzrLqczEyTsyTG7uBWnx8cOuxSPapN3tIJyr6mT00zu3nsny6IahmfueN7qQ8Zp5Yt4aMUE/tq4kn2ZXpaVTOeM8sVvqfn8eOC4EXgVtKcfRkpn2Lrzk90zmB1tK4iEjHCLp/viCJlin2WTki6aEJSrGmEFLinaM2AEPIrGxODwnP9y/ymkE01Ihq/5SwS9tg9LAWOM51sg2Z0rYalSw/Otn8aROZrjMX69agUZ00CIlXi1IDecfiUZ415MmUUTBoIism43ft3ihPLGgfZ0HF72hUnlDHAHGXD3uGF016bX6qG142Iioc8QDX/8MO/skUE6Tcj4FwlZ63ifH4jMR0S+O3qp9TheFceNwKtgpHveakYIeW1sXNLOaOK/5YFmdjmDUlaWlOyzLSoVF79aeLk1oVBshDm5ZMawY2dE/43G1MNYTgoXExDYUvBiciouCgnbR5k2hlyXkLgKbE68yATDxXIUbnvmHDKWh/7AZd7Oc9PjHTxy7e0UBVU8io/7G2+hp+dJ5AidARtB1hn9aDgoKI7CokAjCdvl0cb7aJW7KTEqObviIqYGX5u5tZQmsus94HYysGJjrS9sK30SoRx5QtNbCcfN5qug1H/2sNF+c6YWKcSYpbYA1Ub7KOkOCbS6Dk/FK1AFzAp28r6Kf7C16wfY7mASklcr4ayae5gWvZqoMZuqwBksr/g5byv/CMuKFrK2ZwF5Z3jOvemobGyrJeX6SVs9rNwwiZt/fylus4HWK4bpiLjS5b4tW/GqQRShsqzkArRRPHsSJHSaY68aaIpLsxPhprYzWJWupTnXzsu96/nZju+xpuu5V7ud44P8EyDTDBdJcUHmIfeP16YPbyIc9wReBSFjFiW+8+jMPIyUkHQLUtYrgm2kHJ3n0hWYcvDFNBmdvtuP04o3EPO18nDXPL63+0S8ShvLYx/mawtvR1cLo5dXK2JO8ceZw6CbXROEt1eezvx7v0mq+wTeMX09qpAowmVjWy33bFlCVXmcJ14qpaspimVrCEDLSNS8pHxmJ3NqCzRmSTuIlEsRQlDlm8Q7a67nwaYCy40rHQJagHiuc1QyLYCKQ8zI0OEUchxsKbAdlYBqAiZ/bvwNJxQtG1iWPGqwGwov/ChkkE7DmH0/jv3juBE4CCwov5nG5P3sjt+CTzic7O/g/FAzunApVk0eTtaQcTVCah5XqiBGGwIJ7DPD3NW6dIAVOOd6eKa7mm9u/jFfX3Bgfn2BwLUVnu+axgvNU4h5M6Qtg5xdmJIkEgFoDOE4w49ZsXQDs2fuQ9cKXyjiLla1J1leXtBbWBg7jbmRk2nJ7cWr+Cn1VvO9rV8n5umg2wwW2hFgCAtddQjo1rBegSTnaAQ0C1vadJudlBrlHFXos0B4YGS9hAgg9CMvdX6r4bgReBXknSz/ar+XDfGnEdQxxe9wRnATRl99wYWRRi6MFIJq7bbO09kyphnNwyYLgsKNfrRrbl9p7iAsqfFEezdfsPP4tf3LUGmKwoRoD/GQF1V3cR2BElchUUiECSs6OVUj6wy+GCVFCebM3FeQOO+DJM8rvQ8xI3oRxcbUvrZ1av2D1XufnvElbPlD1nY20ZQOYrsKM4OdCK/AGTXKC5y+WaUrHQJq8GBu65HBsxzUiX26BP3XJkCJgXHwFeoy909k8kfgNIFagwh9FuHdv1TcmxXHjcAB4EqHX+z6Tzryjdh9XPEpofGHxAQ+Ets9qj4gqtrstadQpcYJapkBQ6AjaYgX0ZiKMZYEkIKgI99LnbZ/dt0Xu3eT1zxofWrEqiYJFWUQQmKnfHx50WV8Zeujw46ZOKEVRRktLupIm4b0qgEjMBIeRefLs2+kPtNAQ6aRcqOMWl8NN7z0iTGrFRUkmtCYE16I/1UEV8YDQihIbfoIcRIJTg+4HaC+upKxm/07JL5IvxYlzh5k/P8hI99H8Z1/VPr9esXxwOAB8EryJbrMlgEDAGBLmw7Hyx5r9MPe44S4YeotbDdn4MMlLCzWN0zkqvuu4/OPv5eGzdX07IjiWiPrXDXKjLE5Cftx285HsEbEIhUFwkVZ7rnkCs6ZPZ2SUAB1iJGxLK1AkzYCAhXtIJRuJvhrOaXkZKaGpmBoBqeXno5nDMVgvyqZEZrL+ye+NuRQ0mmH3N8ZzbeQR6Z/dXCNpH4AjKRKy0Hqh0fewTcYjnsCB0BTZiemO5pTz5IKjVaYKZ70kK1eKkpvRughtmbCzDV8dMRD/HD1heSdwdx1N6OT2BMlNr1A8qkJeM+EU/FpB5bj3pcem2nZUHUqwjEURfCr69/N53/3EBvrW3Gkw67mct6mFMSfXCnY3FvFjkwZtlRYFd/BJ6fvZm704Fl3r5xQYOV5quNphBBoQuXMstM5t/xsIp7x1Sg8IOzdIAyQI2v+bbBGZzNKaULuIWTuSVBKwHdFYQowFpyG8e/v6xzHjcABEPWU4hFezBFMQLriJRq8BJS/gdMG2iRE6AsIo0CLpakB7upezLYt1aOW9EDg5DWcnEos5OEDk87gyrpTX7UvE4Nl/7+9Mw+Pqj73+Oc9Z7YkQyYJ2YBAEsIWdiGCK1oUqtVWtKhV3LpRt7bW2mvl2selfay1oly1995irZdeq1DlaltotUUptYoL+yI7BAmEAEkgmSwzc+b87h8zCVkmKzOZkJzP88yTmXPO73feNzPzzm99v2yqPECqvYZzPQfIcHo56ktmW/UoUsMbkbI8bpbceyPPbF3OykNrkYQgH5/M5euZn1Fg93NNYiVrveW8cmIkxbXH+PGW/+b5KfeR7+64+Qxg02zcmnczNw6bi9fw4rF7Yj8TENGQoRECAIAOLZSElapHld+EaexDox6FBnVvIOKJmLQFLTs2Nvdi4qlFmA8sJaQ5sAG4VamI72zcGO+5gL+WLoHgaYUhQbCLg/EZ96Bp90csd2H6ZSzcvI6SynQi9bjcNieLJn2HC3JbS2K3xfyCWTy5/Rmuzf4IXYLoAlnOKs7xHKPKtwFv7Z+orPs7di0Zh/2L2BJNTOBrqYcoctXgCm8GutLzOee7y5h/YAb1pvDa5++yYGzHIqNBZXCk7gA2sZHtyiPN0XovQk8h+hCU80LwfUDzpCsOJOkbza5VNW9gBHZhC8/YhPYZ1GMqAw0XzbsELnDfF2Prex/x1CL8BfCsUmokUAl8s4u2xxynnsD8gicY5MpDFxu62BicMJz5I57ArrU9kl9Rk0uVz0luxjG0CHkF/MEgYzO7JrF1Ttpwbsk5jEMLNoqF6qIQfKwtnc++6j9QYVRS5j/IaMfLOMQgy17LjAGljQEAQhqJbj3AbE8JJooD3vb1EgF2Va3n5599nZf3P8rifQtYuPMuyuqjK07aVSRlESRcCzgBDfSRSNpLiK2g2XU1tUsbA0BTAqaChHmh7gECWgYk/wQtsf9kGW4gLlqEYa2BmYRUiQCWAI8C/9XZOnqKTFcO945aiNc4iSBtioo0Zdn+tcwp2ABKsfxEKnV+MM1QsznBZuPb04pISeiaBLVSQQJm5C+eX0FoqVEoOuhagDsGvc/O6kIMpeFssYYxQQsyKbGcP1UOZ8SA9uW4Kv3HeO3g082kwvzmMX6z7xEeLHwRWwcqS7FCxIV4HkclPwIYiEQOyhX+GpIifsoVx7VJZGU+iFIBROLjR2+gsy2BBi3C1vNNndQiFJGPRKShuzAQOKlU43xTCTCks0bHA7ctpVMBACAraQ8O3SDRYTB3xodMyD9IittLVmolP7h0Mt+78IJuWKCht/FBdwCXOGGCXWEPKxOlOmq5PX8aTq11nz1gCof9iTg0O18bdnm7d11f8W7E1GVBFWBP9cZu+BFdRPQ2AwDA9kA+/hbJUEwFdcpGvQwL19F/AwB0oiXQVIswrEDUkpuA37RTxTCl1BERGQ68JyJbgUgBI+JifBGZD8wHGDas833oeDI6tRZbOC1XgiPA+YV7OL9wT2idv7+E3dWDGDWga6m1RYS85Os4ULUcs8mvso4i3x5KsZ2mQZETPvYpTIQk1yRs5jAwDgAG245msPbzoTgdAfakTebpybeTm9T+6r7qQCXBCEuhTWVSE2wr7vceBg64kY9O7eX8xLJGrUm/0lhaNZr5g7qe3rwvEi8twuVAiojYwq2BHCBi57SntAijyRjPGHZXHUHTmpuraya1ZpBFu5/nuXOewaZ1bXJm/MDvU28co7RmDRqhnYqDbQajw0t5RcCuIFOHsqCGx3Ux4pyAefKHLFiRxKq9efiDOg7dhojGl1IUo5Pbv+fIAZPZcupfraZKFYq8pLFtllPKz6nqxXhrfo9p+jCN2WSk/5CkpO7JjXeXqWkz+e3JNayv2M1gWyX1poPPjWRuyH0gbl2Z3kaH3QGl1ENKqRylVB4h2bH3uqJFKOG2WhMtws+UUgpYDTRod98O/PGMPOlFXJhxPQ69+by/qaDetOEz7QRVkN3ePY3nSk6e4scr3uGuN/7Iu3v3tayuEV0cTM/+JbOy/o1znQazE+qY6vQ3W4SoCbgFRqYuQNNsiJ7O6qM/4d39hdQbdkylUW+Y1AUM7n91BX6j7Q1PAIWe6WQ6h2Fv0uS2i5NzUi8h3dn21OKx8m9wqmohRrAYU5USMP+XbZ/N4H9e+juht79nsGl2vlnwU67M+REJSXMZmnob3xv9PIXJ3ZM+74uc6YrBr9FCllxEikSkoXtQCKwTkc2EvvRNtQgfBO4Xkb2ExgheOkNbeg0Zrny+kvMw4MRUghMfOfYKxrsOc0XqZvKchzGCoV/vFz/6lKue/zVp/Cffnng/6czilQ+vIWCcaLP+xISLyLCZuLTWX6agAq82kxzP6Wm/t9Zvp84feVPT+uI2Fs2E0UXnWwWPMzv7FoYmjCQ/aTzX5dzDBelf5aQ/sky5z7+Fet+HqCbTb3a7iSelhoPFr/Lntza0e89oo4vOWM905uTcxeXZN5HqaC3g2p+JpxbhfkJdiT7JcPc0LspYwB9LFpHn3o0trGbslCAFCYdQwdVU149m4dvv88zVyyhIL8MZ3uk3fcg6dh+6nLF5H0cc9BI9G3Fdh1n3h2atABVubQzP/Gm37a41qvm0YhUltXvIduUybeBsBthTuSDjKi7IuIpd1bv59b4XqTa8KGUyNHEo9464q5mSr9+/iUg/9q6EALnDD7Hs92v5yrX9J69/b8faOxBDpg2cxnj3qcYA0IBNTIqrlvK7dZ8yLquE/IHHGwMAgF030amkpm5lm3VrnsfxJX4fn2lHqVB3o9RIx5eyBJe9eb97ztRxJDha938FmJp3elKm0n+MZ3fdy6HKxVyiv8J55qOUH72M8uo/A1Dhr2DhrkWU+yvwm34CyqC45iBP7PgFpjo9caTrQ5AIHy2/T+d4WTKnTrYWZbWIH9ay4RiiiUaqzaA+kg4JQsCsZkT6MTRpPfPqsvvx+TbiTrwuYt0iWkgSzHMPplkDKkiOHnmU77KxBVw+toBV2/fiN4LYbaFpw2duvhpHEzmulUdepsBWwpzkEhzhwDVM8xLw/hvKkcma46E9CU0xMfEaNeyo2sk4T2igMMF1CZqegt9fi66fDoDBoMbafxYyanT/W5rbm7GCQIwZ4Cigvi5y//7mKefxwLIVBE2dljviDNOJvcXqt7bQtPa374oIT954JVsPHWXt3s8Z4HJyxcRRpCY1X7C0t3oj96WVNgaABuwSxKx+kuO+qzAibCVWKCr8pzMli9gYlPEW+w9+HdPcAQrKTwzgpV/NJuD38J3vtr82waJnsYJAjBmbdhf/OrKZYJMJFF1cjEy5lUx3KhPy5lLlW4XDFmhcW2AqcOgJuJMitwK6y4Sh2UwY2vavsEvTcWuRZwvE2MeY5DGsq9yAz2ye2stUJgXu5rsRbbYcRhX8nc+2b2X5sg/YvSPIyNHZLHjkYvKHd21grsJby6/eXst72/bitNu44fxJ3HbJFGwdCItYdA7pyemaM6WoqEitW7cu3mZ0mWO1n7Cl/Gmq/fvQNQ9D3fOYnP6NRhWfLYc3c6LiPnI9uxERXI6JpKctwmEf1UHN0eXt0iXMUD/HFSERCfowjIF/4eFtj1LuK29sETg0B+ekTOLuEbHJJVDr83PNU7/jRFUNhhmyy2W3cdGYPJ6948sxuWdvR0TWK6WKolWf1RLoATITp6FXPcZzO9+iPmgQVCVMSPktT025kYFONxOHTIIhqzHNWsBE03ogRVcELsu6iS0lKxhn24KjyfSjwoUkfReH5uCRsQ+zsvQvfFLxKQ7NwczMS5mZ+YWY2fTndTs4VVPXGAAA6gMG7+8sZn9ZOcOzBrZT2qIzWEGgB9hddZSHNr5OvXk6Q9Hmys+5+5MlLLv4HvxBgzVlezhR72XKwGGM9sQnCNg1B1OGLqP65BPovmVomIgkIu770BKvASDJlsgNQ+dyw9C5HdQWHdYfOExdoHUXRdeE7YfKrCAQBawg0AO8WrwWf4sBNUOZHKwpZ1XpZzyyYSU+08AwTUSES7NH8fS51zUTK+0pRDSSUx9GqQdBVYN4kBjLQ9SoAAAH/UlEQVQnDllduptfbvsbB70VZLgGcPeYGVyfNwURITc9BbuuEQi27qIMTutgzbNFp7BGVnqAI7UnMSOMvegIP9v0NpX+WmoMPz7ToD4YYM3R3bx5cFMcLD2NiB3R0mIeAD4o28f9n77OAW85Joqy+iqe3PoOr+z/BIC550/Erje3waYJWR43U/J79cbTswYrCEQR0zTxGeUEW4yeT08fjrPJZiHthEbC2y6039nxrfCjlzff6loXDLDswPoesTnePLN9FfXB5q2kumCAF3b8A1MpsjxuFt/5VfIyUnHYdOy6RlFBDr+96/q4y6P3FazuQJQ4VLkYd+0LuLV6AmgUM5W8rN+gawlcnzuNZcWfUOGvQR2GpBUJYIRTlZ1SpJY4OXmFn0D26SZvwIy4wqjbBE2TzXuOUF1Tz+RRQ/C4u5bUJFYU10RWYq4zAngDPpIdLiblDuJPD95OubcWh00nOcHVw1b2bawgEAVKq94ko34hjvDqOB2TIeY6Dh6dx/DB/0eyPYGlF9/Ny/v+yYo/bMVs8sMnCBjgXmun8tpQC8Kl27h6aMQtF91i/+Fyvvv0cmrqfIgIASPIndddyC1XRm2WqdsMS0pj56mjrY4n2Oy47af3TYgI6QNir2nQH7G6A1HAqP4PbC1yojg0xRDZTl0g9AFPcyZxf+EVqDY2B9oqQk3bRN3BiAGZ3FIQnb1Vpqn43sLlnKj0UlsfoKbOjz8QZPGbH7JxV0nHFcSYH4ydiUtvvq8hQbdz5+gZaFZzv0ewgkAUSKIikrAQphJq/afzBogI7qTITVlngp2b88/l51Pn8Nol32z1xegu2/aV4q31tUrb5PMbLH+vdY7+nmZG9kiemnotQ5NCugXpziQeGDeLO0acF2fL+g9WdyAKVDGYZNValkwEBrjGNTs2d04Rr73+EfW+030Cp9PGLXPP47bJ3ck92D4NXYCWKOBUTcRcMD3OrCGFzBpSiFLKGuyLA1ZLIAq4UxZgtPhX+pVGiczAoTfPz3/rDefxpVkTcdh1EhMcOBw2rpo9kXlzY/PLN2HkYAwjwi5Fh43LinpXjj0rAMQHa+9AlDjmfQf/yZ8xUD9OnengpP0a8tIfQ9Mix1lvjY+yY6fIyvTgTmo7W240eOO9zTy3dA2+QBClFC6njfxBA1m84EacDqsxeLYR7b0DVhDoJ2zfX8ry97ZwylvHpVNH8MXzxuCwWwHgbMTaQGTRLcYNH8S44YPibYZFL6TTYwIioovIRhFZEX79vohsCj+OiMhb7ZRNFpHDIvJCk2P/EJFdTeqwsj9aWMSBrrQEGrQIkyGkRdhwQkSW037K8J8CayIcnxdOTGphYREnOtUSaKJF2EppqIkWYcSWgIhMBbKAv3XfTAsLi1gRUy1CEdGAhcCP2qj35XBX4CdizQ9ZWMSFDoNAUy3CNi65iRYCJE24G/iLUupQhHPzlFITgIvDj1sjXIOIzA8Lmq47fvx4R+ZaWFh0kc60BBq0CIuBpcBMEXkFoIkWYVsJ8s8H7g2XfRq4TUSeBFBKHQ7/rQZepQ0hEqXUYqVUkVKqKCOjZ3XsLCz6Ax0ODCqlHgIeAgirEj/QWS1CpdS8hucicgdQpJT6sYjYgBSl1AkJ6UJfDaw6E0csLCy6R6y1CNvCCbwjIluATcBh4MUztMXCwqIbWCsGLSzOMqK9YtDaQGRh0c+xgoCFRT/HCgIWFv0cKwhYWPRzzqqBQRE5DhyMYpXpQBtZ//oEln9nP5F8zFVKRW3RzFkVBKKNiKyL5ihrb8Py7+ynJ3y0ugMWFv0cKwhYWPRz+nsQWBxvA2KM5d/ZT8x97NdjAhYWFlZLwMKi39Mng4CIXC8i20XEFJGiJsdnich6Edka/jszfDxRRFaKyM5wuSc7qH+YiHhF5IFY+9LG/WPin4hMa5LzcbOIXNtTPrWwI1b+RSzf08TQv4Eisjr82Xwh0jURUUr1uQdQCIwG/kFo+3LD8XOAweHn44HD4eeJwBfCzx3A+8CV7dS/HHid0LbqPuNf+Dpb+Pkg4FjD6z7iX8Tyfci/JOAi4E7ghc7a0ydTjiuldkBrRRul1MYmL7cDLhFxKqVqgdXha/wisgHIiVS3iMwB9gM1MTC9U8TKv/B1DbiglYRhjxBD/9oq74uyC+0SQ/9qgH+JyIiu2NMnuwOd5KvAxpYfABFJAb4MvNuygIgkAQ8Cj/WIhWdGl/0Ln58uItuBrcCdSikj0nW9gG7511H5XsSZ+tdpztqWgIisArIjnPp3pVR76c8RkXHAL4DZLY7bCCVJeU4ptT9C0ceAZ5VS3ljnRY2TfyilPgbGiUghsERE/qrayBx1JsTLv/bKR5N4+tdVztogoJS6vDvlJJQ+/U3gNqXUvhanFwN7lFKL2ig+HZgrIk8BKYApIvVKqc4PwnSSOPnX9P47RKSGUN806plc4uVfB+WjRrzfv65w1gaB7hBuSq0EHlJKfdDi3M8AD/Cttsqr5oIrjwLeWASA7nKm/olIPnBIKWWISC6hwavi2FncNaLgX5vlewNn6l+3icfoaKwfhLQQSgAfUAa8Ez7+MKEBvU1NHpmEBlkUIYWlhuPfCpf5CvB4hHs8SvxmB2LiH6G079vD5zcAc/qYfxHL9xX/wq+LgQrAG77H2I7ssVYMWlj0c/rz7ICFhQVWELCw6PdYQcDCop9jBQELi36OFQQsLPo5VhCwsOjnWEHAwqKfYwUBC4t+zv8DBJPf5N2ijiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Geopandas also has some convenient built-in plotting methods\n",
    "# quick scatter plot colored by 'rating'\n",
    "gf.plot(c=gf.rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometric operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-122.424878932 47.442169968 0.32405186399999764 0.3614228639999979\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,95.2457628)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.007228457279999958\" opacity=\"0.6\" d=\"M -122.254492,47.455556 L -122.3616457,47.4669435 L -122.39311699999999,47.530419 L -122.4114929,47.5787474 L -122.4066885,47.6743627 L -122.40396090000002,47.691771 L -122.36419850000001,47.7902068 L -122.2361385,47.7572161 L -122.12825900000001,47.641193 L -122.1142131,47.557971 L -122.11659909999999,47.55525 L -122.16778000000001,47.509529 L -122.17799540000001,47.5014079 L -122.2062982,47.4790861 L -122.254492,47.455556 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x128033f98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We also now have access to lots of quantifiable spatial information. For example, how large\n",
    "# is the area containing all these points?\n",
    "#http://geopandas.org/geometric_manipulations.html\n",
    "point_collection = shapely.geometry.MultiPoint(gf.geometry.tolist())\n",
    "polygon = point_collection.convex_hull\n",
    "polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-122.424878932 47.442169968 0.32405186399999764 0.3614228639999979\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,95.2457628)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.007228457279999958\" points=\"-122.254492,47.455556 -122.3616457,47.4669435 -122.39311699999999,47.530419 -122.4114929,47.5787474 -122.4066885,47.6743627 -122.40396090000002,47.691771 -122.36419850000001,47.7902068 -122.2361385,47.7572161 -122.12825900000001,47.641193 -122.1142131,47.557971 -122.11659909999999,47.55525 -122.16778000000001,47.509529 -122.17799540000001,47.5014079 -122.2062982,47.4790861 -122.254492,47.455556\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x127fe9588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perimeter = polygon.boundary\n",
    "perimeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-122.254492 47.455556, -122.3616457 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  POLYGON ((-122.254492 47.455556, -122.3616457 ..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can save these geometries in a separte geodataframe as you go:\n",
    "gfShape = gpd.GeoDataFrame(geometry=[polygon], crs = {'init': 'epsg:4326'})\n",
    "gfShape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other free geospatial tools\n",
    "\n",
    "Geographic Information Systems (GIS) are designed for working with geospatial data. If geopandas is lacking, consider using [QGIS](https://qgis.org/en/site/). Also, [Google Earth Pro](https://www.google.com/earth/desktop/) is now free and is a great visualization tool. Finally, [GDAL/OGR](http://www.gdal.org) is a powerful library with command line tools that many other software packages are based upon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you want to share the geometry / export it to a GIS program for further analysis:\n",
    "# Default is ESRI shapefile, but Geojson or Geopackage, etc should work to\n",
    "#http://geopandas.org/io.html\n",
    "#http://www.geopackage.org\n",
    "\n",
    "#gfShape.to_file('./data/myshape.shp') #ESRI shapefile is default\n",
    "gfShape.to_file('./data/myshape.gpkg', driver='GPKG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance, area, etc.\n",
    " \n",
    "We commonly have points in latitude and longitude, but want to know distances on the ground in meters. These conversions are non-trivial and require some knowledge of different coordinate systems and map projections. Be carefule when making these calculations!\n",
    "https://support.esri.com/en/technical-article/000011356"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We digress... what about that question about area?\n",
    "# Step 1) convert to a local coordinate system in metric units of distance!\n",
    "\n",
    "# Google Mercator (EPSG:3857 - https://epsg.io/3857) is another popular \n",
    "# projection for web maps, but it is \"direction preserving\", not \"area preserving!\"\n",
    "\n",
    "# A good choice for local distances is the Universal Transverse Mercator\n",
    "# projection. For Seattle it's EPSG:32610\n",
    "# http://spatialreference.org/ref/epsg/wgs-84-utm-zone-10n/\n",
    "\n",
    "\n",
    "gfShape.to_crs({'init': 'epsg:32610'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Points are within an area of 600.0 km^2\n"
     ]
    }
   ],
   "source": [
    "# will be in units of km^2\n",
    "area = gfShape.area.values[0] * 1e-6\n",
    "print(f'Points are within an area of {area:.1f} km^2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise:\n",
    "# Calculate the length of the perimeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfD = gpd.pd.read_csv('./data/Dist_Out.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>distance.value</th>\n",
       "      <th>duration.value</th>\n",
       "      <th>duration_in_traffic.value</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>fare.value</th>\n",
       "      <th>mode</th>\n",
       "      <th>origin</th>\n",
       "      <th>pair</th>\n",
       "      <th>place_id</th>\n",
       "      <th>rank</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lon</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>1.475451</td>\n",
       "      <td>32.183333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cafe</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>0.430702</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pharmacy</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>0.494717</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.719527</td>\n",
       "      <td>-122.295845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJ8fdfu2QRkFQRvoCQ1-nxHo0</td>\n",
       "      <td>ChIJ8fdfu2QRkFQRvoCQ1-nxHo0</td>\n",
       "      <td>2</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pharmacy</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>0.167185</td>\n",
       "      <td>3.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.723967</td>\n",
       "      <td>-122.292140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJi_xMRXoRkFQRhfEsI71aRq8</td>\n",
       "      <td>ChIJi_xMRXoRkFQRhfEsI71aRq8</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pharmacy</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>0.812306</td>\n",
       "      <td>16.483333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.733500</td>\n",
       "      <td>-122.291991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJKXaRMp4RkFQRr9i6RGtJ8Uo</td>\n",
       "      <td>ChIJKXaRMp4RkFQRr9i6RGtJ8Uo</td>\n",
       "      <td>3</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      class       departure_time  distance.value  duration.value  \\\n",
       "0  hospital  2018-06-06 12:41:26        1.475451       32.183333   \n",
       "1      cafe  2018-06-06 12:41:26        0.430702        9.133333   \n",
       "2  pharmacy  2018-06-06 12:41:26        0.494717       10.200000   \n",
       "3  pharmacy  2018-06-06 12:41:26        0.167185        3.416667   \n",
       "4  pharmacy  2018-06-06 12:41:26        0.812306       16.483333   \n",
       "\n",
       "   duration_in_traffic.value    end_lat     end_lon  fare.value     mode  \\\n",
       "0                        NaN  47.716034 -122.312230         NaN  walking   \n",
       "1                        NaN  47.719859 -122.295224         NaN  walking   \n",
       "2                        NaN  47.719527 -122.295845         NaN  walking   \n",
       "3                        NaN  47.723967 -122.292140         NaN  walking   \n",
       "4                        NaN  47.733500 -122.291991         NaN  walking   \n",
       "\n",
       "         origin                                      pair  \\\n",
       "0  530330001003  530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "1  530330001003  530330001003-ChIJ17BqS3sRkFQR31AY2_7b1Vg   \n",
       "2  530330001003  530330001003-ChIJ8fdfu2QRkFQRvoCQ1-nxHo0   \n",
       "3  530330001003  530330001003-ChIJi_xMRXoRkFQRhfEsI71aRq8   \n",
       "4  530330001003  530330001003-ChIJKXaRMp4RkFQRr9i6RGtJ8Uo   \n",
       "\n",
       "                      place_id  rank  start_lat  start_lon status  \n",
       "0  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72281 -122.28945     OK  \n",
       "1  ChIJ17BqS3sRkFQR31AY2_7b1Vg     1   47.72281 -122.28945     OK  \n",
       "2  ChIJ8fdfu2QRkFQRvoCQ1-nxHo0     2   47.72281 -122.28945     OK  \n",
       "3  ChIJi_xMRXoRkFQRhfEsI71aRq8     1   47.72281 -122.28945     OK  \n",
       "4  ChIJKXaRMp4RkFQRr9i6RGtJ8Uo     3   47.72281 -122.28945     OK  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 16 columns):\n",
      "class                        15000 non-null object\n",
      "departure_time               15000 non-null object\n",
      "distance.value               15000 non-null float64\n",
      "duration.value               15000 non-null float64\n",
      "duration_in_traffic.value    0 non-null float64\n",
      "end_lat                      15000 non-null float64\n",
      "end_lon                      15000 non-null float64\n",
      "fare.value                   2891 non-null float64\n",
      "mode                         15000 non-null object\n",
      "origin                       15000 non-null int64\n",
      "pair                         15000 non-null object\n",
      "place_id                     15000 non-null object\n",
      "rank                         15000 non-null int64\n",
      "start_lat                    15000 non-null float64\n",
      "start_lon                    15000 non-null float64\n",
      "status                       15000 non-null object\n",
      "dtypes: float64(8), int64(2), object(6)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "dfD.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                                                        hospital\n",
       "departure_time                                    2018-06-06 12:41:26\n",
       "distance.value                                                1.47545\n",
       "duration.value                                                32.1833\n",
       "duration_in_traffic.value                                         NaN\n",
       "end_lat                                                        47.716\n",
       "end_lon                                                      -122.312\n",
       "fare.value                                                        NaN\n",
       "mode                                                          walking\n",
       "origin                                                   530330001003\n",
       "pair                         530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA\n",
       "place_id                                  ChIJEerXpl0RkFQRDOjfwBQDzlA\n",
       "rank                                                                1\n",
       "start_lat                                                     47.7228\n",
       "start_lon                                                    -122.289\n",
       "status                                                             OK\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexing is the same as with pandas:\n",
    "dfD.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>place_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>place_class</th>\n",
       "      <th>type</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name                              address     city  \\\n",
       "313  Elevated Natural Health  12000 15th Avenue Northeast Suite C  Seattle   \n",
       "\n",
       "           lat        lng                     place_id  rating place_class  \\\n",
       "313  47.716034 -122.31223  ChIJEerXpl0RkFQRDOjfwBQDzlA     5.0    hospital   \n",
       "\n",
       "    type                         geometry  \n",
       "313  NaN  POINT (-122.3122296 47.7160344)  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'distance.value in the table is route distance, what about straight-line distance?\n",
    "# we already have destination (place_id) lon,lat points in the earlier geodataframe 'gf'\n",
    "row = 0\n",
    "dest = dfD.place_id.iloc[row]\n",
    "\n",
    "gf.query('place_id == @dest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So let's use the start_lat, start_lon for the points in this second geodataframe\n",
    "geometries = [shapely.geometry.Point(xy) for xy in zip(dfD.start_lon, dfD.start_lat)]\n",
    "crs = {'init': 'epsg:4326'}\n",
    "gfD = gpd.GeoDataFrame(dfD, crs=crs, geometry=geometries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                                                        hospital\n",
       "departure_time                                    2018-06-06 12:41:26\n",
       "distance.value                                                1.47545\n",
       "duration.value                                                32.1833\n",
       "duration_in_traffic.value                                         NaN\n",
       "end_lat                                                        47.716\n",
       "end_lon                                                      -122.312\n",
       "fare.value                                                        NaN\n",
       "mode                                                          walking\n",
       "origin                                                   530330001003\n",
       "pair                         530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA\n",
       "place_id                                  ChIJEerXpl0RkFQRDOjfwBQDzlA\n",
       "rank                                                                1\n",
       "start_lat                                                     47.7228\n",
       "start_lon                                                    -122.289\n",
       "status                                                             OK\n",
       "geometry                                  POINT (-122.28945 47.72281)\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfD.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To get distance let's convert once again to UTM\n",
    "# Or UTM EPSG:32610\n",
    "gf_merc = gf.to_crs({'init': 'epsg:32610'})\n",
    "gfD_merc = gfD.to_crs({'init': 'epsg:32610'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gf_merc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (551585.3758357617 5284968.473739896)\n",
      "<shapely.coords.CoordinateSequence object at 0x12bb4cb38>\n"
     ]
    }
   ],
   "source": [
    "# Let's do a sanity check for distance for this first pair:\n",
    "pointDest = gf_merc.query('place_id == @dest').geometry.values[0]\n",
    "print(pointDest)\n",
    "print(pointDest.coords)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"551584.3758357617 5284967.473739896 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10569936.947479792)\"><circle cx=\"551585.3758357617\" cy=\"5284968.473739896\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.point.Point at 0x12afff2b0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointDest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array('d', [553287.0140557075]), array('d', [5285736.936265724]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointOrig = gfD_merc.geometry.iloc[0]\n",
    "pointOrig.coords.xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance in meters: 1867.1\n",
      "Distance in miles: 1.2\n"
     ]
    }
   ],
   "source": [
    "dist_m = pointDest.distance(pointOrig)\n",
    "print(f'Distance in meters: {dist_m:.1f}')\n",
    "dist_mi = dist_m/1609.34\n",
    "print(f'Distance in miles: {dist_mi:.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# measuring the straight line distance in google earth i get (1.87 km, 1.16 miles)\n",
    "# Does this make sense? if this is a walking trip that the reported distances would be a bit larger!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gdp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-09d3f775b0b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mgeometries\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpointOrig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpointDest\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mGF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgdp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeometry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgeometries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'gdp' is not defined"
     ]
    }
   ],
   "source": [
    "geometries =[pointOrig,pointDest]\n",
    "GF = gdp.GeoDataFrame(dist_m, crs=crs, geometry=geometries)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excercise: save the Start Point, End Point, and Straight Line between\n",
    "# them to view in Google Earth\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>distance.value</th>\n",
       "      <th>duration.value</th>\n",
       "      <th>duration_in_traffic.value</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>fare.value</th>\n",
       "      <th>mode</th>\n",
       "      <th>origin</th>\n",
       "      <th>pair</th>\n",
       "      <th>place_id</th>\n",
       "      <th>rank</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lon</th>\n",
       "      <th>status</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>1.475451</td>\n",
       "      <td>32.183333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (553287.0140557075 5285736.936265724)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>1.574891</td>\n",
       "      <td>13.633333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bicycling</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (553287.0140557075 5285736.936265724)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>1.574891</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>driving</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (553287.0140557075 5285736.936265724)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>1.563083</td>\n",
       "      <td>12.583333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>2.5</td>\n",
       "      <td>transit</td>\n",
       "      <td>530330001003</td>\n",
       "      <td>530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72281</td>\n",
       "      <td>-122.28945</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (553287.0140557075 5285736.936265724)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.295214</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330001005</td>\n",
       "      <td>530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72293</td>\n",
       "      <td>-122.29444</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552912.6740573557 5285746.85115021)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.336234</td>\n",
       "      <td>11.583333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bicycling</td>\n",
       "      <td>530330001005</td>\n",
       "      <td>530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72293</td>\n",
       "      <td>-122.29444</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552912.6740573557 5285746.85115021)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.333748</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>driving</td>\n",
       "      <td>530330001005</td>\n",
       "      <td>530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72293</td>\n",
       "      <td>-122.29444</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552912.6740573557 5285746.85115021)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.416408</td>\n",
       "      <td>10.983333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>2.5</td>\n",
       "      <td>transit</td>\n",
       "      <td>530330001005</td>\n",
       "      <td>530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72293</td>\n",
       "      <td>-122.29444</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552912.6740573557 5285746.85115021)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:31</td>\n",
       "      <td>1.564947</td>\n",
       "      <td>31.016667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330006001</td>\n",
       "      <td>530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72512</td>\n",
       "      <td>-122.33652</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (549754.8601332628 5285962.350292516)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:31</td>\n",
       "      <td>1.565569</td>\n",
       "      <td>10.116667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bicycling</td>\n",
       "      <td>530330006001</td>\n",
       "      <td>530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72512</td>\n",
       "      <td>-122.33652</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (549754.8601332628 5285962.350292516)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>1.886265</td>\n",
       "      <td>7.066667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>driving</td>\n",
       "      <td>530330006001</td>\n",
       "      <td>530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72512</td>\n",
       "      <td>-122.33652</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (549754.8601332628 5285962.350292516)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>5.047856</td>\n",
       "      <td>32.116667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>2.5</td>\n",
       "      <td>transit</td>\n",
       "      <td>530330006001</td>\n",
       "      <td>530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.72512</td>\n",
       "      <td>-122.33652</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (549754.8601332628 5285962.350292516)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>1.106899</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walking</td>\n",
       "      <td>530330007001</td>\n",
       "      <td>530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.71744</td>\n",
       "      <td>-122.29460</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552906.2332471436 5285136.585338849)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>1.268490</td>\n",
       "      <td>12.850000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bicycling</td>\n",
       "      <td>530330007001</td>\n",
       "      <td>530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.71744</td>\n",
       "      <td>-122.29460</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552906.2332471436 5285136.585338849)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>1.207582</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>driving</td>\n",
       "      <td>530330007001</td>\n",
       "      <td>530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.71744</td>\n",
       "      <td>-122.29460</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552906.2332471436 5285136.585338849)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>hospital</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>1.266004</td>\n",
       "      <td>13.283333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.31223</td>\n",
       "      <td>2.5</td>\n",
       "      <td>transit</td>\n",
       "      <td>530330007001</td>\n",
       "      <td>530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>1</td>\n",
       "      <td>47.71744</td>\n",
       "      <td>-122.29460</td>\n",
       "      <td>OK</td>\n",
       "      <td>POINT (552906.2332471436 5285136.585338849)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        class       departure_time  distance.value  duration.value  \\\n",
       "0    hospital  2018-06-06 12:41:26        1.475451       32.183333   \n",
       "25   hospital  2018-06-06 12:41:27        1.574891       13.633333   \n",
       "50   hospital  2018-06-06 12:41:27        1.574891        6.700000   \n",
       "75   hospital  2018-06-06 12:41:27        1.563083       12.583333   \n",
       "100  hospital  2018-06-06 12:41:28        1.295214       27.900000   \n",
       "125  hospital  2018-06-06 12:41:28        1.336234       11.583333   \n",
       "150  hospital  2018-06-06 12:41:28        1.333748        5.316667   \n",
       "175  hospital  2018-06-06 12:41:28        1.416408       10.983333   \n",
       "400  hospital  2018-06-06 12:41:31        1.564947       31.016667   \n",
       "425  hospital  2018-06-06 12:41:31        1.565569       10.116667   \n",
       "450  hospital  2018-06-06 12:41:32        1.886265        7.066667   \n",
       "475  hospital  2018-06-06 12:41:32        5.047856       32.116667   \n",
       "500  hospital  2018-06-06 12:41:32        1.106899       25.150000   \n",
       "525  hospital  2018-06-06 12:41:33        1.268490       12.850000   \n",
       "550  hospital  2018-06-06 12:41:33        1.207582        5.450000   \n",
       "575  hospital  2018-06-06 12:41:33        1.266004       13.283333   \n",
       "\n",
       "     duration_in_traffic.value    end_lat    end_lon  fare.value       mode  \\\n",
       "0                          NaN  47.716034 -122.31223         NaN    walking   \n",
       "25                         NaN  47.716034 -122.31223         NaN  bicycling   \n",
       "50                         NaN  47.716034 -122.31223         NaN    driving   \n",
       "75                         NaN  47.716034 -122.31223         2.5    transit   \n",
       "100                        NaN  47.716034 -122.31223         NaN    walking   \n",
       "125                        NaN  47.716034 -122.31223         NaN  bicycling   \n",
       "150                        NaN  47.716034 -122.31223         NaN    driving   \n",
       "175                        NaN  47.716034 -122.31223         2.5    transit   \n",
       "400                        NaN  47.716034 -122.31223         NaN    walking   \n",
       "425                        NaN  47.716034 -122.31223         NaN  bicycling   \n",
       "450                        NaN  47.716034 -122.31223         NaN    driving   \n",
       "475                        NaN  47.716034 -122.31223         2.5    transit   \n",
       "500                        NaN  47.716034 -122.31223         NaN    walking   \n",
       "525                        NaN  47.716034 -122.31223         NaN  bicycling   \n",
       "550                        NaN  47.716034 -122.31223         NaN    driving   \n",
       "575                        NaN  47.716034 -122.31223         2.5    transit   \n",
       "\n",
       "           origin                                      pair  \\\n",
       "0    530330001003  530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "25   530330001003  530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "50   530330001003  530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "75   530330001003  530330001003-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "100  530330001005  530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "125  530330001005  530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "150  530330001005  530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "175  530330001005  530330001005-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "400  530330006001  530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "425  530330006001  530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "450  530330006001  530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "475  530330006001  530330006001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "500  530330007001  530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "525  530330007001  530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "550  530330007001  530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "575  530330007001  530330007001-ChIJEerXpl0RkFQRDOjfwBQDzlA   \n",
       "\n",
       "                        place_id  rank  start_lat  start_lon status  \\\n",
       "0    ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72281 -122.28945     OK   \n",
       "25   ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72281 -122.28945     OK   \n",
       "50   ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72281 -122.28945     OK   \n",
       "75   ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72281 -122.28945     OK   \n",
       "100  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72293 -122.29444     OK   \n",
       "125  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72293 -122.29444     OK   \n",
       "150  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72293 -122.29444     OK   \n",
       "175  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72293 -122.29444     OK   \n",
       "400  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72512 -122.33652     OK   \n",
       "425  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72512 -122.33652     OK   \n",
       "450  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72512 -122.33652     OK   \n",
       "475  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.72512 -122.33652     OK   \n",
       "500  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.71744 -122.29460     OK   \n",
       "525  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.71744 -122.29460     OK   \n",
       "550  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.71744 -122.29460     OK   \n",
       "575  ChIJEerXpl0RkFQRDOjfwBQDzlA     1   47.71744 -122.29460     OK   \n",
       "\n",
       "                                        geometry  \n",
       "0    POINT (553287.0140557075 5285736.936265724)  \n",
       "25   POINT (553287.0140557075 5285736.936265724)  \n",
       "50   POINT (553287.0140557075 5285736.936265724)  \n",
       "75   POINT (553287.0140557075 5285736.936265724)  \n",
       "100   POINT (552912.6740573557 5285746.85115021)  \n",
       "125   POINT (552912.6740573557 5285746.85115021)  \n",
       "150   POINT (552912.6740573557 5285746.85115021)  \n",
       "175   POINT (552912.6740573557 5285746.85115021)  \n",
       "400  POINT (549754.8601332628 5285962.350292516)  \n",
       "425  POINT (549754.8601332628 5285962.350292516)  \n",
       "450  POINT (549754.8601332628 5285962.350292516)  \n",
       "475  POINT (549754.8601332628 5285962.350292516)  \n",
       "500  POINT (552906.2332471436 5285136.585338849)  \n",
       "525  POINT (552906.2332471436 5285136.585338849)  \n",
       "550  POINT (552906.2332471436 5285136.585338849)  \n",
       "575  POINT (552906.2332471436 5285136.585338849)  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# That was kind of complicated for one point, but it's now easy to get all the \n",
    "# straight-line distances between origins and a particular destination:\n",
    "pId = 'ChIJEerXpl0RkFQRDOjfwBQDzlA'\n",
    "pointDest = gf_merc.query('place_id == @pId').geometry.values[0]\n",
    "gfDest = gfD_merc.query('place_id == @pId')\n",
    "gfDest\n",
    "# NOTE: lots of the same origin-id. Is that the same person?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/valentina/anaconda/envs/dssg2018/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/Users/valentina/anaconda/envs/dssg2018/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance.value</th>\n",
       "      <th>straightDist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.475451</td>\n",
       "      <td>1.160173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.574891</td>\n",
       "      <td>1.160173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1.574891</td>\n",
       "      <td>1.160173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1.563083</td>\n",
       "      <td>1.160173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.295214</td>\n",
       "      <td>0.956105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.336234</td>\n",
       "      <td>0.956105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1.333748</td>\n",
       "      <td>0.956105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1.416408</td>\n",
       "      <td>0.956105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1.564947</td>\n",
       "      <td>1.294273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>1.565569</td>\n",
       "      <td>1.294273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1.886265</td>\n",
       "      <td>1.294273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>5.047856</td>\n",
       "      <td>1.294273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>1.106899</td>\n",
       "      <td>0.827366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>1.268490</td>\n",
       "      <td>0.827366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>1.207582</td>\n",
       "      <td>0.827366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>1.266004</td>\n",
       "      <td>0.827366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance.value  straightDist\n",
       "0          1.475451      1.160173\n",
       "25         1.574891      1.160173\n",
       "50         1.574891      1.160173\n",
       "75         1.563083      1.160173\n",
       "100        1.295214      0.956105\n",
       "125        1.336234      0.956105\n",
       "150        1.333748      0.956105\n",
       "175        1.416408      0.956105\n",
       "400        1.564947      1.294273\n",
       "425        1.565569      1.294273\n",
       "450        1.886265      1.294273\n",
       "475        5.047856      1.294273\n",
       "500        1.106899      0.827366\n",
       "525        1.268490      0.827366\n",
       "550        1.207582      0.827366\n",
       "575        1.266004      0.827366"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfDest.loc[:,'straightDist'] = gfDest.distance(pointDest).values/1609.34\n",
    "gfDest.loc[:, ['distance.value','straightDist']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place_id</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>distance.value</th>\n",
       "      <th>duration.value</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>rating</th>\n",
       "      <th>place_class</th>\n",
       "      <th>type</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>1.475451</td>\n",
       "      <td>32.183333</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>1.574891</td>\n",
       "      <td>13.633333</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>1.574891</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>1.563083</td>\n",
       "      <td>12.583333</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.295214</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.336234</td>\n",
       "      <td>11.583333</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.333748</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>1.416408</td>\n",
       "      <td>10.983333</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:31</td>\n",
       "      <td>1.564947</td>\n",
       "      <td>31.016667</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:31</td>\n",
       "      <td>1.565569</td>\n",
       "      <td>10.116667</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>1.886265</td>\n",
       "      <td>7.066667</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>5.047856</td>\n",
       "      <td>32.116667</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>1.106899</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>1.268490</td>\n",
       "      <td>12.850000</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>1.207582</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ChIJEerXpl0RkFQRDOjfwBQDzlA</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>1.266004</td>\n",
       "      <td>13.283333</td>\n",
       "      <td>Elevated Natural Health</td>\n",
       "      <td>12000 15th Avenue Northeast Suite C</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.716034</td>\n",
       "      <td>-122.312230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3122296 47.7160344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>0.430702</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>0.430702</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>0.430702</td>\n",
       "      <td>3.066667</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>0.561840</td>\n",
       "      <td>5.283333</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>0.277812</td>\n",
       "      <td>5.366667</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>0.333126</td>\n",
       "      <td>2.266667</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>0.285270</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:28</td>\n",
       "      <td>0.414543</td>\n",
       "      <td>3.683333</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:32</td>\n",
       "      <td>0.210068</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>0.352393</td>\n",
       "      <td>3.816667</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>0.530764</td>\n",
       "      <td>4.466667</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ChIJ17BqS3sRkFQR31AY2_7b1Vg</td>\n",
       "      <td>2018-06-06 12:41:33</td>\n",
       "      <td>0.210068</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>Kaffeeklatsch Seattle</td>\n",
       "      <td>12513 Lake City Way NE #H</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719859</td>\n",
       "      <td>-122.295224</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295224 47.719859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ChIJ8fdfu2QRkFQRvoCQ1-nxHo0</td>\n",
       "      <td>2018-06-06 12:41:26</td>\n",
       "      <td>0.494717</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>Bartell Drugs Lake City</td>\n",
       "      <td>3018 NE 125th St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719527</td>\n",
       "      <td>-122.295845</td>\n",
       "      <td>3.6</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295845 47.71952720000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ChIJ8fdfu2QRkFQRvoCQ1-nxHo0</td>\n",
       "      <td>2018-06-06 12:41:27</td>\n",
       "      <td>0.549410</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>Bartell Drugs Lake City</td>\n",
       "      <td>3018 NE 125th St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.719527</td>\n",
       "      <td>-122.295845</td>\n",
       "      <td>3.6</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.295845 47.71952720000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13174</th>\n",
       "      <td>ChIJa8-7uDRCkFQR7RchkpotIaQ</td>\n",
       "      <td>2018-06-06 12:44:56</td>\n",
       "      <td>0.586078</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>Rockin Robins Espresso</td>\n",
       "      <td>1520 Southwest 100th Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.514026</td>\n",
       "      <td>-122.354502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3545019 47.5140261)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13175</th>\n",
       "      <td>ChIJa8-7uDRCkFQR7RchkpotIaQ</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.681790</td>\n",
       "      <td>7.466667</td>\n",
       "      <td>Rockin Robins Espresso</td>\n",
       "      <td>1520 Southwest 100th Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.514026</td>\n",
       "      <td>-122.354502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3545019 47.5140261)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13176</th>\n",
       "      <td>ChIJr0ps12FBkFQRpD7qXcDkd8I</td>\n",
       "      <td>2018-06-06 12:44:56</td>\n",
       "      <td>0.124301</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>The People's Choice Marketplace</td>\n",
       "      <td>9416 Delridge Way Southwest</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.518879</td>\n",
       "      <td>-122.356506</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3565064 47.5188795)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13177</th>\n",
       "      <td>ChIJr0ps12FBkFQRpD7qXcDkd8I</td>\n",
       "      <td>2018-06-06 12:44:56</td>\n",
       "      <td>0.124301</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>The People's Choice Marketplace</td>\n",
       "      <td>9416 Delridge Way Southwest</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.518879</td>\n",
       "      <td>-122.356506</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3565064 47.5188795)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13178</th>\n",
       "      <td>ChIJr0ps12FBkFQRpD7qXcDkd8I</td>\n",
       "      <td>2018-06-06 12:44:56</td>\n",
       "      <td>0.124301</td>\n",
       "      <td>1.216667</td>\n",
       "      <td>The People's Choice Marketplace</td>\n",
       "      <td>9416 Delridge Way Southwest</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.518879</td>\n",
       "      <td>-122.356506</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3565064 47.5188795)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13179</th>\n",
       "      <td>ChIJr0ps12FBkFQRpD7qXcDkd8I</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.124301</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>The People's Choice Marketplace</td>\n",
       "      <td>9416 Delridge Way Southwest</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.518879</td>\n",
       "      <td>-122.356506</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3565064 47.5188795)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13180</th>\n",
       "      <td>ChIJwbFCXAlCkFQRPgO1ZSixaHs</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.773773</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>Sunset Cafe</td>\n",
       "      <td>8115 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.529942</td>\n",
       "      <td>-122.270239</td>\n",
       "      <td>4.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2702394 47.5299419)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13181</th>\n",
       "      <td>ChIJwbFCXAlCkFQRPgO1ZSixaHs</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.776880</td>\n",
       "      <td>4.683333</td>\n",
       "      <td>Sunset Cafe</td>\n",
       "      <td>8115 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.529942</td>\n",
       "      <td>-122.270239</td>\n",
       "      <td>4.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2702394 47.5299419)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13182</th>\n",
       "      <td>ChIJwbFCXAlCkFQRPgO1ZSixaHs</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.776880</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>Sunset Cafe</td>\n",
       "      <td>8115 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.529942</td>\n",
       "      <td>-122.270239</td>\n",
       "      <td>4.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2702394 47.5299419)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13183</th>\n",
       "      <td>ChIJwbFCXAlCkFQRPgO1ZSixaHs</td>\n",
       "      <td>2018-06-06 12:44:58</td>\n",
       "      <td>1.596022</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>Sunset Cafe</td>\n",
       "      <td>8115 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.529942</td>\n",
       "      <td>-122.270239</td>\n",
       "      <td>4.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2702394 47.5299419)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13184</th>\n",
       "      <td>ChIJx0ob9m1CkFQR8GnhmgbZoLo</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.359851</td>\n",
       "      <td>7.233333</td>\n",
       "      <td>Safeway Pharmacy</td>\n",
       "      <td>9262 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.521045</td>\n",
       "      <td>-122.268030</td>\n",
       "      <td>2.8</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2680296 47.5210445)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13185</th>\n",
       "      <td>ChIJx0ob9m1CkFQR8GnhmgbZoLo</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.359851</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>Safeway Pharmacy</td>\n",
       "      <td>9262 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.521045</td>\n",
       "      <td>-122.268030</td>\n",
       "      <td>2.8</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2680296 47.5210445)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13186</th>\n",
       "      <td>ChIJx0ob9m1CkFQR8GnhmgbZoLo</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.831572</td>\n",
       "      <td>4.966667</td>\n",
       "      <td>Safeway Pharmacy</td>\n",
       "      <td>9262 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.521045</td>\n",
       "      <td>-122.268030</td>\n",
       "      <td>2.8</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2680296 47.5210445)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13187</th>\n",
       "      <td>ChIJx0ob9m1CkFQR8GnhmgbZoLo</td>\n",
       "      <td>2018-06-06 12:44:58</td>\n",
       "      <td>0.359851</td>\n",
       "      <td>7.233333</td>\n",
       "      <td>Safeway Pharmacy</td>\n",
       "      <td>9262 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.521045</td>\n",
       "      <td>-122.268030</td>\n",
       "      <td>2.8</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2680296 47.5210445)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13188</th>\n",
       "      <td>ChIJ9bFEiAxCkFQRYUBsIamJTnA</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.513362</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>Neighborcare Health at Rainier Beach (Medical ...</td>\n",
       "      <td>9245 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519612</td>\n",
       "      <td>-122.269239</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269239 47.519612)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13189</th>\n",
       "      <td>ChIJ9bFEiAxCkFQRYUBsIamJTnA</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.513984</td>\n",
       "      <td>3.883333</td>\n",
       "      <td>Neighborcare Health at Rainier Beach (Medical ...</td>\n",
       "      <td>9245 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519612</td>\n",
       "      <td>-122.269239</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269239 47.519612)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13190</th>\n",
       "      <td>ChIJ9bFEiAxCkFQRYUBsIamJTnA</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.571162</td>\n",
       "      <td>4.116667</td>\n",
       "      <td>Neighborcare Health at Rainier Beach (Medical ...</td>\n",
       "      <td>9245 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519612</td>\n",
       "      <td>-122.269239</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269239 47.519612)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13191</th>\n",
       "      <td>ChIJ9bFEiAxCkFQRYUBsIamJTnA</td>\n",
       "      <td>2018-06-06 12:44:58</td>\n",
       "      <td>0.579863</td>\n",
       "      <td>9.033333</td>\n",
       "      <td>Neighborcare Health at Rainier Beach (Medical ...</td>\n",
       "      <td>9245 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519612</td>\n",
       "      <td>-122.269239</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pharmacy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269239 47.519612)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13192</th>\n",
       "      <td>ChIJOfK1InJCkFQR69GMFxI4U8o</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.472343</td>\n",
       "      <td>9.516667</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>9304 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.520251</td>\n",
       "      <td>-122.266915</td>\n",
       "      <td>3.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2669155 47.5202511)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13193</th>\n",
       "      <td>ChIJOfK1InJCkFQR69GMFxI4U8o</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.755749</td>\n",
       "      <td>4.616667</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>9304 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.520251</td>\n",
       "      <td>-122.266915</td>\n",
       "      <td>3.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2669155 47.5202511)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13194</th>\n",
       "      <td>ChIJOfK1InJCkFQR69GMFxI4U8o</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.752641</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>9304 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.520251</td>\n",
       "      <td>-122.266915</td>\n",
       "      <td>3.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2669155 47.5202511)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>ChIJOfK1InJCkFQR69GMFxI4U8o</td>\n",
       "      <td>2018-06-06 12:44:58</td>\n",
       "      <td>0.472343</td>\n",
       "      <td>9.516667</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>9304 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.520251</td>\n",
       "      <td>-122.266915</td>\n",
       "      <td>3.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2669155 47.5202511)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13196</th>\n",
       "      <td>ChIJKU3pPXFCkFQRcF6MpFTDMkE</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.629584</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>Redwing Cafe</td>\n",
       "      <td>9272 57th Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519534</td>\n",
       "      <td>-122.261415</td>\n",
       "      <td>4.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2614145 47.5195336)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13197</th>\n",
       "      <td>ChIJKU3pPXFCkFQRcF6MpFTDMkE</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.622126</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>Redwing Cafe</td>\n",
       "      <td>9272 57th Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519534</td>\n",
       "      <td>-122.261415</td>\n",
       "      <td>4.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2614145 47.5195336)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13198</th>\n",
       "      <td>ChIJKU3pPXFCkFQRcF6MpFTDMkE</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.619018</td>\n",
       "      <td>3.933333</td>\n",
       "      <td>Redwing Cafe</td>\n",
       "      <td>9272 57th Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519534</td>\n",
       "      <td>-122.261415</td>\n",
       "      <td>4.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2614145 47.5195336)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13199</th>\n",
       "      <td>ChIJKU3pPXFCkFQRcF6MpFTDMkE</td>\n",
       "      <td>2018-06-06 12:44:58</td>\n",
       "      <td>0.697328</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>Redwing Cafe</td>\n",
       "      <td>9272 57th Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.519534</td>\n",
       "      <td>-122.261415</td>\n",
       "      <td>4.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.2614145 47.5195336)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13200</th>\n",
       "      <td>ChIJJ609fQxCkFQRq0MSabjnTeU</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.571162</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>Ethio Mini-Market</td>\n",
       "      <td>8432 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.527018</td>\n",
       "      <td>-122.269556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269556 47.527018)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13201</th>\n",
       "      <td>ChIJJ609fQxCkFQRq0MSabjnTeU</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.574270</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>Ethio Mini-Market</td>\n",
       "      <td>8432 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.527018</td>\n",
       "      <td>-122.269556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269556 47.527018)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13202</th>\n",
       "      <td>ChIJJ609fQxCkFQRq0MSabjnTeU</td>\n",
       "      <td>2018-06-06 12:44:57</td>\n",
       "      <td>0.574270</td>\n",
       "      <td>3.716667</td>\n",
       "      <td>Ethio Mini-Market</td>\n",
       "      <td>8432 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.527018</td>\n",
       "      <td>-122.269556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269556 47.527018)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13203</th>\n",
       "      <td>ChIJJ609fQxCkFQRq0MSabjnTeU</td>\n",
       "      <td>2018-06-06 12:44:58</td>\n",
       "      <td>0.571162</td>\n",
       "      <td>11.616667</td>\n",
       "      <td>Ethio Mini-Market</td>\n",
       "      <td>8432 Rainier Avenue South</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.527018</td>\n",
       "      <td>-122.269556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.269556 47.527018)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13204 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          place_id       departure_time  distance.value  \\\n",
       "0      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:26        1.475451   \n",
       "1      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:27        1.574891   \n",
       "2      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:27        1.574891   \n",
       "3      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:27        1.563083   \n",
       "4      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:28        1.295214   \n",
       "5      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:28        1.336234   \n",
       "6      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:28        1.333748   \n",
       "7      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:28        1.416408   \n",
       "8      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:31        1.564947   \n",
       "9      ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:31        1.565569   \n",
       "10     ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:32        1.886265   \n",
       "11     ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:32        5.047856   \n",
       "12     ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:32        1.106899   \n",
       "13     ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:33        1.268490   \n",
       "14     ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:33        1.207582   \n",
       "15     ChIJEerXpl0RkFQRDOjfwBQDzlA  2018-06-06 12:41:33        1.266004   \n",
       "16     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:26        0.430702   \n",
       "17     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:27        0.430702   \n",
       "18     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:27        0.430702   \n",
       "19     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:27        0.561840   \n",
       "20     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:28        0.277812   \n",
       "21     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:28        0.333126   \n",
       "22     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:28        0.285270   \n",
       "23     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:28        0.414543   \n",
       "24     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:32        0.210068   \n",
       "25     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:33        0.352393   \n",
       "26     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:33        0.530764   \n",
       "27     ChIJ17BqS3sRkFQR31AY2_7b1Vg  2018-06-06 12:41:33        0.210068   \n",
       "28     ChIJ8fdfu2QRkFQRvoCQ1-nxHo0  2018-06-06 12:41:26        0.494717   \n",
       "29     ChIJ8fdfu2QRkFQRvoCQ1-nxHo0  2018-06-06 12:41:27        0.549410   \n",
       "...                            ...                  ...             ...   \n",
       "13174  ChIJa8-7uDRCkFQR7RchkpotIaQ  2018-06-06 12:44:56        0.586078   \n",
       "13175  ChIJa8-7uDRCkFQR7RchkpotIaQ  2018-06-06 12:44:57        0.681790   \n",
       "13176  ChIJr0ps12FBkFQRpD7qXcDkd8I  2018-06-06 12:44:56        0.124301   \n",
       "13177  ChIJr0ps12FBkFQRpD7qXcDkd8I  2018-06-06 12:44:56        0.124301   \n",
       "13178  ChIJr0ps12FBkFQRpD7qXcDkd8I  2018-06-06 12:44:56        0.124301   \n",
       "13179  ChIJr0ps12FBkFQRpD7qXcDkd8I  2018-06-06 12:44:57        0.124301   \n",
       "13180  ChIJwbFCXAlCkFQRPgO1ZSixaHs  2018-06-06 12:44:57        0.773773   \n",
       "13181  ChIJwbFCXAlCkFQRPgO1ZSixaHs  2018-06-06 12:44:57        0.776880   \n",
       "13182  ChIJwbFCXAlCkFQRPgO1ZSixaHs  2018-06-06 12:44:57        0.776880   \n",
       "13183  ChIJwbFCXAlCkFQRPgO1ZSixaHs  2018-06-06 12:44:58        1.596022   \n",
       "13184  ChIJx0ob9m1CkFQR8GnhmgbZoLo  2018-06-06 12:44:57        0.359851   \n",
       "13185  ChIJx0ob9m1CkFQR8GnhmgbZoLo  2018-06-06 12:44:57        0.359851   \n",
       "13186  ChIJx0ob9m1CkFQR8GnhmgbZoLo  2018-06-06 12:44:57        0.831572   \n",
       "13187  ChIJx0ob9m1CkFQR8GnhmgbZoLo  2018-06-06 12:44:58        0.359851   \n",
       "13188  ChIJ9bFEiAxCkFQRYUBsIamJTnA  2018-06-06 12:44:57        0.513362   \n",
       "13189  ChIJ9bFEiAxCkFQRYUBsIamJTnA  2018-06-06 12:44:57        0.513984   \n",
       "13190  ChIJ9bFEiAxCkFQRYUBsIamJTnA  2018-06-06 12:44:57        0.571162   \n",
       "13191  ChIJ9bFEiAxCkFQRYUBsIamJTnA  2018-06-06 12:44:58        0.579863   \n",
       "13192  ChIJOfK1InJCkFQR69GMFxI4U8o  2018-06-06 12:44:57        0.472343   \n",
       "13193  ChIJOfK1InJCkFQR69GMFxI4U8o  2018-06-06 12:44:57        0.755749   \n",
       "13194  ChIJOfK1InJCkFQR69GMFxI4U8o  2018-06-06 12:44:57        0.752641   \n",
       "13195  ChIJOfK1InJCkFQR69GMFxI4U8o  2018-06-06 12:44:58        0.472343   \n",
       "13196  ChIJKU3pPXFCkFQRcF6MpFTDMkE  2018-06-06 12:44:57        0.629584   \n",
       "13197  ChIJKU3pPXFCkFQRcF6MpFTDMkE  2018-06-06 12:44:57        0.622126   \n",
       "13198  ChIJKU3pPXFCkFQRcF6MpFTDMkE  2018-06-06 12:44:57        0.619018   \n",
       "13199  ChIJKU3pPXFCkFQRcF6MpFTDMkE  2018-06-06 12:44:58        0.697328   \n",
       "13200  ChIJJ609fQxCkFQRq0MSabjnTeU  2018-06-06 12:44:57        0.571162   \n",
       "13201  ChIJJ609fQxCkFQRq0MSabjnTeU  2018-06-06 12:44:57        0.574270   \n",
       "13202  ChIJJ609fQxCkFQRq0MSabjnTeU  2018-06-06 12:44:57        0.574270   \n",
       "13203  ChIJJ609fQxCkFQRq0MSabjnTeU  2018-06-06 12:44:58        0.571162   \n",
       "\n",
       "       duration.value                                               name  \\\n",
       "0           32.183333                            Elevated Natural Health   \n",
       "1           13.633333                            Elevated Natural Health   \n",
       "2            6.700000                            Elevated Natural Health   \n",
       "3           12.583333                            Elevated Natural Health   \n",
       "4           27.900000                            Elevated Natural Health   \n",
       "5           11.583333                            Elevated Natural Health   \n",
       "6            5.316667                            Elevated Natural Health   \n",
       "7           10.983333                            Elevated Natural Health   \n",
       "8           31.016667                            Elevated Natural Health   \n",
       "9           10.116667                            Elevated Natural Health   \n",
       "10           7.066667                            Elevated Natural Health   \n",
       "11          32.116667                            Elevated Natural Health   \n",
       "12          25.150000                            Elevated Natural Health   \n",
       "13          12.850000                            Elevated Natural Health   \n",
       "14           5.450000                            Elevated Natural Health   \n",
       "15          13.283333                            Elevated Natural Health   \n",
       "16           9.133333                              Kaffeeklatsch Seattle   \n",
       "17           3.583333                              Kaffeeklatsch Seattle   \n",
       "18           3.066667                              Kaffeeklatsch Seattle   \n",
       "19           5.283333                              Kaffeeklatsch Seattle   \n",
       "20           5.366667                              Kaffeeklatsch Seattle   \n",
       "21           2.266667                              Kaffeeklatsch Seattle   \n",
       "22           1.666667                              Kaffeeklatsch Seattle   \n",
       "23           3.683333                              Kaffeeklatsch Seattle   \n",
       "24           5.100000                              Kaffeeklatsch Seattle   \n",
       "25           3.816667                              Kaffeeklatsch Seattle   \n",
       "26           4.466667                              Kaffeeklatsch Seattle   \n",
       "27           5.100000                              Kaffeeklatsch Seattle   \n",
       "28          10.200000                            Bartell Drugs Lake City   \n",
       "29           3.916667                            Bartell Drugs Lake City   \n",
       "...               ...                                                ...   \n",
       "13174        4.433333                             Rockin Robins Espresso   \n",
       "13175        7.466667                             Rockin Robins Espresso   \n",
       "13176        2.666667                    The People's Choice Marketplace   \n",
       "13177        0.983333                    The People's Choice Marketplace   \n",
       "13178        1.216667                    The People's Choice Marketplace   \n",
       "13179        2.666667                    The People's Choice Marketplace   \n",
       "13180       15.700000                                        Sunset Cafe   \n",
       "13181        4.683333                                        Sunset Cafe   \n",
       "13182        4.200000                                        Sunset Cafe   \n",
       "13183       10.400000                                        Sunset Cafe   \n",
       "13184        7.233333                                   Safeway Pharmacy   \n",
       "13185        2.466667                                   Safeway Pharmacy   \n",
       "13186        4.966667                                   Safeway Pharmacy   \n",
       "13187        7.233333                                   Safeway Pharmacy   \n",
       "13188       10.650000  Neighborcare Health at Rainier Beach (Medical ...   \n",
       "13189        3.883333  Neighborcare Health at Rainier Beach (Medical ...   \n",
       "13190        4.116667  Neighborcare Health at Rainier Beach (Medical ...   \n",
       "13191        9.033333  Neighborcare Health at Rainier Beach (Medical ...   \n",
       "13192        9.516667                                         McDonald's   \n",
       "13193        4.616667                                         McDonald's   \n",
       "13194        5.183333                                         McDonald's   \n",
       "13195        9.516667                                         McDonald's   \n",
       "13196       13.000000                                       Redwing Cafe   \n",
       "13197        4.833333                                       Redwing Cafe   \n",
       "13198        3.933333                                       Redwing Cafe   \n",
       "13199        8.900000                                       Redwing Cafe   \n",
       "13200       11.950000                                  Ethio Mini-Market   \n",
       "13201        3.700000                                  Ethio Mini-Market   \n",
       "13202        3.716667                                  Ethio Mini-Market   \n",
       "13203       11.616667                                  Ethio Mini-Market   \n",
       "\n",
       "                                   address     city        lat         lng  \\\n",
       "0      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "1      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "2      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "3      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "4      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "5      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "6      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "7      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "8      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "9      12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "10     12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "11     12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "12     12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "13     12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "14     12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "15     12000 15th Avenue Northeast Suite C  Seattle  47.716034 -122.312230   \n",
       "16               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "17               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "18               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "19               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "20               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "21               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "22               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "23               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "24               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "25               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "26               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "27               12513 Lake City Way NE #H  Seattle  47.719859 -122.295224   \n",
       "28                        3018 NE 125th St  Seattle  47.719527 -122.295845   \n",
       "29                        3018 NE 125th St  Seattle  47.719527 -122.295845   \n",
       "...                                    ...      ...        ...         ...   \n",
       "13174          1520 Southwest 100th Street  Seattle  47.514026 -122.354502   \n",
       "13175          1520 Southwest 100th Street  Seattle  47.514026 -122.354502   \n",
       "13176          9416 Delridge Way Southwest  Seattle  47.518879 -122.356506   \n",
       "13177          9416 Delridge Way Southwest  Seattle  47.518879 -122.356506   \n",
       "13178          9416 Delridge Way Southwest  Seattle  47.518879 -122.356506   \n",
       "13179          9416 Delridge Way Southwest  Seattle  47.518879 -122.356506   \n",
       "13180            8115 Rainier Avenue South  Seattle  47.529942 -122.270239   \n",
       "13181            8115 Rainier Avenue South  Seattle  47.529942 -122.270239   \n",
       "13182            8115 Rainier Avenue South  Seattle  47.529942 -122.270239   \n",
       "13183            8115 Rainier Avenue South  Seattle  47.529942 -122.270239   \n",
       "13184            9262 Rainier Avenue South  Seattle  47.521045 -122.268030   \n",
       "13185            9262 Rainier Avenue South  Seattle  47.521045 -122.268030   \n",
       "13186            9262 Rainier Avenue South  Seattle  47.521045 -122.268030   \n",
       "13187            9262 Rainier Avenue South  Seattle  47.521045 -122.268030   \n",
       "13188            9245 Rainier Avenue South  Seattle  47.519612 -122.269239   \n",
       "13189            9245 Rainier Avenue South  Seattle  47.519612 -122.269239   \n",
       "13190            9245 Rainier Avenue South  Seattle  47.519612 -122.269239   \n",
       "13191            9245 Rainier Avenue South  Seattle  47.519612 -122.269239   \n",
       "13192            9304 Rainier Avenue South  Seattle  47.520251 -122.266915   \n",
       "13193            9304 Rainier Avenue South  Seattle  47.520251 -122.266915   \n",
       "13194            9304 Rainier Avenue South  Seattle  47.520251 -122.266915   \n",
       "13195            9304 Rainier Avenue South  Seattle  47.520251 -122.266915   \n",
       "13196               9272 57th Avenue South  Seattle  47.519534 -122.261415   \n",
       "13197               9272 57th Avenue South  Seattle  47.519534 -122.261415   \n",
       "13198               9272 57th Avenue South  Seattle  47.519534 -122.261415   \n",
       "13199               9272 57th Avenue South  Seattle  47.519534 -122.261415   \n",
       "13200            8432 Rainier Avenue South  Seattle  47.527018 -122.269556   \n",
       "13201            8432 Rainier Avenue South  Seattle  47.527018 -122.269556   \n",
       "13202            8432 Rainier Avenue South  Seattle  47.527018 -122.269556   \n",
       "13203            8432 Rainier Avenue South  Seattle  47.527018 -122.269556   \n",
       "\n",
       "       rating  place_class type                               geometry  \n",
       "0         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "1         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "2         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "3         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "4         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "5         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "6         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "7         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "8         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "9         5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "10        5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "11        5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "12        5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "13        5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "14        5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "15        5.0     hospital  NaN        POINT (-122.3122296 47.7160344)  \n",
       "16        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "17        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "18        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "19        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "20        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "21        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "22        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "23        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "24        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "25        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "26        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "27        4.5         cafe  NaN          POINT (-122.295224 47.719859)  \n",
       "28        3.6     pharmacy  NaN  POINT (-122.295845 47.71952720000001)  \n",
       "29        3.6     pharmacy  NaN  POINT (-122.295845 47.71952720000001)  \n",
       "...       ...          ...  ...                                    ...  \n",
       "13174     NaN         cafe  NaN        POINT (-122.3545019 47.5140261)  \n",
       "13175     NaN         cafe  NaN        POINT (-122.3545019 47.5140261)  \n",
       "13176     5.0  supermarket  NaN        POINT (-122.3565064 47.5188795)  \n",
       "13177     5.0  supermarket  NaN        POINT (-122.3565064 47.5188795)  \n",
       "13178     5.0  supermarket  NaN        POINT (-122.3565064 47.5188795)  \n",
       "13179     5.0  supermarket  NaN        POINT (-122.3565064 47.5188795)  \n",
       "13180     4.6         cafe  NaN        POINT (-122.2702394 47.5299419)  \n",
       "13181     4.6         cafe  NaN        POINT (-122.2702394 47.5299419)  \n",
       "13182     4.6         cafe  NaN        POINT (-122.2702394 47.5299419)  \n",
       "13183     4.6         cafe  NaN        POINT (-122.2702394 47.5299419)  \n",
       "13184     2.8     pharmacy  NaN        POINT (-122.2680296 47.5210445)  \n",
       "13185     2.8     pharmacy  NaN        POINT (-122.2680296 47.5210445)  \n",
       "13186     2.8     pharmacy  NaN        POINT (-122.2680296 47.5210445)  \n",
       "13187     2.8     pharmacy  NaN        POINT (-122.2680296 47.5210445)  \n",
       "13188     4.0     pharmacy  NaN          POINT (-122.269239 47.519612)  \n",
       "13189     4.0     pharmacy  NaN          POINT (-122.269239 47.519612)  \n",
       "13190     4.0     pharmacy  NaN          POINT (-122.269239 47.519612)  \n",
       "13191     4.0     pharmacy  NaN          POINT (-122.269239 47.519612)  \n",
       "13192     3.5         cafe  NaN        POINT (-122.2669155 47.5202511)  \n",
       "13193     3.5         cafe  NaN        POINT (-122.2669155 47.5202511)  \n",
       "13194     3.5         cafe  NaN        POINT (-122.2669155 47.5202511)  \n",
       "13195     3.5         cafe  NaN        POINT (-122.2669155 47.5202511)  \n",
       "13196     4.8         cafe  NaN        POINT (-122.2614145 47.5195336)  \n",
       "13197     4.8         cafe  NaN        POINT (-122.2614145 47.5195336)  \n",
       "13198     4.8         cafe  NaN        POINT (-122.2614145 47.5195336)  \n",
       "13199     4.8         cafe  NaN        POINT (-122.2614145 47.5195336)  \n",
       "13200     5.0  supermarket  NaN          POINT (-122.269556 47.527018)  \n",
       "13201     5.0  supermarket  NaN          POINT (-122.269556 47.527018)  \n",
       "13202     5.0  supermarket  NaN          POINT (-122.269556 47.527018)  \n",
       "13203     5.0  supermarket  NaN          POINT (-122.269556 47.527018)  \n",
       "\n",
       "[13204 rows x 13 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Given these two databases share a common column (place_id), we could do an\n",
    "# \"attribute\" join\n",
    "# NOTE that the spatial information doesn't really matter in this case:\n",
    "# http://geopandas.org/mergingdata.html\n",
    "\n",
    "# Maybe we're only interested in 'departure time, distance and duration'\n",
    "# for a given destination, but want to keep the location and other attributes:\n",
    "\n",
    "columns = ('place_id','departure_time','distance.value','duration.value')\n",
    "gfSubset = gfD.loc[:,columns]\n",
    "gfSubset\n",
    "gfPlace = gfSubset.merge(gf, on='place_id')\n",
    "gfPlace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>nhood</th>\n",
       "      <th>nested</th>\n",
       "      <th>area</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seattle:Ballard:Loyal Heights</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>Ballard</td>\n",
       "      <td>Loyal Heights</td>\n",
       "      <td>2.132066e+07</td>\n",
       "      <td>Loyal Heights</td>\n",
       "      <td>POLYGON ((-122.3763324293266 47.67593689108358...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seattle:Ballard:Adams</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>Ballard</td>\n",
       "      <td>Adams</td>\n",
       "      <td>2.243128e+07</td>\n",
       "      <td>Adams</td>\n",
       "      <td>POLYGON ((-122.3763324293266 47.67593689108358...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seattle:Ballard:Whittier Heights</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>Ballard</td>\n",
       "      <td>Whittier Heights</td>\n",
       "      <td>1.419569e+07</td>\n",
       "      <td>Whittier Heights</td>\n",
       "      <td>POLYGON ((-122.3763324293266 47.67593689108358...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seattle:Ballard:West Woodland</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>Ballard</td>\n",
       "      <td>West Woodland</td>\n",
       "      <td>2.219937e+07</td>\n",
       "      <td>West Woodland</td>\n",
       "      <td>POLYGON ((-122.3763324293266 47.67593689108358...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seattle:Ballard:Sunset Hill</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>Ballard</td>\n",
       "      <td>Sunset Hill</td>\n",
       "      <td>2.433562e+07</td>\n",
       "      <td>Sunset Hill</td>\n",
       "      <td>POLYGON ((-122.40210822192 47.69766032373056, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id     city county    nhood  \\\n",
       "0     Seattle:Ballard:Loyal Heights  Seattle   King  Ballard   \n",
       "1             Seattle:Ballard:Adams  Seattle   King  Ballard   \n",
       "2  Seattle:Ballard:Whittier Heights  Seattle   King  Ballard   \n",
       "3     Seattle:Ballard:West Woodland  Seattle   King  Ballard   \n",
       "4       Seattle:Ballard:Sunset Hill  Seattle   King  Ballard   \n",
       "\n",
       "             nested          area              name  \\\n",
       "0     Loyal Heights  2.132066e+07     Loyal Heights   \n",
       "1             Adams  2.243128e+07             Adams   \n",
       "2  Whittier Heights  1.419569e+07  Whittier Heights   \n",
       "3     West Woodland  2.219937e+07     West Woodland   \n",
       "4       Sunset Hill  2.433562e+07       Sunset Hill   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-122.3763324293266 47.67593689108358...  \n",
       "1  POLYGON ((-122.3763324293266 47.67593689108358...  \n",
       "2  POLYGON ((-122.3763324293266 47.67593689108358...  \n",
       "3  POLYGON ((-122.3763324293266 47.67593689108358...  \n",
       "4  POLYGON ((-122.40210822192 47.69766032373056, ...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spatial merging uses spatial relationships (“intersects”, “within” or “contains”)\n",
    "# For example, let's extract points from the database for a particular neighborhood\n",
    "#https://github.com/seattleio\n",
    "#https://github.com/geopandas/geopandas/blob/master/examples/spatial_joins.ipynb\n",
    "\n",
    "gfN = gpd.read_file('./data/neighborhoods.geojson')\n",
    "gfN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bbbbcc0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfN.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c9f6e80>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighborhood = 'University District'\n",
    "gfNeighborhood = gfN.query('nhood == @neighborhood')\n",
    "gfNeighborhood.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_left</th>\n",
       "      <th>address</th>\n",
       "      <th>city_left</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>place_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>place_class</th>\n",
       "      <th>type</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>id</th>\n",
       "      <th>city_right</th>\n",
       "      <th>county</th>\n",
       "      <th>nhood</th>\n",
       "      <th>nested</th>\n",
       "      <th>area</th>\n",
       "      <th>name_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Trader Joe's</td>\n",
       "      <td>4555 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.662670</td>\n",
       "      <td>-122.317620</td>\n",
       "      <td>ChIJq6Rq_l8UkFQRyDDI__HHS40</td>\n",
       "      <td>4.5</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.31762 47.66267)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>United States Postal Service</td>\n",
       "      <td>4244 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659572</td>\n",
       "      <td>-122.312814</td>\n",
       "      <td>ChIJ942DrfQUkFQRZGAt56Ohmps</td>\n",
       "      <td>2.4</td>\n",
       "      <td>post_office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3128135 47.6595724)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>University Branch - The Seattle Public Library</td>\n",
       "      <td>5009 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.665244</td>\n",
       "      <td>-122.317883</td>\n",
       "      <td>ChIJVZZq9mAUkFQRRAHfUiu_sc0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>library</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3178828 47.6652442)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Planned Parenthood - Roosevelt Health Center</td>\n",
       "      <td>5020 Roosevelt Way Northeast Unit 1</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.665606</td>\n",
       "      <td>-122.317216</td>\n",
       "      <td>ChIJ34vP-2AUkFQRfX000CN_DRM</td>\n",
       "      <td>3.9</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3172158 47.66560629999999)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Pediatric Care Center at UWMC-Roosevelt</td>\n",
       "      <td>4th Floor</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658870</td>\n",
       "      <td>-122.318370</td>\n",
       "      <td>ChIJF4gRCfd5CEER2R8euiu_qGo</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.31837 47.65887)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>General Internal Medicine Center at UWMC-Roose...</td>\n",
       "      <td>3rd floor</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658839</td>\n",
       "      <td>-122.318284</td>\n",
       "      <td>ChIJtZRBUPQUkFQRZdmRDl51ZMk</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.318284 47.658839)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>University Of Washington Roosevelt Clinic</td>\n",
       "      <td>4245 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658839</td>\n",
       "      <td>-122.318283</td>\n",
       "      <td>ChIJtZRBUPQUkFQRrha7Mf1oh6M</td>\n",
       "      <td>4.2</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3182835 47.6588389)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Weight Loss Management Center at UWMC-Roosevelt</td>\n",
       "      <td>4225 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658185</td>\n",
       "      <td>-122.318323</td>\n",
       "      <td>ChIJIwGFQfQUkFQRcdVqKMfmQOo</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3183226 47.6581846)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Seattle Children's Autism Center</td>\n",
       "      <td>4909 25th Avenue Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.664693</td>\n",
       "      <td>-122.302321</td>\n",
       "      <td>ChIJRW4ofYgUkFQRxi35AvCwS4s</td>\n",
       "      <td>4.2</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3023209 47.6646934)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>UWMC</td>\n",
       "      <td>1959 Northeast Pacific Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.650199</td>\n",
       "      <td>-122.308637</td>\n",
       "      <td>ChIJAXMSQOwUkFQR-9ogUZ5H8x4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3086365 47.6501993)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>University of Washington Medical Center Obstet...</td>\n",
       "      <td>1959 Northeast Pacific Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.649287</td>\n",
       "      <td>-122.306851</td>\n",
       "      <td>ChIJRX_miesUkFQRm8r85WW5g8c</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3068507 47.6492873)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>University of Washington: Department of Pediat...</td>\n",
       "      <td>1959 Northeast Pacific Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.649339</td>\n",
       "      <td>-122.306765</td>\n",
       "      <td>ChIJ89uGFOwUkFQReIDliJVN-Zw</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3067647 47.6493393)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>UW Medical Center</td>\n",
       "      <td>1959 Northeast Pacific Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.649331</td>\n",
       "      <td>-122.306730</td>\n",
       "      <td>ChIJEU9wSOwUkFQRxUjYrb-PTJE</td>\n",
       "      <td>4.1</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3067297 47.6493309)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>UW Medical Center Surgery Pavilion</td>\n",
       "      <td>1959 NE Pacific St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.648597</td>\n",
       "      <td>-122.305330</td>\n",
       "      <td>ChIJZWhjg-sUkFQRRja4r4aAo74</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hospital</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3053296 47.6485967)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Vista Café</td>\n",
       "      <td>3720 15th Avenue Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.651643</td>\n",
       "      <td>-122.313417</td>\n",
       "      <td>ChIJhybsC-4UkFQRcLWGF1Q9MJo</td>\n",
       "      <td>4.1</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3134169 47.6516428)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Voula's Offshore Cafe</td>\n",
       "      <td>658 Northeast Northlake Way</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.654656</td>\n",
       "      <td>-122.321507</td>\n",
       "      <td>ChIJ_6PM3vAUkFQRKmNERgd3N8M</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3215067 47.65465560000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>The Rotunda</td>\n",
       "      <td>1959 Northeast Pacific Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.651133</td>\n",
       "      <td>-122.310589</td>\n",
       "      <td>ChIJ83oWzO0UkFQRr6sdL7FlrLU</td>\n",
       "      <td>3.4</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3105889 47.65113270000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>Atrium Espresso</td>\n",
       "      <td>University of Washington School of Medicine</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.649266</td>\n",
       "      <td>-122.308112</td>\n",
       "      <td>ChIJd4r_C-wUkFQRYuFjrjT6nSM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3081117 47.6492662)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Court Café</td>\n",
       "      <td>1959 Northeast Pacific Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.650472</td>\n",
       "      <td>-122.309418</td>\n",
       "      <td>ChIJDwi3OewUkFQRLalRFImOA3o</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3094177 47.6504719)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>Husky Grind at Mercer Court</td>\n",
       "      <td>3925 Adams Lane Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.654722</td>\n",
       "      <td>-122.317148</td>\n",
       "      <td>ChIJTwA1fPEUkFQRnLQr5-aUP0Q</td>\n",
       "      <td>3.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3171479 47.6547215)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Overpass Espresso</td>\n",
       "      <td>Magnuson Health Sciences Center T-Wing</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.650999</td>\n",
       "      <td>-122.309026</td>\n",
       "      <td>ChIJW52kTuwUkFQRV6mkGPoBowk</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3090261 47.6509995)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>H-bar</td>\n",
       "      <td>3920 15th Avenue Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.653260</td>\n",
       "      <td>-122.311730</td>\n",
       "      <td>ChIJIQv9efIUkFQRUtMgUxr7kl4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3117298 47.6532599)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Surgery Pavilion Espresso</td>\n",
       "      <td>University of Washington</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.648764</td>\n",
       "      <td>-122.305551</td>\n",
       "      <td>ChIJ5-Q9musUkFQR4Ib4VrtYPYs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3055507 47.6487641)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Buzz Cafe</td>\n",
       "      <td>Gould Hall (GLD)</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.654947</td>\n",
       "      <td>-122.312508</td>\n",
       "      <td>ChIJv8NnWPIUkFQRtlIKqPJOXpI</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3125082 47.6549468)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Bean &amp; Bagel</td>\n",
       "      <td>1410 Northeast 40th Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.655436</td>\n",
       "      <td>-122.312781</td>\n",
       "      <td>ChIJT0WJVfIUkFQR7Om-dyKcHok</td>\n",
       "      <td>4.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3127815 47.6554359)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>Husky Grind at District Market</td>\n",
       "      <td>1315 Northeast Campus Parkway</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.655846</td>\n",
       "      <td>-122.313808</td>\n",
       "      <td>ChIJ2eQIqfMUkFQRg5vN3vSI9ZI</td>\n",
       "      <td>3.4</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3138085 47.65584620000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>Portage Bay Cafe</td>\n",
       "      <td>4130 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.657779</td>\n",
       "      <td>-122.317525</td>\n",
       "      <td>ChIJzZzBbfQUkFQRP0f0nUznInY</td>\n",
       "      <td>4.3</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3175253 47.6577795)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>City Grind Espresso at The Henry</td>\n",
       "      <td>4100 15th Avenue Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.656225</td>\n",
       "      <td>-122.311816</td>\n",
       "      <td>ChIJG-0WH_MUkFQR1nfRTUJ17Ts</td>\n",
       "      <td>5.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3118162 47.6562254)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>Reboot Cafe</td>\n",
       "      <td>Paul G. Allen Center for Computer Science &amp; En...</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.653373</td>\n",
       "      <td>-122.305710</td>\n",
       "      <td>ChIJfe0fzZIUkFQRp9k-690xopA</td>\n",
       "      <td>4.4</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3057102 47.65337279999999)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>Mary Gates Hall Espresso</td>\n",
       "      <td>1851 Northeast Grant Lane</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.654637</td>\n",
       "      <td>-122.307481</td>\n",
       "      <td>ChIJnwOTrfIUkFQR7uzFy_69AxU</td>\n",
       "      <td>4.1</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3074811 47.6546367)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>Bambu Desserts &amp; Drinks</td>\n",
       "      <td>4511 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661684</td>\n",
       "      <td>-122.313291</td>\n",
       "      <td>ChIJHU8d5ooUkFQRc5dP-nn88uI</td>\n",
       "      <td>3.7</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3132905 47.6616842)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>Sureshot Espresso</td>\n",
       "      <td>4505 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661475</td>\n",
       "      <td>-122.313522</td>\n",
       "      <td>ChIJJy3C5ooUkFQR2KbseduBHTk</td>\n",
       "      <td>4.3</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3135218 47.66147539999999)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>Rick's Cafe</td>\n",
       "      <td>Whitman Court</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659287</td>\n",
       "      <td>-122.303603</td>\n",
       "      <td>ChIJ7UAxnI4UkFQR_w-mbg5oYzc</td>\n",
       "      <td>5.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3036026 47.65928710000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>Orin's Place</td>\n",
       "      <td>University District</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659141</td>\n",
       "      <td>-122.308598</td>\n",
       "      <td>ChIJyxMrhIwUkFQRqDuQ0kmVoRU</td>\n",
       "      <td>3.2</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3085983 47.6591406)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>Oasis</td>\n",
       "      <td>4524 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661611</td>\n",
       "      <td>-122.312741</td>\n",
       "      <td>ChIJq9Hw5IoUkFQRwYb0N_6MWBg</td>\n",
       "      <td>4.3</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.312741 47.6616111)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>Parnassus</td>\n",
       "      <td>4000 15th Avenue Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658283</td>\n",
       "      <td>-122.306545</td>\n",
       "      <td>ChIJc-HtVowUkFQR8jL7IPqPmpE</td>\n",
       "      <td>4.7</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3065451 47.6582829)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>The Supreme Cup</td>\n",
       "      <td>4293 Memorial Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659323</td>\n",
       "      <td>-122.310842</td>\n",
       "      <td>ChIJS9fhWIsUkFQRSAIUp7o3Ijw</td>\n",
       "      <td>2.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3108416 47.6593227)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>Beetle Cafe</td>\n",
       "      <td>4334 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.660786</td>\n",
       "      <td>-122.312930</td>\n",
       "      <td>ChIJZaOGIYsUkFQRDD61nHXvoAM</td>\n",
       "      <td>4.3</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3129305 47.6607864)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>西雅圖美心</td>\n",
       "      <td>4343 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661062</td>\n",
       "      <td>-122.313496</td>\n",
       "      <td>ChIJUaE834oUkFQR9ZVd49_7eaU</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3134961 47.6610619)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>Slate Coffee Roasters</td>\n",
       "      <td>1309 Northeast 45th Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661121</td>\n",
       "      <td>-122.313882</td>\n",
       "      <td>ChIJ64092YoUkFQRS3tSeE8P1OU</td>\n",
       "      <td>4.4</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3138822 47.6611211)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>Wann Yen</td>\n",
       "      <td>1313 Northeast 43rd Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659740</td>\n",
       "      <td>-122.313524</td>\n",
       "      <td>ChIJ7W6wNIsUkFQRLVUSj8Qz53g</td>\n",
       "      <td>4.8</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3135242 47.65974)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>Ugly Mug Cafe &amp; Coffee Roasters</td>\n",
       "      <td>1309 Northeast 43rd Street</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659637</td>\n",
       "      <td>-122.313952</td>\n",
       "      <td>ChIJwZGQzPQUkFQRRhd8D82mN_k</td>\n",
       "      <td>4.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3139517 47.65963720000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>Cafe Allegro</td>\n",
       "      <td>4214 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658787</td>\n",
       "      <td>-122.312854</td>\n",
       "      <td>ChIJ-foDq_QUkFQRMB-kVqbhxQw</td>\n",
       "      <td>4.4</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3128543 47.65878660000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>Public Grounds</td>\n",
       "      <td>4060 George Washington Lane Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.657504</td>\n",
       "      <td>-122.310405</td>\n",
       "      <td>ChIJC6Hty4wUkFQR3wn0SaATTd8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3104048 47.65750379999999)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>Tower Café</td>\n",
       "      <td>Mezzanine</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.660496</td>\n",
       "      <td>-122.315133</td>\n",
       "      <td>ChIJ53WL04oUkFQRxKYrT-Rd3wM</td>\n",
       "      <td>3.5</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3151334 47.6604955)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>Bulldog News</td>\n",
       "      <td>4208 University Way NE</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658545</td>\n",
       "      <td>-122.313071</td>\n",
       "      <td>ChIJf99kqPQUkFQR18QQrQUcR6g</td>\n",
       "      <td>4.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3130705 47.6585451)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>Cafe on the Ave.</td>\n",
       "      <td>4201 University Way NE</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658449</td>\n",
       "      <td>-122.313476</td>\n",
       "      <td>ChIJbV44pvQUkFQRBuSZQ_SrifQ</td>\n",
       "      <td>3.9</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3134756 47.6584494)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>4147 University Way NE</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658176</td>\n",
       "      <td>-122.313476</td>\n",
       "      <td>ChIJm2i9WPMUkFQRQ8B_XnsrsTc</td>\n",
       "      <td>4.2</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3134757 47.6581759)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>4001 East Stevens Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.655297</td>\n",
       "      <td>-122.305034</td>\n",
       "      <td>ChIJd4oropIUkFQRqgtOcCXdrKU</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.305034 47.655297)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>Pineapple Café</td>\n",
       "      <td>4242 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.659548</td>\n",
       "      <td>-122.317444</td>\n",
       "      <td>ChIJVUuOAvUUkFQR_6qR2MmpKmU</td>\n",
       "      <td>3.6</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3174442 47.6595485)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>Higher Education Tutors</td>\n",
       "      <td>4000 15th Avenue Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.653354</td>\n",
       "      <td>-122.311960</td>\n",
       "      <td>ChIJg-mDdvIUkFQR-S6NlKK7Bcc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.31196 47.6533537)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>Dawg Bites</td>\n",
       "      <td>Intramural Activities Building</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.653473</td>\n",
       "      <td>-122.301685</td>\n",
       "      <td>ChIJ8SgXBJMUkFQRQM4LYe2jMSQ</td>\n",
       "      <td>3.2</td>\n",
       "      <td>cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3016849 47.653473)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>District Market</td>\n",
       "      <td>1315 Northeast Campus Parkway</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.655665</td>\n",
       "      <td>-122.313469</td>\n",
       "      <td>ChIJ3d5ZB_MUkFQRp895UHels0Y</td>\n",
       "      <td>3.6</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3134689 47.6556649)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>H Mart</td>\n",
       "      <td>4216 University Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.658890</td>\n",
       "      <td>-122.312864</td>\n",
       "      <td>ChIJe5f8rPQUkFQRSDaDuXJUcOQ</td>\n",
       "      <td>4.4</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3128643 47.6588898)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>Super Supplements</td>\n",
       "      <td>4336 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661053</td>\n",
       "      <td>-122.317377</td>\n",
       "      <td>ChIJnSZlP_UUkFQRE8TDyAy8rz0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3173771 47.661053)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>Super Supplements - U-District</td>\n",
       "      <td>4336 Roosevelt Way Northeast</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>47.661067</td>\n",
       "      <td>-122.317521</td>\n",
       "      <td>ChIJrd-DP_UUkFQRJy5zWy4dq20</td>\n",
       "      <td>4.7</td>\n",
       "      <td>supermarket</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3175214 47.66106720000001)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>Ravenna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.665206</td>\n",
       "      <td>-122.303858</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3038576 47.66520562)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>University Campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.654449</td>\n",
       "      <td>-122.309501</td>\n",
       "      <td>University-Campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3095013 47.65444945)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>University District Northwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.661482</td>\n",
       "      <td>-122.315469</td>\n",
       "      <td>University-District-Northwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>urban village</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-122.3154694 47.66148179)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>University of Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.655614</td>\n",
       "      <td>-122.305211</td>\n",
       "      <td>University-of-Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>citywide</td>\n",
       "      <td>Major Employment Area</td>\n",
       "      <td>POINT (-122.305211 47.655614)</td>\n",
       "      <td>5</td>\n",
       "      <td>Seattle:University District:</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>King</td>\n",
       "      <td>University District</td>\n",
       "      <td>None</td>\n",
       "      <td>4.934839e+07</td>\n",
       "      <td>University District</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name_left  \\\n",
       "13                                         Trader Joe's   \n",
       "64                         United States Postal Service   \n",
       "302      University Branch - The Seattle Public Library   \n",
       "314        Planned Parenthood - Roosevelt Health Center   \n",
       "317             Pediatric Care Center at UWMC-Roosevelt   \n",
       "318   General Internal Medicine Center at UWMC-Roose...   \n",
       "319           University Of Washington Roosevelt Clinic   \n",
       "320     Weight Loss Management Center at UWMC-Roosevelt   \n",
       "321                    Seattle Children's Autism Center   \n",
       "501                                                UWMC   \n",
       "502   University of Washington Medical Center Obstet...   \n",
       "503   University of Washington: Department of Pediat...   \n",
       "504                                   UW Medical Center   \n",
       "968                  UW Medical Center Surgery Pavilion   \n",
       "981                                          Vista Café   \n",
       "984                               Voula's Offshore Cafe   \n",
       "985                                         The Rotunda   \n",
       "986                                     Atrium Espresso   \n",
       "988                                          Court Café   \n",
       "990                         Husky Grind at Mercer Court   \n",
       "991                                   Overpass Espresso   \n",
       "994                                               H-bar   \n",
       "995                           Surgery Pavilion Espresso   \n",
       "998                                           Buzz Cafe   \n",
       "999                                        Bean & Bagel   \n",
       "1000                     Husky Grind at District Market   \n",
       "1005                                   Portage Bay Cafe   \n",
       "1006                   City Grind Espresso at The Henry   \n",
       "1013                                        Reboot Cafe   \n",
       "1015                           Mary Gates Hall Espresso   \n",
       "...                                                 ...   \n",
       "1115                            Bambu Desserts & Drinks   \n",
       "1116                                  Sureshot Espresso   \n",
       "1439                                        Rick's Cafe   \n",
       "1441                                       Orin's Place   \n",
       "1442                                              Oasis   \n",
       "1443                                          Parnassus   \n",
       "1444                                    The Supreme Cup   \n",
       "1445                                        Beetle Cafe   \n",
       "1446                                              西雅圖美心   \n",
       "1447                              Slate Coffee Roasters   \n",
       "1448                                           Wann Yen   \n",
       "1450                    Ugly Mug Cafe & Coffee Roasters   \n",
       "1451                                       Cafe Allegro   \n",
       "1452                                     Public Grounds   \n",
       "1453                                         Tower Café   \n",
       "1454                                       Bulldog News   \n",
       "1456                                   Cafe on the Ave.   \n",
       "1457                                          Starbucks   \n",
       "1459                                          Starbucks   \n",
       "1461                                     Pineapple Café   \n",
       "1488                            Higher Education Tutors   \n",
       "1489                                         Dawg Bites   \n",
       "1490                                    District Market   \n",
       "1491                                             H Mart   \n",
       "1492                                  Super Supplements   \n",
       "1493                     Super Supplements - U-District   \n",
       "1572                                            Ravenna   \n",
       "1576                                  University Campus   \n",
       "1577                      University District Northwest   \n",
       "1613                           University of Washington   \n",
       "\n",
       "                                                address city_left        lat  \\\n",
       "13                         4555 Roosevelt Way Northeast   Seattle  47.662670   \n",
       "64                        4244 University Way Northeast   Seattle  47.659572   \n",
       "302                        5009 Roosevelt Way Northeast   Seattle  47.665244   \n",
       "314                 5020 Roosevelt Way Northeast Unit 1   Seattle  47.665606   \n",
       "317                                           4th Floor   Seattle  47.658870   \n",
       "318                                           3rd floor   Seattle  47.658839   \n",
       "319                        4245 Roosevelt Way Northeast   Seattle  47.658839   \n",
       "320                        4225 Roosevelt Way Northeast   Seattle  47.658185   \n",
       "321                          4909 25th Avenue Northeast   Seattle  47.664693   \n",
       "501                       1959 Northeast Pacific Street   Seattle  47.650199   \n",
       "502                       1959 Northeast Pacific Street   Seattle  47.649287   \n",
       "503                       1959 Northeast Pacific Street   Seattle  47.649339   \n",
       "504                       1959 Northeast Pacific Street   Seattle  47.649331   \n",
       "968                                  1959 NE Pacific St   Seattle  47.648597   \n",
       "981                          3720 15th Avenue Northeast   Seattle  47.651643   \n",
       "984                         658 Northeast Northlake Way   Seattle  47.654656   \n",
       "985                       1959 Northeast Pacific Street   Seattle  47.651133   \n",
       "986         University of Washington School of Medicine   Seattle  47.649266   \n",
       "988                       1959 Northeast Pacific Street   Seattle  47.650472   \n",
       "990                           3925 Adams Lane Northeast   Seattle  47.654722   \n",
       "991              Magnuson Health Sciences Center T-Wing   Seattle  47.650999   \n",
       "994                          3920 15th Avenue Northeast   Seattle  47.653260   \n",
       "995                            University of Washington   Seattle  47.648764   \n",
       "998                                    Gould Hall (GLD)   Seattle  47.654947   \n",
       "999                          1410 Northeast 40th Street   Seattle  47.655436   \n",
       "1000                      1315 Northeast Campus Parkway   Seattle  47.655846   \n",
       "1005                       4130 Roosevelt Way Northeast   Seattle  47.657779   \n",
       "1006                         4100 15th Avenue Northeast   Seattle  47.656225   \n",
       "1013  Paul G. Allen Center for Computer Science & En...   Seattle  47.653373   \n",
       "1015                          1851 Northeast Grant Lane   Seattle  47.654637   \n",
       "...                                                 ...       ...        ...   \n",
       "1115                      4511 University Way Northeast   Seattle  47.661684   \n",
       "1116                      4505 University Way Northeast   Seattle  47.661475   \n",
       "1439                                      Whitman Court   Seattle  47.659287   \n",
       "1441                                University District   Seattle  47.659141   \n",
       "1442                      4524 University Way Northeast   Seattle  47.661611   \n",
       "1443                         4000 15th Avenue Northeast   Seattle  47.658283   \n",
       "1444                        4293 Memorial Way Northeast   Seattle  47.659323   \n",
       "1445                      4334 University Way Northeast   Seattle  47.660786   \n",
       "1446                      4343 University Way Northeast   Seattle  47.661062   \n",
       "1447                         1309 Northeast 45th Street   Seattle  47.661121   \n",
       "1448                         1313 Northeast 43rd Street   Seattle  47.659740   \n",
       "1450                         1309 Northeast 43rd Street   Seattle  47.659637   \n",
       "1451                      4214 University Way Northeast   Seattle  47.658787   \n",
       "1452              4060 George Washington Lane Northeast   Seattle  47.657504   \n",
       "1453                                          Mezzanine   Seattle  47.660496   \n",
       "1454                             4208 University Way NE   Seattle  47.658545   \n",
       "1456                             4201 University Way NE   Seattle  47.658449   \n",
       "1457                             4147 University Way NE   Seattle  47.658176   \n",
       "1459                    4001 East Stevens Way Northeast   Seattle  47.655297   \n",
       "1461                       4242 Roosevelt Way Northeast   Seattle  47.659548   \n",
       "1488                         4000 15th Avenue Northeast   Seattle  47.653354   \n",
       "1489                     Intramural Activities Building   Seattle  47.653473   \n",
       "1490                      1315 Northeast Campus Parkway   Seattle  47.655665   \n",
       "1491                      4216 University Way Northeast   Seattle  47.658890   \n",
       "1492                       4336 Roosevelt Way Northeast   Seattle  47.661053   \n",
       "1493                       4336 Roosevelt Way Northeast   Seattle  47.661067   \n",
       "1572                                                NaN       NaN  47.665206   \n",
       "1576                                                NaN       NaN  47.654449   \n",
       "1577                                                NaN       NaN  47.661482   \n",
       "1613                                                NaN       NaN  47.655614   \n",
       "\n",
       "             lng                       place_id  rating    place_class  \\\n",
       "13   -122.317620    ChIJq6Rq_l8UkFQRyDDI__HHS40     4.5    supermarket   \n",
       "64   -122.312814    ChIJ942DrfQUkFQRZGAt56Ohmps     2.4    post_office   \n",
       "302  -122.317883    ChIJVZZq9mAUkFQRRAHfUiu_sc0     4.3        library   \n",
       "314  -122.317216    ChIJ34vP-2AUkFQRfX000CN_DRM     3.9       hospital   \n",
       "317  -122.318370    ChIJF4gRCfd5CEER2R8euiu_qGo     5.0       hospital   \n",
       "318  -122.318284    ChIJtZRBUPQUkFQRZdmRDl51ZMk     5.0       hospital   \n",
       "319  -122.318283    ChIJtZRBUPQUkFQRrha7Mf1oh6M     4.2       hospital   \n",
       "320  -122.318323    ChIJIwGFQfQUkFQRcdVqKMfmQOo     5.0       hospital   \n",
       "321  -122.302321    ChIJRW4ofYgUkFQRxi35AvCwS4s     4.2       hospital   \n",
       "501  -122.308637    ChIJAXMSQOwUkFQR-9ogUZ5H8x4     5.0       hospital   \n",
       "502  -122.306851    ChIJRX_miesUkFQRm8r85WW5g8c     5.0       hospital   \n",
       "503  -122.306765    ChIJ89uGFOwUkFQReIDliJVN-Zw     5.0       hospital   \n",
       "504  -122.306730    ChIJEU9wSOwUkFQRxUjYrb-PTJE     4.1       hospital   \n",
       "968  -122.305330    ChIJZWhjg-sUkFQRRja4r4aAo74     5.0       hospital   \n",
       "981  -122.313417    ChIJhybsC-4UkFQRcLWGF1Q9MJo     4.1           cafe   \n",
       "984  -122.321507    ChIJ_6PM3vAUkFQRKmNERgd3N8M     4.5           cafe   \n",
       "985  -122.310589    ChIJ83oWzO0UkFQRr6sdL7FlrLU     3.4           cafe   \n",
       "986  -122.308112    ChIJd4r_C-wUkFQRYuFjrjT6nSM     NaN           cafe   \n",
       "988  -122.309418    ChIJDwi3OewUkFQRLalRFImOA3o     3.0           cafe   \n",
       "990  -122.317148    ChIJTwA1fPEUkFQRnLQr5-aUP0Q     3.8           cafe   \n",
       "991  -122.309026    ChIJW52kTuwUkFQRV6mkGPoBowk     3.0           cafe   \n",
       "994  -122.311730    ChIJIQv9efIUkFQRUtMgUxr7kl4     3.8           cafe   \n",
       "995  -122.305551    ChIJ5-Q9musUkFQR4Ib4VrtYPYs     NaN           cafe   \n",
       "998  -122.312508    ChIJv8NnWPIUkFQRtlIKqPJOXpI     3.0           cafe   \n",
       "999  -122.312781    ChIJT0WJVfIUkFQR7Om-dyKcHok     4.5           cafe   \n",
       "1000 -122.313808    ChIJ2eQIqfMUkFQRg5vN3vSI9ZI     3.4           cafe   \n",
       "1005 -122.317525    ChIJzZzBbfQUkFQRP0f0nUznInY     4.3           cafe   \n",
       "1006 -122.311816    ChIJG-0WH_MUkFQR1nfRTUJ17Ts     5.0           cafe   \n",
       "1013 -122.305710    ChIJfe0fzZIUkFQRp9k-690xopA     4.4           cafe   \n",
       "1015 -122.307481    ChIJnwOTrfIUkFQR7uzFy_69AxU     4.1           cafe   \n",
       "...          ...                            ...     ...            ...   \n",
       "1115 -122.313291    ChIJHU8d5ooUkFQRc5dP-nn88uI     3.7           cafe   \n",
       "1116 -122.313522    ChIJJy3C5ooUkFQR2KbseduBHTk     4.3           cafe   \n",
       "1439 -122.303603    ChIJ7UAxnI4UkFQR_w-mbg5oYzc     5.0           cafe   \n",
       "1441 -122.308598    ChIJyxMrhIwUkFQRqDuQ0kmVoRU     3.2           cafe   \n",
       "1442 -122.312741    ChIJq9Hw5IoUkFQRwYb0N_6MWBg     4.3           cafe   \n",
       "1443 -122.306545    ChIJc-HtVowUkFQR8jL7IPqPmpE     4.7           cafe   \n",
       "1444 -122.310842    ChIJS9fhWIsUkFQRSAIUp7o3Ijw     2.5           cafe   \n",
       "1445 -122.312930    ChIJZaOGIYsUkFQRDD61nHXvoAM     4.3           cafe   \n",
       "1446 -122.313496    ChIJUaE834oUkFQR9ZVd49_7eaU     3.0           cafe   \n",
       "1447 -122.313882    ChIJ64092YoUkFQRS3tSeE8P1OU     4.4           cafe   \n",
       "1448 -122.313524    ChIJ7W6wNIsUkFQRLVUSj8Qz53g     4.8           cafe   \n",
       "1450 -122.313952    ChIJwZGQzPQUkFQRRhd8D82mN_k     4.6           cafe   \n",
       "1451 -122.312854    ChIJ-foDq_QUkFQRMB-kVqbhxQw     4.4           cafe   \n",
       "1452 -122.310405    ChIJC6Hty4wUkFQR3wn0SaATTd8     3.0           cafe   \n",
       "1453 -122.315133    ChIJ53WL04oUkFQRxKYrT-Rd3wM     3.5           cafe   \n",
       "1454 -122.313071    ChIJf99kqPQUkFQR18QQrQUcR6g     4.6           cafe   \n",
       "1456 -122.313476    ChIJbV44pvQUkFQRBuSZQ_SrifQ     3.9           cafe   \n",
       "1457 -122.313476    ChIJm2i9WPMUkFQRQ8B_XnsrsTc     4.2           cafe   \n",
       "1459 -122.305034    ChIJd4oropIUkFQRqgtOcCXdrKU     3.0           cafe   \n",
       "1461 -122.317444    ChIJVUuOAvUUkFQR_6qR2MmpKmU     3.6           cafe   \n",
       "1488 -122.311960    ChIJg-mDdvIUkFQR-S6NlKK7Bcc     NaN         school   \n",
       "1489 -122.301685    ChIJ8SgXBJMUkFQRQM4LYe2jMSQ     3.2           cafe   \n",
       "1490 -122.313469    ChIJ3d5ZB_MUkFQRp895UHels0Y     3.6    supermarket   \n",
       "1491 -122.312864    ChIJe5f8rPQUkFQRSDaDuXJUcOQ     4.4    supermarket   \n",
       "1492 -122.317377    ChIJnSZlP_UUkFQRE8TDyAy8rz0     3.6    supermarket   \n",
       "1493 -122.317521    ChIJrd-DP_UUkFQRJy5zWy4dq20     4.7    supermarket   \n",
       "1572 -122.303858                        Ravenna     NaN  urban village   \n",
       "1576 -122.309501              University-Campus     NaN  urban village   \n",
       "1577 -122.315469  University-District-Northwest     NaN  urban village   \n",
       "1613 -122.305211       University-of-Washington     NaN       citywide   \n",
       "\n",
       "                       type                                geometry  \\\n",
       "13                      NaN             POINT (-122.31762 47.66267)   \n",
       "64                      NaN         POINT (-122.3128135 47.6595724)   \n",
       "302                     NaN         POINT (-122.3178828 47.6652442)   \n",
       "314                     NaN  POINT (-122.3172158 47.66560629999999)   \n",
       "317                     NaN             POINT (-122.31837 47.65887)   \n",
       "318                     NaN           POINT (-122.318284 47.658839)   \n",
       "319                     NaN         POINT (-122.3182835 47.6588389)   \n",
       "320                     NaN         POINT (-122.3183226 47.6581846)   \n",
       "321                     NaN         POINT (-122.3023209 47.6646934)   \n",
       "501                     NaN         POINT (-122.3086365 47.6501993)   \n",
       "502                     NaN         POINT (-122.3068507 47.6492873)   \n",
       "503                     NaN         POINT (-122.3067647 47.6493393)   \n",
       "504                     NaN         POINT (-122.3067297 47.6493309)   \n",
       "968                     NaN         POINT (-122.3053296 47.6485967)   \n",
       "981                     NaN         POINT (-122.3134169 47.6516428)   \n",
       "984                     NaN  POINT (-122.3215067 47.65465560000001)   \n",
       "985                     NaN  POINT (-122.3105889 47.65113270000001)   \n",
       "986                     NaN         POINT (-122.3081117 47.6492662)   \n",
       "988                     NaN         POINT (-122.3094177 47.6504719)   \n",
       "990                     NaN         POINT (-122.3171479 47.6547215)   \n",
       "991                     NaN         POINT (-122.3090261 47.6509995)   \n",
       "994                     NaN         POINT (-122.3117298 47.6532599)   \n",
       "995                     NaN         POINT (-122.3055507 47.6487641)   \n",
       "998                     NaN         POINT (-122.3125082 47.6549468)   \n",
       "999                     NaN         POINT (-122.3127815 47.6554359)   \n",
       "1000                    NaN  POINT (-122.3138085 47.65584620000001)   \n",
       "1005                    NaN         POINT (-122.3175253 47.6577795)   \n",
       "1006                    NaN         POINT (-122.3118162 47.6562254)   \n",
       "1013                    NaN  POINT (-122.3057102 47.65337279999999)   \n",
       "1015                    NaN         POINT (-122.3074811 47.6546367)   \n",
       "...                     ...                                     ...   \n",
       "1115                    NaN         POINT (-122.3132905 47.6616842)   \n",
       "1116                    NaN  POINT (-122.3135218 47.66147539999999)   \n",
       "1439                    NaN  POINT (-122.3036026 47.65928710000001)   \n",
       "1441                    NaN         POINT (-122.3085983 47.6591406)   \n",
       "1442                    NaN          POINT (-122.312741 47.6616111)   \n",
       "1443                    NaN         POINT (-122.3065451 47.6582829)   \n",
       "1444                    NaN         POINT (-122.3108416 47.6593227)   \n",
       "1445                    NaN         POINT (-122.3129305 47.6607864)   \n",
       "1446                    NaN         POINT (-122.3134961 47.6610619)   \n",
       "1447                    NaN         POINT (-122.3138822 47.6611211)   \n",
       "1448                    NaN           POINT (-122.3135242 47.65974)   \n",
       "1450                    NaN  POINT (-122.3139517 47.65963720000001)   \n",
       "1451                    NaN  POINT (-122.3128543 47.65878660000001)   \n",
       "1452                    NaN  POINT (-122.3104048 47.65750379999999)   \n",
       "1453                    NaN         POINT (-122.3151334 47.6604955)   \n",
       "1454                    NaN         POINT (-122.3130705 47.6585451)   \n",
       "1456                    NaN         POINT (-122.3134756 47.6584494)   \n",
       "1457                    NaN         POINT (-122.3134757 47.6581759)   \n",
       "1459                    NaN           POINT (-122.305034 47.655297)   \n",
       "1461                    NaN         POINT (-122.3174442 47.6595485)   \n",
       "1488                    NaN           POINT (-122.31196 47.6533537)   \n",
       "1489                    NaN          POINT (-122.3016849 47.653473)   \n",
       "1490                    NaN         POINT (-122.3134689 47.6556649)   \n",
       "1491                    NaN         POINT (-122.3128643 47.6588898)   \n",
       "1492                    NaN          POINT (-122.3173771 47.661053)   \n",
       "1493                    NaN  POINT (-122.3175214 47.66106720000001)   \n",
       "1572                    NaN        POINT (-122.3038576 47.66520562)   \n",
       "1576                    NaN        POINT (-122.3095013 47.65444945)   \n",
       "1577                    NaN        POINT (-122.3154694 47.66148179)   \n",
       "1613  Major Employment Area           POINT (-122.305211 47.655614)   \n",
       "\n",
       "      index_right                            id city_right county  \\\n",
       "13              5  Seattle:University District:    Seattle   King   \n",
       "64              5  Seattle:University District:    Seattle   King   \n",
       "302             5  Seattle:University District:    Seattle   King   \n",
       "314             5  Seattle:University District:    Seattle   King   \n",
       "317             5  Seattle:University District:    Seattle   King   \n",
       "318             5  Seattle:University District:    Seattle   King   \n",
       "319             5  Seattle:University District:    Seattle   King   \n",
       "320             5  Seattle:University District:    Seattle   King   \n",
       "321             5  Seattle:University District:    Seattle   King   \n",
       "501             5  Seattle:University District:    Seattle   King   \n",
       "502             5  Seattle:University District:    Seattle   King   \n",
       "503             5  Seattle:University District:    Seattle   King   \n",
       "504             5  Seattle:University District:    Seattle   King   \n",
       "968             5  Seattle:University District:    Seattle   King   \n",
       "981             5  Seattle:University District:    Seattle   King   \n",
       "984             5  Seattle:University District:    Seattle   King   \n",
       "985             5  Seattle:University District:    Seattle   King   \n",
       "986             5  Seattle:University District:    Seattle   King   \n",
       "988             5  Seattle:University District:    Seattle   King   \n",
       "990             5  Seattle:University District:    Seattle   King   \n",
       "991             5  Seattle:University District:    Seattle   King   \n",
       "994             5  Seattle:University District:    Seattle   King   \n",
       "995             5  Seattle:University District:    Seattle   King   \n",
       "998             5  Seattle:University District:    Seattle   King   \n",
       "999             5  Seattle:University District:    Seattle   King   \n",
       "1000            5  Seattle:University District:    Seattle   King   \n",
       "1005            5  Seattle:University District:    Seattle   King   \n",
       "1006            5  Seattle:University District:    Seattle   King   \n",
       "1013            5  Seattle:University District:    Seattle   King   \n",
       "1015            5  Seattle:University District:    Seattle   King   \n",
       "...           ...                           ...        ...    ...   \n",
       "1115            5  Seattle:University District:    Seattle   King   \n",
       "1116            5  Seattle:University District:    Seattle   King   \n",
       "1439            5  Seattle:University District:    Seattle   King   \n",
       "1441            5  Seattle:University District:    Seattle   King   \n",
       "1442            5  Seattle:University District:    Seattle   King   \n",
       "1443            5  Seattle:University District:    Seattle   King   \n",
       "1444            5  Seattle:University District:    Seattle   King   \n",
       "1445            5  Seattle:University District:    Seattle   King   \n",
       "1446            5  Seattle:University District:    Seattle   King   \n",
       "1447            5  Seattle:University District:    Seattle   King   \n",
       "1448            5  Seattle:University District:    Seattle   King   \n",
       "1450            5  Seattle:University District:    Seattle   King   \n",
       "1451            5  Seattle:University District:    Seattle   King   \n",
       "1452            5  Seattle:University District:    Seattle   King   \n",
       "1453            5  Seattle:University District:    Seattle   King   \n",
       "1454            5  Seattle:University District:    Seattle   King   \n",
       "1456            5  Seattle:University District:    Seattle   King   \n",
       "1457            5  Seattle:University District:    Seattle   King   \n",
       "1459            5  Seattle:University District:    Seattle   King   \n",
       "1461            5  Seattle:University District:    Seattle   King   \n",
       "1488            5  Seattle:University District:    Seattle   King   \n",
       "1489            5  Seattle:University District:    Seattle   King   \n",
       "1490            5  Seattle:University District:    Seattle   King   \n",
       "1491            5  Seattle:University District:    Seattle   King   \n",
       "1492            5  Seattle:University District:    Seattle   King   \n",
       "1493            5  Seattle:University District:    Seattle   King   \n",
       "1572            5  Seattle:University District:    Seattle   King   \n",
       "1576            5  Seattle:University District:    Seattle   King   \n",
       "1577            5  Seattle:University District:    Seattle   King   \n",
       "1613            5  Seattle:University District:    Seattle   King   \n",
       "\n",
       "                    nhood nested          area           name_right  \n",
       "13    University District   None  4.934839e+07  University District  \n",
       "64    University District   None  4.934839e+07  University District  \n",
       "302   University District   None  4.934839e+07  University District  \n",
       "314   University District   None  4.934839e+07  University District  \n",
       "317   University District   None  4.934839e+07  University District  \n",
       "318   University District   None  4.934839e+07  University District  \n",
       "319   University District   None  4.934839e+07  University District  \n",
       "320   University District   None  4.934839e+07  University District  \n",
       "321   University District   None  4.934839e+07  University District  \n",
       "501   University District   None  4.934839e+07  University District  \n",
       "502   University District   None  4.934839e+07  University District  \n",
       "503   University District   None  4.934839e+07  University District  \n",
       "504   University District   None  4.934839e+07  University District  \n",
       "968   University District   None  4.934839e+07  University District  \n",
       "981   University District   None  4.934839e+07  University District  \n",
       "984   University District   None  4.934839e+07  University District  \n",
       "985   University District   None  4.934839e+07  University District  \n",
       "986   University District   None  4.934839e+07  University District  \n",
       "988   University District   None  4.934839e+07  University District  \n",
       "990   University District   None  4.934839e+07  University District  \n",
       "991   University District   None  4.934839e+07  University District  \n",
       "994   University District   None  4.934839e+07  University District  \n",
       "995   University District   None  4.934839e+07  University District  \n",
       "998   University District   None  4.934839e+07  University District  \n",
       "999   University District   None  4.934839e+07  University District  \n",
       "1000  University District   None  4.934839e+07  University District  \n",
       "1005  University District   None  4.934839e+07  University District  \n",
       "1006  University District   None  4.934839e+07  University District  \n",
       "1013  University District   None  4.934839e+07  University District  \n",
       "1015  University District   None  4.934839e+07  University District  \n",
       "...                   ...    ...           ...                  ...  \n",
       "1115  University District   None  4.934839e+07  University District  \n",
       "1116  University District   None  4.934839e+07  University District  \n",
       "1439  University District   None  4.934839e+07  University District  \n",
       "1441  University District   None  4.934839e+07  University District  \n",
       "1442  University District   None  4.934839e+07  University District  \n",
       "1443  University District   None  4.934839e+07  University District  \n",
       "1444  University District   None  4.934839e+07  University District  \n",
       "1445  University District   None  4.934839e+07  University District  \n",
       "1446  University District   None  4.934839e+07  University District  \n",
       "1447  University District   None  4.934839e+07  University District  \n",
       "1448  University District   None  4.934839e+07  University District  \n",
       "1450  University District   None  4.934839e+07  University District  \n",
       "1451  University District   None  4.934839e+07  University District  \n",
       "1452  University District   None  4.934839e+07  University District  \n",
       "1453  University District   None  4.934839e+07  University District  \n",
       "1454  University District   None  4.934839e+07  University District  \n",
       "1456  University District   None  4.934839e+07  University District  \n",
       "1457  University District   None  4.934839e+07  University District  \n",
       "1459  University District   None  4.934839e+07  University District  \n",
       "1461  University District   None  4.934839e+07  University District  \n",
       "1488  University District   None  4.934839e+07  University District  \n",
       "1489  University District   None  4.934839e+07  University District  \n",
       "1490  University District   None  4.934839e+07  University District  \n",
       "1491  University District   None  4.934839e+07  University District  \n",
       "1492  University District   None  4.934839e+07  University District  \n",
       "1493  University District   None  4.934839e+07  University District  \n",
       "1572  University District   None  4.934839e+07  University District  \n",
       "1576  University District   None  4.934839e+07  University District  \n",
       "1577  University District   None  4.934839e+07  University District  \n",
       "1613  University District   None  4.934839e+07  University District  \n",
       "\n",
       "[82 rows x 18 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhood_places = gpd.sjoin(gf, gfNeighborhood, how=\"inner\", op='within')\n",
    "nhood_places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82 Places in University District\n",
      "\n",
      "13                                           Trader Joe's\n",
      "64                           United States Postal Service\n",
      "302        University Branch - The Seattle Public Library\n",
      "314          Planned Parenthood - Roosevelt Health Center\n",
      "317               Pediatric Care Center at UWMC-Roosevelt\n",
      "318     General Internal Medicine Center at UWMC-Roose...\n",
      "319             University Of Washington Roosevelt Clinic\n",
      "320       Weight Loss Management Center at UWMC-Roosevelt\n",
      "321                      Seattle Children's Autism Center\n",
      "501                                                  UWMC\n",
      "502     University of Washington Medical Center Obstet...\n",
      "503     University of Washington: Department of Pediat...\n",
      "504                                     UW Medical Center\n",
      "968                    UW Medical Center Surgery Pavilion\n",
      "981                                            Vista Café\n",
      "984                                 Voula's Offshore Cafe\n",
      "985                                           The Rotunda\n",
      "986                                       Atrium Espresso\n",
      "988                                            Court Café\n",
      "990                           Husky Grind at Mercer Court\n",
      "991                                     Overpass Espresso\n",
      "994                                                 H-bar\n",
      "995                             Surgery Pavilion Espresso\n",
      "998                                             Buzz Cafe\n",
      "999                                          Bean & Bagel\n",
      "1000                       Husky Grind at District Market\n",
      "1005                                     Portage Bay Cafe\n",
      "1006                     City Grind Espresso at The Henry\n",
      "1013                                          Reboot Cafe\n",
      "1015                             Mary Gates Hall Espresso\n",
      "                              ...                        \n",
      "1115                              Bambu Desserts & Drinks\n",
      "1116                                    Sureshot Espresso\n",
      "1439                                          Rick's Cafe\n",
      "1441                                         Orin's Place\n",
      "1442                                                Oasis\n",
      "1443                                            Parnassus\n",
      "1444                                      The Supreme Cup\n",
      "1445                                          Beetle Cafe\n",
      "1446                                                西雅圖美心\n",
      "1447                                Slate Coffee Roasters\n",
      "1448                                             Wann Yen\n",
      "1450                      Ugly Mug Cafe & Coffee Roasters\n",
      "1451                                         Cafe Allegro\n",
      "1452                                       Public Grounds\n",
      "1453                                           Tower Café\n",
      "1454                                         Bulldog News\n",
      "1456                                     Cafe on the Ave.\n",
      "1457                                            Starbucks\n",
      "1459                                            Starbucks\n",
      "1461                                       Pineapple Café\n",
      "1488                              Higher Education Tutors\n",
      "1489                                           Dawg Bites\n",
      "1490                                      District Market\n",
      "1491                                               H Mart\n",
      "1492                                    Super Supplements\n",
      "1493                       Super Supplements - U-District\n",
      "1572                                              Ravenna\n",
      "1576                                    University Campus\n",
      "1577                        University District Northwest\n",
      "1613                             University of Washington\n",
      "Name: name_left, Length: 82, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('{} Places in {}\\n'.format(len(nhood_places), neighborhood))\n",
    "print(nhood_places.name_left)\n",
    "\n",
    "# NOTE how columns with the same row were automatically dealt with by \n",
    "# appending a _left or _right suffix!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name_left                      Trader Joe's\n",
       "address        4555 Roosevelt Way Northeast\n",
       "city_left                           Seattle\n",
       "lat                                 47.6627\n",
       "lng                                -122.318\n",
       "place_id        ChIJq6Rq_l8UkFQRyDDI__HHS40\n",
       "rating                                  4.5\n",
       "place_class                     supermarket\n",
       "type                                    NaN\n",
       "geometry        POINT (-122.31762 47.66267)\n",
       "index_right                               5\n",
       "id             Seattle:University District:\n",
       "city_right                          Seattle\n",
       "county                                 King\n",
       "nhood                   University District\n",
       "nested                                 None\n",
       "area                            4.93484e+07\n",
       "name_right              University District\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhood_places.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lots of directions to take this in!\n",
    "- Which neighborhood has the longest travel times?\n",
    "- What about ratio of straight-line distance to traveled distance?\n",
    "- Export data for a particular neighborhood\n",
    "- Incorporate another GIS dataset of your choosing!\n",
    "\n",
    "# Good luck from here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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