Mexico-Deaths.ipynb

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    "<a href=\"https://colab.research.google.com/github/stat10/DS10-Case-Studies/blob/master/Mexico-Deaths.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "import os\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Mexico for Spring Break or Death\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Mexico for spring break is dangerous?\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/test.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/news_clips_group.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What parties have a stake in the outcome?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
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   "source": [
    "- the public\n",
    "- travel agents\n",
    "- mexico tourism board\n",
    "- airlines\n",
    "- US government\n",
    "- US embassies\n",
    "- Schools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/Screen-Shot-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## What data do we need to know if this is true?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
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   "source": []
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  {
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   "source": [
    "## In class Exercise: Find a Data Source for this Online (2 min)\n",
    "\n",
    "In 2003, the Foreign Relations Authorization Act mandated that, \"to the maximum extent practicable, the US Department of State collect and make available on the Department's Bureau of Consular Affairs Internet [website](https://travel.state.gov/content/travel/en/international-travel/while-abroad/death-abroad1/death-statistics.html) certain information with respect to each United States citizen who dies in a foreign country from a non-natural cause. The information required is: \n",
    "\n",
    "1. the date of death; \n",
    "\n",
    "2. the locality where the death occurred; and \n",
    "\n",
    "3. the cause of death, including, if the death resulted from an act of terrorism, a statement disclosing that fact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/Screen2Shot-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We would select which country and time frame we are interested in viewing and select \"download\". This then saves the files. We have uploaded all of the data onto a website called \"Github\" to make it easy for you to run code on the data here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load the data for deaths in Mexico from 2002 to 2018 into this Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Read in the data and save it as a dataframe, we can take a look at the first few rows of information.\n",
    "\n",
    "You will notice that the data file has the three pieces of required informationn:\n",
    "\n",
    "\n",
    "Date, City, and Cause of Death\n",
    " \n",
    "We can also look at all the types of Cause of Death (remember these are \"unnatural deaths\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>City</th>\n",
       "      <th>Cause of Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/2/02</td>\n",
       "      <td>Guadalajara -  Jalisco -  Mexico</td>\n",
       "      <td>Veh. Accident-Auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/2/02</td>\n",
       "      <td>Tijuana -  Baja California Norte -  Mexico</td>\n",
       "      <td>Homicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/3/02</td>\n",
       "      <td>Hermosillo -  Sonora -  Mexico</td>\n",
       "      <td>Veh. Accident-Auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/6/02</td>\n",
       "      <td>Veracruz -  Veracruz -  Mexico</td>\n",
       "      <td>Suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/8/02</td>\n",
       "      <td>Puerto Vallarta -  Jalisco -  Mexico</td>\n",
       "      <td>Veh. Accident-Motorcycle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      Date                                        City  \\\n",
       "0  10/2/02            Guadalajara -  Jalisco -  Mexico   \n",
       "1  10/2/02  Tijuana -  Baja California Norte -  Mexico   \n",
       "2  10/3/02              Hermosillo -  Sonora -  Mexico   \n",
       "3  10/6/02              Veracruz -  Veracruz -  Mexico   \n",
       "4  10/8/02        Puerto Vallarta -  Jalisco -  Mexico   \n",
       "\n",
       "             Cause of Death  \n",
       "0        Veh. Accident-Auto  \n",
       "1                  Homicide  \n",
       "2        Veh. Accident-Auto  \n",
       "3                   Suicide  \n",
       "4  Veh. Accident-Motorcycle  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists('Data/deaths_mexico_2002_2019.csv'):\n",
    "    !wget 'https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Data/deaths_mexico_2002_2019.csv'\n",
    "    df_mexico = pd.read_csv('deaths_mexico_2002_2019.csv', encoding='latin-1')\n",
    "else:\n",
    "    df_mexico = pd.read_csv('Data/deaths_mexico_2002_2019.csv', encoding='latin-1')\n",
    "df_mexico.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load the data for all deaths abroad from 2002 to 2018 into this Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>City</th>\n",
       "      <th>Cause of Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/25/03</td>\n",
       "      <td>Shkin -  Afghanistan</td>\n",
       "      <td>Terrorist Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/25/03</td>\n",
       "      <td>Shkin -  Afghanistan</td>\n",
       "      <td>Terrorist Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8/29/04</td>\n",
       "      <td>Kabul -  Afghanistan</td>\n",
       "      <td>Terrorist Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8/29/04</td>\n",
       "      <td>Kabul -  Afghanistan</td>\n",
       "      <td>Terrorist Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/29/04</td>\n",
       "      <td>Kabul -  Afghanistan</td>\n",
       "      <td>Terrorist Action</td>\n",
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      "text/plain": [
       "       Date                  City    Cause of Death\n",
       "0  10/25/03  Shkin -  Afghanistan  Terrorist Action\n",
       "1  10/25/03  Shkin -  Afghanistan  Terrorist Action\n",
       "2   8/29/04  Kabul -  Afghanistan  Terrorist Action\n",
       "3   8/29/04  Kabul -  Afghanistan  Terrorist Action\n",
       "4   8/29/04  Kabul -  Afghanistan  Terrorist Action"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists('Data/deaths_all_2002_2019.csv'):\n",
    "    !wget 'https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Data/deaths_all_2002_2019.csv'\n",
    "    df_all = pd.read_csv('deaths_all_2002_2019.csv', encoding='latin-1')\n",
    "else:\n",
    "    df_all = pd.read_csv('Data/deaths_all_2002_2019.csv', encoding='latin-1')\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Look at types of Causes of Death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of different causes of death = 117\n",
      "\n",
      "['Air Accident' 'Armed Conflict' 'DROWNING' 'DROWNING-BEACH'\n",
      " 'DROWNING-Ocean' 'DROWNING-SEA' 'Disaster' 'Drowning' 'Drowning - Beach'\n",
      " 'Drowning - BeachåÂ\\x89ÛÊ' 'Drowning - Ocean' 'Drowning - OceanåÂ\\x89ÛÊ'\n",
      " 'Drowning - Pool' 'Drowning - PoolåÂ\\x89ÛÊ' 'Drowning - River'\n",
      " 'Drowning -Ocean' 'Drowning- BEACH' 'Drowning- LAKE' 'Drowning- Ocean'\n",
      " 'Drowning-BEACH' 'Drowning-Bay' 'Drowning-Beach' 'Drowning-Bridge'\n",
      " 'Drowning-CANAL' 'Drowning-LAKE' 'Drowning-Lake' 'Drowning-OCEAN'\n",
      " 'Drowning-Ocean' 'Drowning-POOL' 'Drowning-Pool' 'Drowning-RIVER'\n",
      " 'Drowning-River' 'Drowning-beach' 'DrowningåÂ\\x89ÛÊ' 'Drug-Related'\n",
      " 'Drug-related' 'Execution' 'Homicide' 'Homocide' 'Hostage-related'\n",
      " 'Hostage-related ' 'Maritime Accident' 'Natural Disaster' 'Other'\n",
      " 'Other Accid.-Fall' 'Other Accident' 'Other Accident - Fall'\n",
      " 'Other Accident - Mountain Climbing' 'Other Accident - Rafting'\n",
      " 'Other Accident - fall' 'Other Accident-' 'Other Accident- Hiking'\n",
      " 'Other Accident- fall' 'Other Accident-Fall' 'Other Accident-Mountain'\n",
      " 'Other Accident-fall' 'Other AccidentåÂ\\x89ÛÊ' 'Other accident'\n",
      " 'OtheråÂ\\x89ÛÊ Accident' 'Suicide' 'Terrorist Action' 'Train Accident'\n",
      " 'Under Investigation' 'Undetermined/Unknown' 'Undetermined/unknown'\n",
      " 'Unknown' 'Unknown/Undetermined' 'Veh Accident - Auto'\n",
      " 'Veh Accident - Motorcycle' 'Veh-Accident-Bus' 'Veh. Accid'\n",
      " 'Veh. Accid - Auto' 'Veh. Accid - Pedestr.' 'Veh. Accid- Auto'\n",
      " 'Veh. Accid- Bus' 'Veh. Accid- Other' 'Veh. Accid- Pedestr'\n",
      " 'Veh. Accid-Auto' 'Veh. Accid-Bus' 'Veh. Accid-Motorcy.'\n",
      " 'Veh. Accid-Other' 'Veh. Accid-Pedestr.' 'Veh. Accid-Pedestrian'\n",
      " 'Veh. Accid-Train' 'Veh. Accid-bike' 'Veh. Accident'\n",
      " 'Veh. Accident - Auto' 'Veh. Accident - Bus' 'Veh. Accident - Motorcycle'\n",
      " 'Veh. Accident -Auto' 'Veh. Accident- Auto' 'Veh. Accident- Bus'\n",
      " 'Veh. Accident- Pedestrian' 'Veh. Accident-Auto' 'Veh. Accident-Bus'\n",
      " 'Veh. Accident-Motorcycle' 'Veh. Accident-Other' 'Veh. Accident-Pedestr'\n",
      " 'Veh. Accident-Pedestrian' 'Veh. Accident-Train' 'Veh. Accident-other'\n",
      " 'Veh. AccidentåÂ\\x89ÛÊ' 'Veh.Accident-Auto' 'Veh.Accident-Bus'\n",
      " 'Vehic. Accident- Other' 'Vehic. Accident-Auto'\n",
      " 'Vehic. Accident-Motorcycle' 'Vehic. Accident-Train'\n",
      " 'Vehicle Accident - Auto' 'Vehicle Accident - Bus'\n",
      " 'Vehicle Accident - Motorcycle' 'Vehicle Accident - Other'\n",
      " 'Vehicle Accident - Pedestrian' 'Vehicle Accident - Train'\n",
      " 'Vehicle Acid.-Auto' 'homicide' 'other Accident']\n"
     ]
    }
   ],
   "source": [
    "cause_of_deaths = df_all['Cause of Death'].unique()\n",
    "cause_of_deaths.sort()\n",
    "print('Number of different causes of death =', len(cause_of_deaths))\n",
    "print('')\n",
    "print(cause_of_deaths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What are the potential issues with the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's look at how many deaths there were in Mexico in 2018 versus how many deaths in total."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Number of deaths in Mexico in 2018 = 196"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Number of deaths in total abroad in 2018 = 724"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of deaths in Mexico in 2018 = 196\n"
     ]
    }
   ],
   "source": [
    "## Code to find number of deaths in Mexico in 2018\n",
    "\n",
    "## make the date column recognzied as a date for mexico\n",
    "df_mexico['Date'] = pd.to_datetime(df_mexico['Date'])\n",
    "start_date = '2018-01-01'\n",
    "end_date = '2018-12-31'\n",
    "mask = (df_mexico['Date'] >= start_date) & (df_mexico['Date'] <= end_date)\n",
    "df_mexico_2018 = df_mexico.loc[mask]\n",
    "deaths_mexico_2018 = len(df_mexico_2018.index)\n",
    "print('Number of deaths in Mexico in 2018 =', deaths_mexico_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of deaths in total abroad in 2018 = 724\n"
     ]
    }
   ],
   "source": [
    "## Code to find number of deaths in total abroad in 2018\n",
    "\n",
    "## make the date column recognzied as a date for mexico\n",
    "df_all['Date'] = pd.to_datetime(df_all['Date'])\n",
    "start_date = '2018-01-01'\n",
    "end_date = '2018-12-31'\n",
    "mask = (df_all['Date'] >= start_date) & (df_all['Date'] <= end_date)\n",
    "df_all_2018 = df_all.loc[mask]\n",
    "deaths_all_2018 = len(df_all_2018.index)\n",
    "print('Number of deaths in total abroad in 2018 =', deaths_all_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What do we need to look at in order to understand if this is true?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### We can see some serious issue with the data when we try to do that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Air Accident' 'DROWNING-BEACH' 'DROWNING-Ocean' 'Disaster' 'Drowning'\n",
      " 'Drowning - Ocean' 'Drowning - OceanåÂ\\x89ÛÊ' 'Drowning - Pool'\n",
      " 'Drowning - PoolåÂ\\x89ÛÊ' 'Drowning -Ocean' 'Drowning- BEACH'\n",
      " 'Drowning-CANAL' 'Drowning-OCEAN' 'Drowning-POOL' 'Drowning-Pool'\n",
      " 'Drowning-RIVER' 'Drug-Related' 'Execution' 'Homicide' 'Homocide'\n",
      " 'Maritime Accident' 'Natural Disaster' 'Other Accid.-Fall'\n",
      " 'Other Accident' 'OtheråÂ\\x89ÛÊ Accident' 'Suicide'\n",
      " 'Undetermined/Unknown' 'Unknown/Undetermined' 'Veh. Accid'\n",
      " 'Veh. Accid-Auto' 'Veh. Accid-Bus' 'Veh. Accid-Motorcy.'\n",
      " 'Veh. Accid-Other' 'Veh. Accid-Pedestr.' 'Veh. Accident'\n",
      " 'Veh. Accident-Auto' 'Veh. Accident-Bus' 'Veh. Accident-Motorcycle'\n",
      " 'Veh. Accident-Other' 'Veh. Accident-Pedestr' 'Veh. Accident-Pedestrian'\n",
      " 'Vehicle Accident - Auto' 'Vehicle Accident - Bus'\n",
      " 'Vehicle Accident - Motorcycle' 'Vehicle Accident - Other'\n",
      " 'Vehicle Accident - Pedestrian' 'homicide']\n"
     ]
    }
   ],
   "source": [
    "cause_of_deaths = df_mexico['Cause of Death'].unique()\n",
    "cause_of_deaths.sort()\n",
    "print(cause_of_deaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Air Accident' 'Drowning' 'Drug-Related' 'Homicide' 'Other Accident'\n",
      " 'Suicide' 'Undetermined/Unknown' 'Veh. Accid-Auto' 'Veh. Accid-Motorcy.'\n",
      " 'Veh. Accid-Other' 'Veh. Accid-Pedestr.']\n"
     ]
    }
   ],
   "source": [
    "cause_of_deaths_2018 = df_mexico_2018['Cause of Death'].unique()\n",
    "cause_of_deaths_2018.sort()\n",
    "print(cause_of_deaths_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Number of homicides in 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Number of homicides in Mexico in 2018 = 67"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Number of homicides in total abroad in 2018 = 132"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of homicides in Mexico in 2018 = 67\n"
     ]
    }
   ],
   "source": [
    "## Code to find number of homicides in Mexico in 2018\n",
    "\n",
    "hom_mexico_2018 = (df_mexico_2018['Cause of Death'].values == 'Homicide').sum()\n",
    "print('Number of homicides in Mexico in 2018 =', hom_mexico_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of homicides in total abroad in 2018 = 132\n"
     ]
    }
   ],
   "source": [
    "## Code to find number of homicides in total abroad in 2018\n",
    "\n",
    "hom_all_2018 = (df_all_2018['Cause of Death'].values == 'Homicide').sum()\n",
    "print('Number of homicides in total abroad in 2018 =', hom_all_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview3.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Load Italy deaths data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>Date</th>\n",
       "      <th>City</th>\n",
       "      <th>Cause of Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/4/02</td>\n",
       "      <td>'strada Provinciale 99' -  Km. 1.3 -  Tortona ...</td>\n",
       "      <td>Veh. Accident-Auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/13/02</td>\n",
       "      <td>Venice -  Italy</td>\n",
       "      <td>Other Accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/30/02</td>\n",
       "      <td>Perugia -  Italy</td>\n",
       "      <td>Suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/03</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Other Accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/7/03</td>\n",
       "      <td>Reggio Calabria -  Italy</td>\n",
       "      <td>Suicide</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date                                               City  \\\n",
       "0   11/4/02  'strada Provinciale 99' -  Km. 1.3 -  Tortona ...   \n",
       "1  11/13/02                                    Venice -  Italy   \n",
       "2  11/30/02                                   Perugia -  Italy   \n",
       "3    1/4/03                                              Italy   \n",
       "4    5/7/03                           Reggio Calabria -  Italy   \n",
       "\n",
       "       Cause of Death  \n",
       "0  Veh. Accident-Auto  \n",
       "1      Other Accident  \n",
       "2             Suicide  \n",
       "3      Other Accident  \n",
       "4             Suicide  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists('Data/deaths_italy_2002_2019.csv'):\n",
    "    !wget 'https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Data/deaths_italy_2002_2019.csv'\n",
    "    df_italy = pd.read_csv('deaths_italy_2002_2019.csv', encoding='latin-1')\n",
    "else:\n",
    "    df_italy = pd.read_csv('Data/deaths_italy_2002_2019.csv', encoding='latin-1')\n",
    "df_italy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Find number of deaths and homicies Italy in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of deaths in Italy in 2018 = 3\n",
      "Number of homicides in Italy in 2018 = 0\n"
     ]
    }
   ],
   "source": [
    "df_italy['Date'] = pd.to_datetime(df_italy['Date'])\n",
    "start_date = '2018-01-01'\n",
    "end_date = '2018-12-31'\n",
    "mask = (df_italy['Date'] >= start_date) & (df_italy['Date'] <= end_date)\n",
    "df_italy_2018 = df_italy.loc[mask]\n",
    "deaths_italy_2018 = len(df_italy_2018.index)\n",
    "print('Number of deaths in Italy in 2018 =', deaths_italy_2018)\n",
    "hom_italy_2018 = (df_italy_2018['Cause of Death'].values == 'Homicide').sum()\n",
    "print('Number of homicides in Italy in 2018 =', hom_italy_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Load Dominican Republic deaths data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>Date</th>\n",
       "      <th>City</th>\n",
       "      <th>Cause of Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/29/02</td>\n",
       "      <td>Road Santana To Nizao -  Bani -  Dominican Rep...</td>\n",
       "      <td>Other Accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/4/02</td>\n",
       "      <td>San Isidro Air Base -  Santo Domingo -  Domini...</td>\n",
       "      <td>Air Accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/24/02</td>\n",
       "      <td>Santo Domingo -  Dominican Republic</td>\n",
       "      <td>Homicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/02</td>\n",
       "      <td>Provincia La Vega -  Dominican Republic</td>\n",
       "      <td>Veh. Accident-Auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/22/02</td>\n",
       "      <td>On The Road To La Romana -  Dominican Republic</td>\n",
       "      <td>Veh. Accident-Auto</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date                                               City  \\\n",
       "0  10/29/02  Road Santana To Nizao -  Bani -  Dominican Rep...   \n",
       "1   11/4/02  San Isidro Air Base -  Santo Domingo -  Domini...   \n",
       "2  11/24/02                Santo Domingo -  Dominican Republic   \n",
       "3  12/11/02            Provincia La Vega -  Dominican Republic   \n",
       "4  12/22/02     On The Road To La Romana -  Dominican Republic   \n",
       "\n",
       "       Cause of Death  \n",
       "0      Other Accident  \n",
       "1        Air Accident  \n",
       "2            Homicide  \n",
       "3  Veh. Accident-Auto  \n",
       "4  Veh. Accident-Auto  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists('Data/deaths_DR_2002_2019.csv'):\n",
    "    !wget 'https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Data/deaths_DR_2002_2019.csv'\n",
    "    df_dr = pd.read_csv('deaths_DR_2002_2019.csv', encoding='latin-1')\n",
    "else:\n",
    "    df_dr = pd.read_csv('Data/deaths_DR_2002_2019.csv', encoding='latin-1')\n",
    "df_dr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Find number of deaths and homicies in the Dominican Republic in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of deaths in Dominican Republic in 2018 = 13\n",
      "Number of homicides in Dominican Republic in 2018 = 3\n"
     ]
    }
   ],
   "source": [
    "df_dr['Date'] = pd.to_datetime(df_dr['Date'])\n",
    "start_date = '2018-01-01'\n",
    "end_date = '2018-12-31'\n",
    "mask = (df_dr['Date'] >= start_date) & (df_dr['Date'] <= end_date)\n",
    "df_dr_2018 = df_dr.loc[mask]\n",
    "deaths_dr_2018 = len(df_dr_2018.index)\n",
    "print('Number of deaths in Dominican Republic in 2018 =', deaths_dr_2018)\n",
    "hom_dr_2018 = (df_dr_2018['Cause of Death'].values == 'Homicide').sum()\n",
    "print('Number of homicides in Dominican Republic in 2018 =', hom_dr_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview4.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What other data do we need?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Tourism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exercise:\n",
    "\n",
    "Find the Data Source online (2 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Monthly U.S. citizen departures are collected and [reported in Tourism Industries U.S. International Air Travel Statistics (I-92 data)](https://travel.trade.gov/research/monthly/departures/) Program. Each month NTTO processes and reports outbound figures in the \"U.S. International Air Passenger Statistics Report\". To help the public better understand the international market we produce a monthly table on-line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview5.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Read in and plot tourism data from 2002 to 2018 from Europe, Caribbean, South America, Mexico, Canada, and total tourism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>year</th>\n",
       "      <th>europe</th>\n",
       "      <th>caribbean</th>\n",
       "      <th>southamer</th>\n",
       "      <th>mexico</th>\n",
       "      <th>mexico-air</th>\n",
       "      <th>canada</th>\n",
       "      <th>canada-air</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>10677881</td>\n",
       "      <td>4324345</td>\n",
       "      <td>1675376</td>\n",
       "      <td>4175927</td>\n",
       "      <td>4175927</td>\n",
       "      <td>3792550</td>\n",
       "      <td>3792550</td>\n",
       "      <td>31365865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>10992891</td>\n",
       "      <td>4941373</td>\n",
       "      <td>1845248</td>\n",
       "      <td>4562351</td>\n",
       "      <td>4562351</td>\n",
       "      <td>3502064</td>\n",
       "      <td>3502064</td>\n",
       "      <td>32516222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>12243834</td>\n",
       "      <td>5165041</td>\n",
       "      <td>1985898</td>\n",
       "      <td>5223552</td>\n",
       "      <td>5223552</td>\n",
       "      <td>3922702</td>\n",
       "      <td>3922702</td>\n",
       "      <td>36497344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>12552629</td>\n",
       "      <td>5397458</td>\n",
       "      <td>2180777</td>\n",
       "      <td>5672740</td>\n",
       "      <td>5672740</td>\n",
       "      <td>3912198</td>\n",
       "      <td>3912198</td>\n",
       "      <td>38372404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>12995893</td>\n",
       "      <td>5780787</td>\n",
       "      <td>2317921</td>\n",
       "      <td>5747999</td>\n",
       "      <td>5747999</td>\n",
       "      <td>3861639</td>\n",
       "      <td>3861639</td>\n",
       "      <td>39758010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    europe  caribbean  southamer   mexico  mexico-air   canada  \\\n",
       "0  2002  10677881    4324345    1675376  4175927     4175927  3792550   \n",
       "1  2003  10992891    4941373    1845248  4562351     4562351  3502064   \n",
       "2  2004  12243834    5165041    1985898  5223552     5223552  3922702   \n",
       "3  2005  12552629    5397458    2180777  5672740     5672740  3912198   \n",
       "4  2006  12995893    5780787    2317921  5747999     5747999  3861639   \n",
       "\n",
       "   canada-air     total  \n",
       "0     3792550  31365865  \n",
       "1     3502064  32516222  \n",
       "2     3922702  36497344  \n",
       "3     3912198  38372404  \n",
       "4     3861639  39758010  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "travel = pd.DataFrame(columns=['year', 'europe', 'caribbean', 'southamer', 'mexico', \n",
    "                               'mexico-air', 'canada', 'canada-air', 'total'])\n",
    "travel['year'] = pd.date_range(start='2002', end='2019', freq='Y').year\n",
    "travel['europe'] = ([10677881, 10992891, 12243834, 12552629, 12995893, 13329777, 12505627, 11979018, \n",
    "                  11103708, 10825923, 11244637, 11407988, 11892216, 12598860, 13614365, 15792769, 17742258])\n",
    "travel['caribbean'] = ([4324345, 4941373, 5165041, 5397458, 5780787, 5649454, 5686838, 5530518, 5716066, \n",
    "                     6031974, 6435343, 6545774, 7171872, 7648197, 7878885, 8320516, 8702217])\n",
    "travel['southamer'] = ([1675376, 1845248, 1985898, 2180777, 2317921, 2463366, 2546511, 2573688, 2129024, \n",
    "                     1653593, 1702869, 1735188, 1771505, 1869463, 2010477, 1925300,  2104107])\n",
    "travel['mexico'] = ([4175927, 4562351, 5223552, 5672740, 5747999, 5799865, 5889363, 5263604, 5383108, \n",
    "                  20084000, 20366668, 20546361, 25881635, 28733286, 31194398, 34946950, 36923011])\n",
    "travel['mexico-air'] = ([4175927, 4562351, 5223552, 5672740, 5747999, 5799865, 5889363, 5263604, \n",
    "                         5383108, 5537383, 5772321, 6219043, 6930590, 7993665, 8933753 , 9810713, 10075187])\n",
    "travel['canada'] = ([3792550, 3502064, 3922702, 3912198, 3861639, 3770453, 3581759, 3280273, 3465111, \n",
    "                     11596943, 11853981, 12007976, 11514572, 12474500, 13894867, 14280387, 14341438])\n",
    "travel['canada-air'] = ([3792550, 3502064, 3922702, 3912198, 3861639, 3770453, 3581759, 3280273, \n",
    "                         3465111, 3518788, 3534659, 3770362, 3410123, 3633045, 4515539, 4815830 , 4584209])\n",
    "travel['total'] = ([31365865, 32516222, 36497344, 38372404, 39758010, 40798278, 40259650, 38844301, 37354842,\n",
    "                    58703951, 60722794, 61569800, 68176355, 73997139, 80226167, 87554795, 93038257])\n",
    "travel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(figsize=(10,6))\n",
    "ax1.plot(travel['year'], travel['europe'] / 1000000, 'o--', label='Europe')\n",
    "ax1.plot(travel['year'], travel['caribbean'] / 1000000, 'o--', label='Caribbean')\n",
    "ax1.plot(travel['year'], travel['southamer'] / 1000000, 'o--', label='South America')\n",
    "ax1.plot(travel['year'], travel['mexico'] / 1000000, 'o--', label='Mexico')\n",
    "ax1.plot(travel['year'], travel['canada'] / 1000000, 'o--', label='Canada')\n",
    "ax1.plot(travel['year'], travel['total'] / 1000000, 'o--', label='Total')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Travelers in Millions')\n",
    "ax1.legend()\n",
    "plt.show(fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What do you notice in this graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "A huge spike between 2010 and 2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview6.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "However, if we just take a snapshot of time and look at 2018 and compare Mexico's travel versus deaths we start to see something interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview7.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of US Travelers to Mexico from 2002 to 2018:\n",
      "0     13.0\n",
      "1     14.0\n",
      "2     14.0\n",
      "3     15.0\n",
      "4     14.0\n",
      "5     14.0\n",
      "6     15.0\n",
      "7     14.0\n",
      "8     14.0\n",
      "9     34.0\n",
      "10    34.0\n",
      "11    33.0\n",
      "12    38.0\n",
      "13    39.0\n",
      "14    39.0\n",
      "15    40.0\n",
      "16    40.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# proportion of US travellers to mexico \n",
    "prop_mexico_travel = travel['mexico'] / travel['total']\n",
    "# proportion of US travellers to mexico by air\n",
    "prop_mexico_air_travel = travel['mexico-air'] / travel['total']\n",
    "\n",
    "print('Proportion of US Travelers to Mexico from 2002 to 2018:') \n",
    "print(round(prop_mexico_travel * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Separate the deaths in Mexico by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>year</th>\n",
       "      <th>deaths</th>\n",
       "      <th>homicides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>77</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>199</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>202</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>236</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>195</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  deaths  homicides\n",
       "0  2002      77          9\n",
       "1  2003     199         40\n",
       "2  2004     202         37\n",
       "3  2005     236         50\n",
       "4  2006     195         43"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = []\n",
    "ndeaths = []\n",
    "nhomicides = []\n",
    "for y in range(2002, 2019, 1):\n",
    "    start_date = str(y) + '-01-01'\n",
    "    end_date = str(y) + '-12-31'\n",
    "    df_mexico_year = df_mexico.loc[(df_mexico['Date'] >= start_date) & (df_mexico['Date'] <= end_date)]\n",
    "    nd = len(df_mexico_year.index)\n",
    "    nhom = (df_mexico_year['Cause of Death'].values == 'Homicide').sum()\n",
    "    year.append(y)\n",
    "    ndeaths.append(nd)\n",
    "    nhomicides.append(nhom)\n",
    "data = {'year':year, 'deaths':ndeaths, 'homicides':nhomicides}\n",
    "mexico_deaths = pd.DataFrame(data)\n",
    "mexico_deaths.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Separate all deaths abroad by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>year</th>\n",
       "      <th>deaths</th>\n",
       "      <th>homicides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>228</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>756</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>865</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>838</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>653</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  deaths  homicides\n",
       "0  2002     228         35\n",
       "1  2003     756        120\n",
       "2  2004     865        128\n",
       "3  2005     838        142\n",
       "4  2006     653        104"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = []\n",
    "ndeaths = []\n",
    "nhomicides = []\n",
    "for y in range(2002, 2019, 1):\n",
    "    start_date = str(y) + '-01-01'\n",
    "    end_date = str(y) + '-12-31'\n",
    "    df_all_year = df_all.loc[(df_all['Date'] >= start_date) & (df_all['Date'] <= end_date)]\n",
    "    nd = len(df_all_year.index)\n",
    "    nhom = (df_all_year['Cause of Death'].values == 'Homicide').sum()\n",
    "    year.append(y)\n",
    "    ndeaths.append(nd)\n",
    "    nhomicides.append(nhom)\n",
    "data = {'year':year, 'deaths':ndeaths, 'homicides':nhomicides}\n",
    "all_deaths = pd.DataFrame(data)\n",
    "all_deaths.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Proportion of US travelers deaths in Mexico from 2002 to 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of US Travelers deaths in Mexico from 2002 to 2018:\n",
      "0     34.0\n",
      "1     26.0\n",
      "2     23.0\n",
      "3     28.0\n",
      "4     30.0\n",
      "5     31.0\n",
      "6     30.0\n",
      "7     26.0\n",
      "8     26.0\n",
      "9     29.0\n",
      "10    28.0\n",
      "11    25.0\n",
      "12    30.0\n",
      "13    27.0\n",
      "14    31.0\n",
      "15    30.0\n",
      "16    27.0\n",
      "Name: deaths, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "prop_deaths_mexico = mexico_deaths['deaths'] / all_deaths['deaths']\n",
    "print('Proportion of US Travelers deaths in Mexico from 2002 to 2018:')\n",
    "print(round(prop_deaths_mexico * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plot of deaths vs travellers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(figsize=(10,6))\n",
    "ax2.plot(travel['year'], prop_mexico_travel * 100, 'o--', label='Travelling to Mexico')\n",
    "ax2.plot(travel['year'], prop_deaths_mexico * 100, 'o--', label='Deaths in Mexico')\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Proportion of US citizens')\n",
    "ax2.legend()\n",
    "plt.show(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### We need to remove 2002 to 2010 because the collection was different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(figsize=(10,6))\n",
    "ax2.plot(travel['year'][9:17], prop_mexico_travel[9:17] * 100, 'o--', label='Travelling to Mexico')\n",
    "ax2.plot(travel['year'][9:17], prop_deaths_mexico[9:17] * 100, 'o--', label='Deaths in Mexico')\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Proportion of US citizens')\n",
    "ax2.legend()\n",
    "plt.show(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### We also discussed the idea of homicides versus all deaths so we can plot the proportion of homicides as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop_homicides_mexico = mexico_deaths['homicides'] / all_deaths['homicides']\n",
    "fig3, ax3 = plt.subplots(figsize=(10,6))\n",
    "ax3.plot(travel['year'][9:17], prop_mexico_travel[9:17] * 100, 'o--', label='Travelling to Mexico')\n",
    "ax3.plot(travel['year'][9:17], prop_deaths_mexico[9:17] * 100, 'o--', label='Deaths in Mexico')\n",
    "ax3.plot(travel['year'][9:17], prop_homicides_mexico[9:17] * 100, 'o--', label='Homicides in Mexico')\n",
    "ax3.set_xlabel('Year')\n",
    "ax3.set_ylabel('Proportion of US citizens')\n",
    "ax3.legend()\n",
    "plt.show(fig3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What data issues do we still have – are these fair comparisons?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- Need to look at countries close to US i.e. canada \n",
    "- drug cartels etc in mexico so maybe only fair to actaully look at tourist locations \n",
    "- maybe only look at by air"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Read in and plot the proportion of deaths in Canada versus the proportion of travel to Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>year</th>\n",
       "      <th>deaths</th>\n",
       "      <th>homicides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  deaths  homicides\n",
       "0  2002       5          0\n",
       "1  2003      23          0\n",
       "2  2004      20          3\n",
       "3  2005      19          0\n",
       "4  2006       7          1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists('Data/deaths_canada_2002_2019.csv'):\n",
    "    !wget 'https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Data/deaths_canada_2002_2019.csv'\n",
    "    df_canada = pd.read_csv('deaths_canada_2002_2019.csv', encoding='latin-1')\n",
    "else:\n",
    "    df_canada = pd.read_csv('Data/deaths_canada_2002_2019.csv', encoding='latin-1')\n",
    "df_canada['Date'] = pd.to_datetime(df_canada['Date'])\n",
    "year = []\n",
    "ndeaths = []\n",
    "nhomicides = []\n",
    "for y in range(2002, 2019, 1):\n",
    "    start_date = str(y) + '-01-01'\n",
    "    end_date = str(y) + '-12-31'\n",
    "    df_canada_year = df_canada.loc[(df_canada['Date'] >= start_date) & (df_canada['Date'] <= end_date)]\n",
    "    nd = len(df_canada_year.index)\n",
    "    nhom = (df_canada_year['Cause of Death'].values == 'Homicide').sum()\n",
    "    year.append(y)\n",
    "    ndeaths.append(nd)\n",
    "    nhomicides.append(nhom)\n",
    "data = {'year':year, 'deaths':ndeaths, 'homicides':nhomicides}\n",
    "canada_deaths = pd.DataFrame(data)\n",
    "canada_deaths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LeWFiMl1bh9M81JkYkXfcu6uPi8iZOXz4MElJSSQlJdG6dWtiY2Pdx2Xro9W2AQMGsHatsylQXFwcmZmZlJSUMHDgwFq5/+rVq1m4cOFpX7d8+XIGDBhA165d6d27N5MmTSI/33uz3ctveu+N8z116623smXLllq/78n0GFSkATHGMLpHG0b3aAPAgewCRj79Fdf3bcfdQy5wz3wVkTOw/l34bAZk7YHIOBj2CPSsvKG5p1q0aOFOnB599FHCwsK49957K5xjrcVai4+P9/pGfH19Wbp0aa3ca/Xq1aSlpZ3Wlk0ZGRmMHz+e9957j759+2Kt5d133+XYsWMEB1e/nFRTUNUiwQAlJSX4+tbepDH1rIk0YMYYhsS35IUlPzHkr0uYu2oPpaVNZ7kdkTqz/l34z28hazdgndf//NYpr2Xbtm0jISGBiRMnkpiYSEZGBpMmTSIlJYXExERmzJgBwEcffVRhQ/XyvT8ff/wx/fr1Izk5mfHjx9e4eXpxcTFRUVHuewwbNoyxY8cSHx/PTTfd5D7vww8/JD4+nj59+jBlypRKPU35+fnMmDGD2bNnk5SUxNy5czl06BBjxoyhZ8+e9O/fn7S0tErtP/fcc9x222307dsXcH5ujR8/nujoaJYvX06/fv3o3bs3qampbN3qLLz+2muvMW7cOEaOHEnnzp154IEH3Per6rMCWLBgAfHx8SQnJ/PBBx+4y6tr42RZWVmMGjWK+Ph4fv3rX2Ot5ZVXXqmQYL/44otMmzat0rXVxVTW01n2Z3DPPffQs2dPVqxYUWUMZ6ws628KX3369LEiTdGqnUfsmOe/tu3v+8iOef5rm3+8uL5DEql3GzdurFjw+ujKX9+94tT9LcHa6RGVvx7v4NQfO1T52tMwffp0+8QTT1hrrd26das1xtjvv//eXX/48GFrrbVFRUV2wIABdsOGDbawsNDGxcXZvLw8a621t99+u50zZ47dv3+/HTRokM3NzbXWWvunP/3J/vnPf7bWWpuammrXrFljrbU2NjbWHj161BYVFdnIyEhrrbWLFy+2UVFRNj093RYXF9uUlBT77bff2tzcXBsbG2t37NhhS0tL7bhx4+xVV11V6ft49dVX7dSpU93Hd955p/3Tn/5krbV20aJFtqrfs1deeaX96KOPqvxcMjMzbVFRkbXW2o8//thed9117nYuuOACm5WVZfPy8mxcXJxNT0+v9rMqi3/btm22tLTUjh071h1/dW2Ut3jxYhscHGx//vlnW1xcbIcMGWLnzZtns7KybMeOHd3XX3jhhZX/XlUTU/k/j6KiIgvYf//731V+DtZW8ffVWgustB7kN3qmItIIJLdrxry7+jN/bTqb9+W415zLKSgi/BTry4kIkJ1edXn+Ea8016lTJ1JSTqx1OmfOHGbOnElxcTF79+5l48aNJCQkcOmll7JgwQKuuuoqFi5cyNNPP83ixYvZuHEj/fv3B+D48eMMGDDA47YvvvhiYmJiAEhKSmLHjh34+fkRHx9P+/btAbj++ut54403Tnmvr7/+mgULFgAwYsQIbrnlFnJzcytt5l6dzMxMbrrpJn766adKdcOHDyciIgKArl27smvXLmJiYqr8rPLy8ujSpQudOjk73kycONEdf01tnPy5dOjQAYAJEybw9ddfc/XVVzNo0CA+/vhjOnbsiK+vb5ULLVf351deQEAA11xzjUefy+lSsibSSPj4GMYmx7mPN+zN4rqXvmXyJZ2YNKjjKRcNFmnybl1QfV1knOsR6MnlbZ3X0BY1X3+ayiczW7du5ZlnnmHFihVERUVxww03uDdRnzBhAq+99hohISH069eP0NBQrLVcdtllvPnmm2fUdmBgoPu9r68vxcXenaSUmJjIqlWruPzyyyvVPfTQQ4wcOZK7776bbdu2VRgLV1WcNX1W1ampjfKMMVUe33777Tz55JN06NCBW2+9tdJ1nsYUHBxcqY3aojFrIo1UVEgAl8RH8+TiHxn2ty/5aP1ebBPaPk6kVg17BPxPGuzuH+yUe1l2djbh4eFERESQkZHBokWL3HVDhw7lu+++Y+bMmUyYMAGA/v378+WXX7J9+3YAcnNzqx2H5amEhAS2bNnC7t27sdbyzjvvVHleeHg4OTk57uOBAwcye/ZswBkPFxsbW6lXbcqUKcycOZOy7R6ttbz33nscPHiQrKwsYmOd9SVnzZp1yjir+6wSEhLYunUrP//8M9Za5syZ477G0zaWL1/Orl27KCkp4d1333X3VqampvLTTz/x3nvvMX78eI9jqktK1kQaqdioYF6Y2Ie3J11MRLA/v3lrDTf/43slbCJV6XkdXPmsqyfNOK9XPntWs0E9lZycTEJCAl27duWmm24iNTXVXefn58eoUaNYvHgxo0ePBqBVq1bMnDmT8ePH06tXL/r378+PP/54VjGEhITw/PPPM3z4cFJSUoiKiiIyMrLSeUOHDmXdunX07t2buXPnMmPGDL799lt69uzJI488UuXsx5iYGN566y2mTp1K165dSUhI4PPPPycsLIz77ruPadOmkZyc7NHPpuo+q5CQEF566SVGjRpFSkoKbdq0cV/jaRt9+/blzjvvJCEhgfj4eMaMGeOuGzduHIMGDaryM6npz6+uaCN3kSagpNTyzve7yTtezO0DO2KtJSu/yL2JvUhTpI3cT8+xY8cICwvDWsvkyZPp0aMHU6ZMqe+wGoTLLruMBx54gEsuucRrbZzNRu7qWRNpAnx9DL+8qB23D+wIwKebDjDg/33By1/+RGFxySmuFpFzwYsvvkhSUhIJCQnk5+dzxx131HdI9e7w4cN07tyZZs2aeTVRO1vqWRNpgn4+lMufPtrIZ5sP0KFFCA9fnsCwbi29NvhVpD6oZ00aE/WsiUgF558XysxbLmTWrRfi62O4/Y2V/P69dfUdloiInAEt3SHShA2Ob0nqBefxr+U7aRHmTJMvKiklt7BY49lERBoJ9ayJNHH+vj7cmno+Y3o5i2S++e1OBv91Cf/8ZgfFJaX1HJ2IiJyKkjWRc0z/C1qQ0CaC6R9uYPSzS1m69WB9hyQiIjVQsiZyjunaOoLZt1/Eyzf2oaColBtnruCx/26q77BEGiVfX1+SkpJITEykV69e/O1vf6O09Mx6rDMzM3nhhRfcx0uWLOGKK644o3t9+OGHPP7446d1zb59+5gwYQKdOnWiT58+jB49+qzXdzuVsLAwr96/qdCYNZFzkDGGkYmtGRwfzetf7yC5XRTg7DUKaL9RaZIWbF/AM6ufYV/uPlqHtmZq8lQu71h5i6TTERwczNq1awE4cOAAv/zlL8nOzuYPf/jDad+rLFm7++67zyomgDFjxlRY9PVUrLVcc8013Hzzzbz99tsArFu3jv3799OlS5ezjkfOjnrWRM5hgX6+3DW4Exd1bAHAk4t/ZMhfl/DO97soKW06y/qILNi+gEe/eZSM3AwslozcDB795lEWbK+9/UBbtmzJK6+8wvPPP4+1lpKSEqZNm8aFF15Iz549efnllwFncdphw4aRnJxMjx49+OCDDwC4//77+emnn0hKSmLatGnuc8eNG0fXrl2ZOHGie4X++++/n4SEBHr27Mm9995bKZZZs2bxm9/8BoBbbrmF3/72t/Tv35+OHTsyd+7cSud/8cUX+Pv7c+edd7rLevXqxcCBA6uNd8eOHXTr1o077riDxMRERowYQX5+PgCvvvoqF154Ib169eLaa68lLy8PgJ9//pl+/frRo0cPHn74YXdb1bUhDvWsiYjb1UmxrN+TxX3//oE3l+/kkSsS6Xt+8/oOS8Qjty6svAn3yA4jmdB1Ak+vepqCkoqbbxeUFPDYise4vOPlHC04yu+W/K5C/T8uq7y10ql07NiRkpISDhw4wAcffEBkZCTff/89hYWFpKamMmLECNq2bcu8efOIiIjg0KFDXHzxxYwZM4bHH3+ctLQ0d0/dkiVLWLNmDRs2bCAmJobU1FSWLVtGt27dmDdvHps3b8YYQ2Zm5injysjI4Ouvv2bz5s2MGTOGcePGVahPS0ujT58+VV4bFBRUZbzgbHI+Z84cXn31Va677jr+/e9/c8MNNzB27Fj3orsPP/wwM2fOZMqUKUydOpW77rqLm266ib///e+nbENrQzrUsyYibr3aRjH3zn48MyGJw8eOc93L3/La0u31HZbIWduft7/K8qzCLK+1+cknn/DGG2+QlJTERRddxOHDh9m6dSvWWh588EF69uzJ8OHDSU9PZ//+quPr27cvcXFx+Pj4kJSUxI4dO4iMjCQoKIjbbruN999/n5CQkFPGcvXVV+Pj40NCQkK1bVWnpnjPP/98kpKSAOjTpw87duwAnORv4MCB9OjRg9mzZ7NhwwYAli1bxvXXXw/AjTfe6FEbop41ETmJMYarkmK5NKEVL3+5neHdWgFwIKeAsEA/QgL0Y0Mappp6wlqHtiYjN6NSeZtQZ0PwZkHNzqgn7WTbt2/H19eXli1bYq3lueeeY+TIkRXOmTVrFgcPHmTVqlX4+/vToUMHCgoKqrxfYGCg+72vry/FxcX4+fmxYsUKPvvsM+bOncvzzz/P559/XmNc5e9T1c5FiYmJVT4eBZg9e3a18Z4cX9lj0FtuuYX58+fTq1cvZs2axZIlS9znVdVbVlMbop41EalGSIAf/+fSLnQ4LxSAB99PY+hfv+SDtelV/rAXacimJk8lyDeoQlmQbxBTk6fWWhsHDx7kzjvv5De/+Y0ziWfkSF588UWKipyJOz/++CO5ublkZWXRsmVL/P39+eKLL9i5cycA4eHh5OTknLKdY8eOkZWVxejRo3nqqadYt+7sdycZOnQohYWFvPLKK+6y9evXs3Tp0mrjrUlOTg5t2rShqKiI2bNnu8tTU1PdExjKl59JG+cSJWsi4pHJl3QkOjyQqW+v5doXv2Hd7lOPkxFpKC7veDmP9n+UNqFtMBjahLbh0f6PnvVs0Pz8fPfSHcOHD2fEiBFMnz4dgNtvv52EhASSk5Pp3r07kydPpri4mIkTJ7Jy5Up69OjBG2+8QdeuXQFo0aIFqampdO/e3T3BoCo5OTlcccUV9OzZkwEDBvDkk0+e1fcATm/XvHnz+PTTT+nUqROJiYk88MADtG7dutp4a/LHP/6Riy66iNTU1ArnP/PMM/z973+nR48epKenu8vPpI1zidc2cjfGvA5cARyw1nZ3lb0DxLtOiQIyrbVJVVy7A8gBSoBiTzY5BW3kLuJtpaWWuav38JeFWzh0rJBnJiRxVVJsfYcl5yht5C6Nydls5O7NwSezgOeBN8oKrLXjy94bY/4G1DSyc4i19pDXohOR0+bjY7gupS2jurfm1a+2M7hLSwB2H8kjOjyQIH/feo5QRKTp8VqyZq39yhjToao644wuvA4Y6q32RcR7woP8+d0Ip5O8tNQy6c1VHCss4qHRCYxMbKXp9iIitai+xqwNBPZba7dWU2+BT4wxq4wxk2q6kTFmkjFmpTFm5cGD2uNQpK75+BgeGt2NYH9f7vzXKn756ndsysiu77DkHHQ07zibM7JZvyeTzRnZHM07Xt8hidSK+krWrgfm1FA/wFqbDIwCfm2MGVTdidbaV6y1KdbalOjo6NqOU0Q8MKDzefz3twP541WJbNqXzeXPLuWbbRrFIN5XNu76aN5x0o/mc7zE2ZfzeEkp6UfzlbBJg3C28wPqPFkzxvgBY4F3qjvHWpvuej0AzAP61k10InKm/Hx9uLFfB5bcO5h7hnfhQtfOBz/uz6Go5Mw2thapSVBQEIcPH6a0tJR9WQWUnvQLsdRa9mdprS6pX9ZaDh8+TFBQ0KlPrkZ9rG45HNhsrd1TVaUxJhTwsdbmuN6PAGbUZYAicuaiQgL47bDOAOQdL2bia98REeTHw1ckMCS+ZT1HJ42JtZajeUXszy4gwM+HTtFhlJRapn+Yxv7sQo7lFTC6YyCtwnZR0yjJ7H1+BPv74uer1aqkfgQFBREXF3fG13stWTPGzAEGA+cZY/YA0621M4EJnPQI1BgTA7xmrR0NtALmuQYo+wFvWWsXeitOEfGeYH9fHh/bgz8t2MSt//ieIfHRPHxFAp2iw+o7NKlH1lpyCos5kF1DZq4AAAAgAElEQVTA/uxC9mc7u2OMSGwNwK3/WMHWA8c4kF3ofqw5plcMz17fG18fw1c/HiIkwJeWEUGszwqglQ1izopdHM6t/MjT18dQUmr56y96Ma5PHDsO5fLGtztJbh9F73bNiIkM0oQYafC8ts5afdA6ayIN0/HiUv75zQ6e/Wwr+UUlfDx1IJ1bhdd3WOIle47msedoPvuzCzjgSsZCAnzdM4iv+vuySosqX9ihGe/d2R+A/5m7jqISS8uIQFqFB9E6MoiO0aF0bR1RbZvz16TzwPs/kF9U4i4L9vflsbE96H9BC4L9fQkP8mfxxv1MmbOagiInCWwVEUhyu2Y8OLobbZufeo9NkdrUENZZExEBIMDPhzsGdeSa5Fjmr0nngpZOz9r6PZkkxkTi66OejYbueHEpAX7OY8SVO47wQ3oW+7MLnd6xnAKshbfuuBiAh+ensWTLidn5Qf4+JLWNch+PT2nLFT3aOMlYRBCtIoJoGX5ij8m/jOt12vFd3dtZnPmJRVvYm5lPTFQw00bGu8vLXJrQih8eHcnmjBxW7zrq/goLdH4dvvrVdhb8kEFyu2Ykt48iuV0zYqKCTzsekdqknjURqRf7swsY9Jcv6BgdxiNXJLA/u+CUv2il9hWXlHLo2HH2ZxfQPdZJnD/duJ9FG/axP8eVjGUXkFNQzI9/GoWPj+HBeT/w1ne78Pc1tAwPolVEIHHNQnj2+t4ArNp5lIKiElpFBNIyIojwQL9G86hx7qo9vPP9LtbvyaKw2Ol9a98ihC9+PxgfH8PezHxahAUQ6KcFoOXsedqzpmRNROqFtZaP0/bx5wWbSM/Mx8dAabkfR2WPsJSwnTB/TbrHCW1pqeVwrpOEHchxxoaN7tGGyGB/Plq/l5e+/In92YUcOlZI2a+B7x4cRquIIF5Yso03vtlJq4hAol3JWKuIICZf0pFAP18OHSvExxiahfg3miTsdB0vLmXzvmxW7zzKkbwifndpFwDGvrCMtPRsEmMjnN63ds3o074ZrSPPfKafnLuUrIlIo1BQVELfP39KdkFxpbqIID/uHNwJH2MwQOvIIPdepB+sTSczrwgf42xCbQzERAW7Z5x+/EMG+UUlzrWuc2KjgujT3llS5PPN+ykusfgYg48PzubeUUHucVHfbDsEBqfedY9W4UG0axFCaanlh/Qsd3nZPVqEBhIdHkhxSSk7j+S5rsV9XmSwP+FB/hSXlHIk7ziGE/U+xhAU4EOgny+lpZbC4lJX3E79f9bu5aH5P5BfdGIZlABfH67tE0tcsxD2ZxdwU78OXNAyjAXrM5j69hqKSyv+fJ//61SS2kbx6cb9zP5up/P4McKVjIUH0f+CFoQEaHRMTT7duJ/vdxxh9a6j7t634d1a8drNzu/bt1fsokvrcBJjItT7JqekMWsi0igE+fuSU0WiBpBdUMxfFm5xH6e0b+ZO1p7/fBtbDxyrcP6gLtHuZO2PH21k70lrbI3u0dqdrN3z9tpKCeIv+sTxxC+c8VI3vr6CkpOSnVv6d+DRMYkcLynlqr8vqxTvb4ZcwL0j48nML2LY376sVH/fZV25a3An0jPzueSJJZXqZ1yVyE39OrB5Xw6jn11a5WdS3vGSUuas2A04ieDwbq24oGUYnVuFMfmSjq6xYCd6xsrGhQ1PaMXwhFanvL9UVv6zO15cyqaMbMo6F4/mHuf+938AnHGa3WOc3rcxSTH0jIuq7pYip6SeNRGpd6mPf056Zn6l8pjIID6/dzDWOguc+hhDcIDTW5GVV0RxaSkWp85a8Pf1oXloAADpmfkUl5RSasvqLaGBfrSJdAaLb96XTXGJdd+71FqahwbQvkUoACt+PuIuLzunTWQwF7R01vpasuVAuWudx7odo8OIbx1OQVEJizbsq1Bfai09YiPp1iaC7IIiPly7F2utE3+pc87FHVuQEBPBoWOFzF21x922tZa/fvJjlZ+dATb98TKC/NWL0xDszy5gza6jrN6VyeqdR1mfnsWMMYlM6NuOnYdz+cvCLSS3b0ZyuygSYyLdkzbk3KTHoCLSaNS07ILGrDmqS2hjo4JZdv/QeohIPHG8uJRSawny92X59sP8/t117j/HAD8fesRG8r/X9CC+dTglpVYzo88xegwqIo2Gp8sunMumjYyvMqGdNjK+HqOSUynfc3ZxxxYsu38o+7MLWL2zbNmQTJqF+gMw8+vtzFq2g97tm7kmL6j3TRzqWRMRaSROZzaoND6fb97Pv1ens2bnUfd4y7BAP1b/30sJ8PPhx/05RAX70zJCM0+bCvWsiYg0MVf3jlVy1oQN7dqKoV2dyQv7sgpYvesoezPz3T1rD89PY8XPR4iNCia5fTN6t42i7/nN6R4bWZ9hSx1Qz5qIiEgjsH5PJt/vcD0+3XmUjKwCBsdHM+vWvgC89OVPdGgRQnK7Zup9ayTUsyYiItKE9IyLomdcFLdxPgAZWfnkFjrLz+QdL+apxT+6d10o6337RZ84BnWJdt9Dj9IbJyVrIiIijVDZMjQAIQF+rH90BGnp2a6lQ47y/c9H6Hu+s67g7iN53PT6d+w+ku9eLDk9M58HXOvCKWFr2JSsiYiINAGBfr70ae9sf1WmbGHnrPwi9hzNr7SrRX5RCU8s2sKgLtHkHS8mNiq4yW4h1pgpWRMREWmiytZt6x4bSXFJ1WPU92bmM39NOjM+2kizEH+6x0Y6XzGRDOvWUgsuNwBK1kRERM4BMVHBVe8UEhXMkK4t8ffzYUN6Fj+kZ/Ha0u2UlFrS/jASgHe/381PB4+RGBtJj9hI2jcPwUcL+NYZJWsiIiLngJoWVj7/vFDOPy/UXV5YXMKOQ3mEBDhpwoa9WcxZsZvjJc4EhvBAP/p1asErNzkTGQ/mFNI8NEA7MHiJkjUREZFzwOnsFBLo50t863D38R+u6s7DVyTw4/4c0tKzSEvPpnxeduPM79h1JI+ENhHux6jJ7aLoGB3m9e/rXKB11kREROSszFuzh3W7nUeoG/dmk19UwlVJMTwzoTfWWh77eDMXRIfRPTaSzq3C8PfVFlqgddZERESkjlzTO45rescBzgzU7QePUTap9GheEW99t4tjrjXhAvx86NY6nDsv6cSoHm0oKbWUlFrtgVoDJWsiIiJSa3x9DJ1bnXiE2jw0gPXTR7DjcC4/pGexYW82P+zJci8RsmFvFte++A3xrcPpERtJYowziaFrm3AC/TQTFfQYVEREROrRzsO5zFmxmzTXTNSs/CIA3rr9IvpfcB4b9maxaudREmMiSWgTQXBA00ng9BhUREREGrz2LUK5f1RXAKy17DmaT1p6Fj3bRgGwZMtBnli0BQAfAxe0dMa+Tb8ykchgf6y1TX4hXyVrIiIi0iAYY2jbPIS2zUPcZXcP7sQ1vWOdR6iu3rdVO48SFuikMNM/3MCybYfoUbaYb2wkiTERhAf519e3UeuUrImIiEiDZYwhJiqYmKhgRia2rlTfPSaSvZkFLN9+hPlr9wLQtnkwS/9nKACLNuwjPNCPxNhIIoMbZwKnZE1EREQaresubMt1F7YFnMV50/ZmkX/8xMK/M/6z0b1zQ7vmIfSIjWR4Qkv37NXy5q9J92gdurqmZE1ERESahOjwQIbEt6xQ9p8pA5yFfPdmkZaexfr0TFqEBXBN7ziKS0oZ+fRXXNAyDF9j+HTTAfcuDemZ+Tzw/g8A9Z6wKVkTERGRJqt5aACDukQzqEu0u6yk1FkJ41hhMd3aRJCWnsWOw3mVrs0vKuGJRVuUrImIiIjUpbI9TKNCAnj+l8kAnH//AqpazGyv6xFqfdJywSIiInLOi4kKPq3yuqRkTURERM5500bGE+xfccHdYH9fpo2Mr6eITvBasmaMed0Yc8AYk1au7FFjTLoxZq3ra3Q1115mjNlijNlmjLnfWzGKiIiIgDOJ4LGxPYiNCsYAsVHBPDa2R72PVwPvjlmbBTwPvHFS+VPW2r9Wd5Exxhf4O3ApsAf43hjzobV2o7cCFREREbm6d2yDSM5O5rWeNWvtV8CRM7i0L7DNWrvdWnsceBu4qlaDExEREWkk6mPM2m+MMetdj0mbVVEfC+wud7zHVVYlY8wkY8xKY8zKgwcP1nasIiIiIvWqrpO1F4FOQBKQAfztbG9orX3FWptirU2Jjo4+9QUiIiIijUidJmvW2v3W2hJrbSnwKs4jz5OlA23LHce5ykRERETOOXWarBlj2pQ7vAZIq+K074HOxpjzjTEBwATgw7qIT0RERKSh8dpsUGPMHGAwcJ4xZg8wHRhsjEkCLLADmOw6NwZ4zVo72lpbbIz5DbAI8AVet9Zu8FacIiIiIg2ZsbaqzRUap5SUFLty5cr6DkNERETklIwxq6y1Kac6TzsYiIiIiDRgStZEREREGjAlayIiIiINmJI1ERERkQZMyZqIiIhIA6ZkTURERKQBU7ImIiIi0oCdMlkzxvzCGBPuev+wMeZ9Y0yy90MTEREREU961v6vtTbHGDMAGA7MxNmQXURERES8zJNkrcT1ejnwirV2ARDgvZBEREREpIwnyVq6MeZlYDzwX2NMoIfXiYiIiMhZ8iTpug5nU/WR1tpMoDkwzatRiYiIiAjgQbJmrc0DPgByjTHtAH9gs7cDExERERHwO9UJxpgpwHRgP1DqKrZATy/GJSIiIiJ4kKwBU4F4a+1hbwcjIiIiIhV5MmZtN5Dl7UBEREREpDJPeta2A0uMMQuAwrJCa+2TXotKRERERADPkrVdrq8AtL6aiIiISJ06ZbJmrf0DgDEmxDUzVERERETqiCd7g/YzxmzEtVyHMaaXMeYFr0cmIiIiIh5NMHgaGAkcBrDWrgMGeTMoEREREXF4tG2UtXb3SUUlVZ4oIiIiIrXKkwkGu40x/QFrjPHHWXdtk3fDEhERERHwrGftTuDXQCyQDiS5jkVERETEyzzpWQu21k4sX2CMae2leERERESkHE961n42xswxxgSXK/uvtwISERERkRM8SdZ+AJYCy4wxnVxlxnshiYiIiEgZTx6DWmvtC8aYdcB/jDH3AdbLcYmIiIgIniVrBsBau8wYMwx4F+jq1ahEREREBPAsWRtd9sZam2GMGQL0915IIiIiIlKm2mTNGHODtfZfwPXGVDlE7auabmyMeR24Ajhgre3uKnsCuBI4DvwE3Gqtzazi2h1ADs7iu8XW2hSPvhsRERGRJqamCQahrtfwKr7CPLj3LOCyk8oWA92ttT2BH4EHarh+iLU2SYmaiIiInMuq7Vmz1r7sevuptXZZ+TpjTOqpbmyt/coY0+Gksk/KHS4HxnkcqYiIiMg5yJOlO57zsOx0/Qr4uJo6C3xijFlljJlUC22JiIiINEo1jVnrhzORINoY87tyVRGA79k0aox5CCgGZldzygBrbboxpiWw2Biz2Vpb5Rg5VzI3CaBdu3ZnE5aIiIhIg1NTz1oAztg0PyqOV8vmLB5fGmNuwZl4MNFaW+V6bdbadNfrAWAe0Le6+1lrX7HWplhrU6Kjo880LBEREZEGqaYxa18CXxpjZllrd9ZGY8aYy4D/AS6x1uZVc04o4GOtzXG9HwHMqI32RURERBqbmh6DPm2tvQd43hhTqQfMWjumphsbY+YAg4HzjDF7gOk4sz8DcR5tAiy31t5pjIkBXrPWjgZaAfNc9X7AW9bahWfyzYmIiIg0djUtivum6/WvZ3Jja+31VRTPrObcvbgW37XWbgd6nUmbIiIiIk1NTY9BV7nergTyrbWlAMYYX5zeMRERERHxMk+W7vgMCCl3HAx86p1wRERERKQ8T5K1IGvtsbID1/uQGs4XERERkVriSbKWa4xJLjswxvQB8r0XkoiIiIiUqWmCQZl7gPeMMXsBA7QGxns1KhEREREBPEjWrLXfG2O6AvGuoi3W2iLvhiUiIiIi4FnPGq7kLM3LsYiIiIjISTwZsyYiIiIi9UTJmoiIiEgDVm2yZoxpb4yJLHc8xBjzjDHmd8aYgLoJT0REROTcVlPP2rtAKIAxJgl4D9iFsxXUC94PTURERERqmmAQ7NqzE+AG4HVr7d+MMT7AWu+HJiIiIiI19ayZcu+H4mw7RdkeoSIiIiLifTX1rH1ujHkXyACaAZ8DGGPaAMfrIDYRERGRc15Nydo9ODsVtAEGlFsItzXwkLcDExEREZEakjVrrQXerqJ8jVcjEhERERG3apM1Y0wOYMsVWeAQ8AVwn7X2sJdjExERETnnVTvBwFobbq2NKPcVCaQAG4CX6ixCERERkXPYae1gYK09aq19CujkpXhEREREpJzT3m7KGOOPhxvAi4iIiMjZqWnM2tgqipvhzBCd67WIRERERMStph6yK086tsBh4Blr7QLvhSQiIiIiZWpauuPWugxERERERCo77TFrIiIiIlJ3lKyJiIiINGDVJmvGmKmu19S6C0dEREREyqupZ61szNpzdRGIiIiIiFRW02zQTcaYrUCMMWZ9uXKDs3VoT++GJiIiIiI1zQa93hjTGlgEjKm7kERERESkTI07EVhr9wG9jDEBQBdX8RZrbZHXIxMRERGRU28bZYy5BHgD2IHzCLStMeZma+1XXo5NRERE5JznydIdTwIjrLWXWGsHASOBpzy5uTHmdWPMAWNMWrmy5saYxcaYra7XZtVce7PrnK3GmJs9aU9ERESkqfEkWfO31m4pO7DW/gj4e3j/WcBlJ5XdD3xmre0MfOY6rsAY0xyYDlwE9AWmV5fUiYiIiDRlniRrK40xrxljBru+XgVWenJz16PSIycVXwX80/X+n8DVVVw6ElhsrT1irT0KLKZy0iciIiLS5J1yzBpwF/Br4Leu46XAC2fRZitrbYbr/T6gVRXnxAK7yx3vcZWJiIiInFNOmaxZawtxxq09WduNW2utMcaezT2MMZOASQDt2rWrlbhEREREGor62Bt0vzGmDYDr9UAV56QDbcsdx7nKKrHWvmKtTbHWpkRHR9d6sCIiIiL1qT6StQ+BstmdNwMfVHHOImCEMaaZa2LBCFeZiIiIyDnFq8maMWYO8C0Qb4zZY4y5DXgcuNS1ldVw1zHGmBRjzGsA1tojwB+B711fM1xlIiIiIucUY23NQ8aMMV2AaUB7yo1xs9YO9W5opy8lJcWuXOnRRFURERGRemWMWWWtTTnVeZ7MBn0PeAl4FSg528BERERExHOeJGvF1toXvR6JiIiIiFTiyZi1/xhj7jbGtHFtFdXctcOAiIiIiHiZJz1rZTM3p5Urs0DH2g9HRERERMrzZFHc8+siEBERERGp7JTJmjHGH2fLqUGuoiXAy9baIi/GJSIiIiJ49hj0RcCfE/uB3ugqu91bQYmIiIiIw5Nk7UJrba9yx58bY9Z5KyAREREROcGT2aAlxphOZQfGmI5ovTURERGROuFJz9o04AtjzHbA4OxkcKtXoxIRERERwLPZoJ8ZYzoD8a6iLdbaQu+GJSIiIiJQQ7JmjBlqrf3cGDP2pKoLjDFYa9/3cmwiIiIi57yaetYuAT4HrqyizgJK1kRERES8rNpkzVo73fV2hrX25/J1xhgtlCsiIiJSBzyZDfrvKsrm1nYgIiIiIlJZTWPWugKJQORJ49YigCBvByYiIiIiNY9ZiweuAKKoOG4tB7jDm0GJiIiIiKOmMWsfGGM+Au6z1v5vHcYkIiIiIi41jlmz1pYAV9dRLCIiIiJyEk92MFhmjHkeeAfILSu01q72WlQiIiIiAniWrCW5XmeUK7PA0NoPR0RERETK82S7qSF1EYiIiIiIVHbKddaMMZHGmCeNMStdX38zxkTWRXAiIiIi5zpPFsV9HWe5jutcX9nAP7wZlIiIiIg4PBmz1slae2254z8YY9Z6KyAREREROcGTnrV8Y8yAsgNjTCqQ772QRERERKSMJz1rdwH/dI1TM8AR4GavRiUiIiIigGezQdcCvYwxEa7jbK9HJSIiIiKAZ7NBWxhjngWWAF8YY54xxrTwemQiIiIi4tGYtbeBg8C1wDjX+3e8GZSIiIiIODwZs9bGWvvHcsd/MsaM91ZAIiIiInKCJz1rnxhjJhhjfFxf1wGLzrRBY0y8MWZtua9sY8w9J50z2BiTVe6cR860PREREZHGzJOetTuAe4B/uY59gFxjzGTAWmsjTqdBa+0WXPuNGmN8gXRgXhWnLrXWXnE69xYRERFpajyZDRruxfaHAT9Za3d6sQ0RERGRRsuTnjWMMWOAQa7DJdbaj2qp/QnAnGrq+hlj1gF7gXuttRtqqU0RERGRRsOTpTseB6YCG11fU40xj51tw8aYAGAM8F4V1auB9tbaXsBzwPwa7jOpbJP5gwcPnm1YIiIiIg2KsdbWfIIx64Eka22p69gXWGOt7XlWDRtzFfBra+0ID87dAaRYaw/VdF5KSopduXLl2YQlIiIiUieMMaustSmnOs+T2aAAUeXeR55ZSJVcTzWPQI0xrY0xxvW+L06ch2upXREREZFGw5Mxa48Ba4wxX+DsDToIuP9sGjXGhAKXApPLld0JYK19CWfx3buMMcU4m8ZPsKfqAhQRERFpgmp8DOrq3YoDioELXcUrrLX76iC206bHoCIiItJYePoYtMaeNWutNcb811rbA/iw1qITEREREY94MmZttTHmwlOfJiIiIiK1zZMxaxcBN7hmZObijFuzZzsbVEREREROzZNkbaTXoxARERGRKlWbrBljgoA7gQuAH4CZ1triugpMRERERGoes/ZPIAUnURsF/K1OIhIRERERt5oegya4ZoFijJkJrKibkERERESkTE09a0Vlb/T4U0RERKR+1NSz1ssYk+16b4Bg13HZbNAIr0cnIiIico6rNlmz1vrWZSAiIiIiUpmnG7mLiIiISD1QsiYiIiLSgClZExEREWnAlKyJiIiINGBK1kTqyvp34anu8GiU87r+3fqOSEREGgFP9gYVkbO1/l34z2+hKN85ztrtHAP0vK7+4hIRkQZPPWueUq+IeMpaKMyBzN2Qn+mUffroiUStTFE+fDajzsMTEZHGRT1rnlCvyLnHWjieC1gIDIfi47D1EyjIdBKwgkzIPwoXXArxl0H2XvjnlSfqSl2bfoz6C1w02amvStaeE+0ZUyffmoiINC5K1jzx2Yyqe0UWPQhR7SGqHUS0gdISKMgC/2DwC9Iv3/pmrZM0+fo7xzu/hbzDFROu6K7QY5xz7swRkH/EVZcFpUVw8d1w2WNgS+CdiSfubXwgKNL5s4+/DALCoHUPCIqC4KgTr20vds6PjD2RmJUX1sp5/WEuLHkMOqRC+wHOa2Scdz8fERFpFJSseaKqX7IAuQfh9REw5GG4ZBrkZMBTiSfq/YKdxG34dOhzCxzdAe9PBv8g8A9x6vyDofeN0O5ip/dl/Tvl6lyvsX0gvLXzaC07o+L1fsHg0wCeZq9/10lqs/Y4ScawR2q/1/HQNji2r1zPViaENIekXzr1838Nh7ZUrO98KVw/x6l/9ybIPXDifsYHev3SSdaMgdBoJ6kqn3DF9nHO9Q+GSV9CcDOnLiC84uceFAG/mFV97MOmV+ydLbvniD8670PPg+h42PgBrH7DKWvWwdVmlHOdf/DZfHoiItJIKVnzRGSc8+jzZKEt4ZqXnF+q4Dwuu+z/QVEeFBc4r0X50LyjU29LwS/QKcs77LwW5cMFw536ozucsU0nG/8v6HYl7FoOs8dVrr9xPnQaAj9+AgvvL5fsuZK6EX+C8zrD7u9h4/zKyWDCGCcJydwNmbsq1vkHQ0gL8Klh9zFPHxPnHoJj+ys+RrQWkm906r/6q/M9lu/5ioiFyV869fPvgj0rKrYdk3wiWSs5DgGhzjXBUc731Kr7iXMnzAbfgBN1Jydc179V/fcIEJNUc31Nyj6H6hLaTkOcr9IS2L8Bdi6DA5ucWAHm3w17Vrp63lKhwwDn7516b0VEmjxjra3vGGpNSkqKXblyZe3f+ORkBJwk5spna7f3qLTUleTlQ7ErkSvKcx61BTdzetV2LjuR5JUlhb0mOL+4d30HK145UVf2eu1rTq/N6jfh4/9xfR/l/tynrIYWnWDZs7D4/1aO63ebnce8Xz4B3z7nJHJ+5Xr3cvZBdhW9jz5+TuJ0z3rn+N93wA8nTcwIbg73/ey8X3Av7Pm+4mPEqPYw8HdO/c5voaSwYs9XYETD6Fn0trVzYMt/nT//vMNOWaehcOM8533OPueRqpI3EZFGwxizylqbcsrzlKx5qC4e89UVa6G48ERCF9bSGdeVtQcObyuX7Ll6B3vf4CRlWxfDtk/LJYKu8376ggrJX3l9boHLn3ISql3LnUfFJ4/rCm5Wl99942YtHNwMO752kuXeE6GkGP5fe2fcXIcBJ8a9nddZyZucW5rSz2k5JyhZk7rzVPeqHxNHtoX/k1b38Zxrigpg7Wyn123HMmdcH8DQh2HQNKf+6A6nd1XJmzRVdfUERKQWeZqsacyanL1hj1T9Q3LYI/UX07nEPwguvM35shaObIcdSyHuQqd+1zfw5jUQcl7F2abR3c6NR8jS9JWWwuLpVc/a/++9zmSw4OaQdL1Tvmu5My7WLwB8A52xxIEREN3Fqc874rz6BjhDPnyb0K9K9T42SupZk9qhHwAN17GD8ONCV8/b1yd6Qe/4AmKT4fBP/7+9e4+PurrzP/46k8k9IQkhIZCEi6aogNwMoLBKK4q1qMV1ZeuuXba29dLWYm1t3f09ar3VVtuti7XaWi/VraJt1aqgFqW0ohblIqgQBKEEEsM193tm5vz+ODOTBEjQMpOZJO/n4zGPTOZ8853zPZnL5/s5l69bU274RAVv0j/sXgM7/+pmfx/c5maK+1p6/5ucMbB4k7v/6IXw99e6lw+fCNe84e7/+myoXN9ZZjxuYs9/LnO//9/FbkKWNzkY0CVD8Uw49xZX/uJ3ob2xs8ybDAWT3MxzgPWPAjYYCAa3yRkDw4OrCbDlqAcAACAASURBVFRtco+HyhKSITnj+GeEK/sYd5RZk741aaHe7PEqI8/NuA3Nuq0pd4FbwST3+1u/grd/5cYQjp7lxr2Nng0jJqvbVGKjtd7Nhj64zQVkB7a5+1evdrPut/0JXr/bTb4aNg7GznFDAVpqjtzXkCIXhAX8nY9duMRt62t3k5Z8bd0DoVnfdJN2fK1ulrmvDYaM7CwfPtGts9j1722gs/yjd9xSTKEyXxuM/3xnsPbyf0FHU/d6TlsEF93jsuO/OuvI4zj96/DZO6CtEe46oUugmOIyhDOvgZlXuqzg7/6jM8gLZQ9PvbTnNUNfvVmf33FOwZrIYJMz2t1C/uk6t57crtUuiPvgRcgogG9vdeXbVkB6LhRMHljdQRJb1rpM/MFtnbfZ17nX5rtPue5LcIFGbok7eWhvcsHa7MVuPGZSWuf+Rkw+etbonB90LoETElpOqScTFvReHlofsSdfeaX38uveDQZxXYLBUB2thS884R4LlfnbO7NuxgOnX909UPS1QWZwge2A392aq7v8fZsb+tDTmqH1lfDO427CUs0uWPewC4SzxwR/FmudxxhTN6iIdFf/kVtvb1Tw6gt3nwp1u926dKNmusxbyTnuig0ix+Jrh+odLhgrONUFSrteh8cXds8upWS7NSXHnulef/u2uDFk2aN7X+exKw3H6F1Pk8FSsuCKP0H+KfDhSlj6BRfodRVaz/OjjW7x7uxR7pYzxrW1N7lPDmGg0WxQEYmMhr2d4912veG6pU77Elz4v25g95v3wKgzYORU1+Uig1NrncvopA11r5ll33IBWvXf3eXaoPNauXUV8ObPXRdm3knuZ3qeut2j7eOOWQsE3Kzy2t3uVlPusm5DRroM3Avf7Lz+MQAGvv62C653rHKfFTmjOwO6rOLOy/5JNwrWRCQ6Gve7s+6sIjeW6BfBWafeVCieAWPOhFMvOXZXk/RfAT+sfahzgP+Bbe7LffZiOPdW113567lurb9wQBa8n5Qe69oPbpHIPgb8bs3MmvLOgG7WN9z/9rWfwKofdQbo4Lpub9zturA3P+vGI4auq50zGjJHDtohFnEfrBljdgENgB/wHV5ZY4wBlgCfA5qB/7TWbuhtnwrWRGKg6SCUv+nOpsvfgH3vw7/9HsbNg73vQdky13VaNN0tMyL9w6EdXQb5B28Fk1xG1Vq4c4wbVD9sXDAgG+cC9aJjfu/IQOf3uXFwtbuhttxdfWfODa5s2fVuTFzXhdRTc+B7u9z99b9xJ4ShrFz2aHdt7I/bFd7P9JdgrdRae7CH8s8B1+KCtZnAEmvtzN72qWBNJA40VwcvRZbiPpSXXQ9YN3OtaLqbaTrrWkgZ0v3vNN6o77U1dg/GjMctpgxw3yzYv9ndzxzpMmMlc132DNz/OTVHXZfyyfna3SUKQ5m5jhY3aQJg6WVuklNXXZdVef1/IdDRZfLDKHepvX667NBAWLrj88Bj1kWTa4wx2caYEdbaqlhXTER6kTa0837pFTDhn926WOWvu+zbmvvdTD6AtQ+6CQ0Bv1tCJLRWVt0eNy4GFLAdL2tdpuLgNpftmPwF9/gzV8G7T3ZuZxI6F1IG+NxPXMCd+6kjA2vo/n8W+SS8SW6YxNGGSly21F11pa4Cane5YC6hy+SFzc9C1cbuf/Op8+Dfg9edfvUW190amvyQPeqTjYeM05PGWAZrFlhhjLHAr6y1DxxWXgh0nbZSEXxMwZpIf5KaDSd91t3AnUWHJiJUvgOblnYf3xLS0QIvfdcFcwmJLjPn8boP4BM/47bZtsL9bUIieBLdz4zhkHuiKz+43XWfhMo8iW65h/66DEFvXyR+n+tyyhnrsgwbn3CZzYPb3OB/cJmz8QtcEFZyTvcxZTlju08QGTO7749PBNzrc1iJux3uqr9Ce7M7oavd7ZYayQgtWxJwr/vQJfdCSq+AC+525S991713whMgRkNargvmDp+AEUcnjbHsBi201lYaY/KBV4BrrbWvdSlfBvzYWvt68PeVwPestesO28+VwJUAo0aNOq28vLzPjkFEIqCtAX5UTLcxLL05+QL4wuPu/l0nQPOh7uWT/hX+OXjud/twt5ZVV+EPbj/cmtsl0PO6gHDGVW58TWs9PHRu9zJPIkz7D5j8r64bcNl1XQJBr/s54WIYe5a7csTaX3f+fWi7Ez7tgqOmQ7BzVfdAMyER8ie4hYzbGlw3UejxbX9yi5d2PR6P140j62hxy2P4290q/TljYMNj7sun65iyYSe5GX3qupSBrK2xc+JD7W53UnLiZ9x78t5SaK3tvn3oOso/G++yz4eL4nWu474b1FpbGfy53xjzLDAD6Hr9j0qguMvvRcHHDt/PA8AD4MasRa3CIhIdyZnuTPdo6z9lFbklAfwd7hbocEFTyKJlwYVFg2X+DsjI7yxfcJ8bHxMqC/gg72RXZi2c9Z3Ox/0dLtjJO8mVG+OCnK5lAV/nSvW+Vti/NbhvX+dzjJjsgrWm/fDXO488ps/f557j0Ifw9JePLL/0Ubco65634LeX9N52AR/sfRdKzoVPnev2m5Llyqb9h7uJDDbJGTB8vLt1lZEHN5a7THPtHpeJrt3tZrGDy+IfTU+LCfehmGTWjDHpgMda2xC8/wpwq7X25S7bzAe+QecEg3ustTN6268mGIj0UwP1moXWdgZ7oaAu1A3b0eK+KLqWhYLF9GFunNnuv3UGk89e1cOTGLi5tocyEfnYelo0eBBn1oYDz7rVOfACT1hrXzbGXA1grf0l8CIuUPsQt3THl2JUVxGJtlBAFocDe4+LMZ3dmIdLTO3M4h1NRr67nmTIn2/vOfsoIsdv7k1HP2mce1Ps6hSkRXFFRPqDgZp9FIknfTwbNN4zayIi8kkM1OyjSDyZtDAu31MK1kRE+os4/SIRkejqn0v+ioiIiAwSCtZERERE4piCNREREZE4pmBNREREJI4pWBMRERGJYwrWREREROKYgjURERGROKZgTURERCSOKVgTERERiWMK1kRERETimII1ERERkTimYE1EREQkjilYExEREYljCtZERERE4piCNREREZE4pmBNREREJI4pWBMRERGJYwrWREREROKYgjURERGROKZgTURERCSOKVgTERERiWMK1kRERETimII1ERERkTimYE1EREQkjilYExEREYljCtZERERE4piCNREREZE4pmBNREREJI4pWBMRERGJY30erBljio0xq4wxW4wxm40xi4+yzaeNMXXGmI3B2019XU8RERGReOCNwXP6gG9bazcYYzKB9caYV6y1Ww7bbrW19oIY1E9EREQkbvR5Zs1aW2Wt3RC83wCUAYV9XQ8RERGR/iCmY9aMMWOAqcBbRyk+wxizyRjzkjFmQp9WTERERCROxCxYM8ZkAE8D11lr6w8r3gCMttZOBn4O/LGX/VxpjFlnjFl34MCB6FVY5Dgt37mceX+Yx6RHJzHvD/NYvnN5rKskIiL9QEyCNWNMIi5Qe9xa+8zh5dbaemttY/D+i0CiMWbY0fZlrX3AWltqrS3Ny8uLar1F/lHLdy7n5jdvpqqpCoulqqmKm9+8WQGbiIgcU59PMDDGGOAhoMxa+7MetikA9llrrTFmBi6oPNSH1RSJmIb2Bu58+05a/a3dHm/1t3L7mtspry8nOzmbnJQcclJyKMwopDizOEa1lXi2fOdylmxYwt6mvRSkF7B42mLmnzA/1tUSkSiLxWzQ2cAXgfeMMRuDj/03MArAWvtL4F+Aa4wxPqAF+IK11sagriKfWJu/jeSEZACu/8v1rNy9koANHHXbxo5G7t90f7fHFpQs4LbZt2GtZc5Tc0hPTA8HctnJ2Xy6+NOcO/pc/AE/f634qytLduWZSZl4jJZPHIhC2dlQ0B/KzgIK2EQGuD4P1qy1rwPmGNvcC9zbNzUSOT717fVs2LeBtXvXsnbvWvY37+fPC/+Mx3iYnDeZkuwSfvfB7zjUemRyeET6CF785xepa6ujtq2WmtYaspKzAPBZH/NPmE9NWw01rTUcaD7AtpptjMocBaOhrr2Oxau6L1OYYBL41mnfYtGERRxqOcQP3/phZ9YuOYfslGwm502mOLMYX8CHL+AjxZvSJ+0kR/IH/DT7mmnqaKLZ10xmYiZ5aXm0+lpZuXslTR1NtPhaaOpo4rHNjx01O7tkwxIFayIDXCwyayL9Wn17PWneNLweL795/zf8bP3PsFiSPElMypvEwpMW0u5vJ8WbwqIJiwAYPWR0t6wIQEpCCounLcbr8ZKbmktuam6350n0JPK9Gd/rsR6ZSZk8ecGT1LbWhgO6mtYaxueOB6Cpo4kdtTuobaultq02nN27ddatFGcWU3aojH978d9I9aaGA7mclByumnQVU/OnUtVYxerK1QxNGUp2crb7mZJNVlIWCZ6ESDdrv+AP+MPHvqN2Bw3tDTR3NIcDroL0AmaOmAnAXWvvCpc3+Zpo6WhhTvEcrph4Be3+dmYvnX1E8HXFxCv41mnfos3fxo2rb/xYdapqquJLL3+J8bnjubjkYkpySiJ70CIScwrWRI7h8MzZ1uqtPHb+Y0zJn8KU/ClcM/kaSgtKmZQ3Kdz9ebhQ5iOS440SPYlMyO15VZtRQ0bx3ILnAAjYAPVt9dS01TA0ZSgAeWl5LJ62mJrWmnBWr2tQt+XQFm5bc9sR+31o3kPMGDGD1RWrefC9B7uNt8tOzubCEy9kaMpQaltraexoZGjKUFK9qbjhqj2L9Hgsay0tvpbOzFVHM8YYTh56MgAry1fyUdNH4bJmXzMF6QV85dSvAK4Le2ftzm6Zr1kjZ/GLub8A4KsrvsqBlu4z0OeNnhcO1l4tf5WADZCWmEa6N520xLTw6yPRk8hlJ19GqjeVtMS08DahQCszKZPnFzxPemI6ad40Ur2pnP/M+VQ1VR1xnGneNNr97Tz1wVPMLpxNSU4Ja6rWcP/G+zkl9xTG547nlKGnMDZrLF6PPvJFehOv40LNQBoKVlpaatetWxfrakg/V9dWhy/gIzc1l80HN3PZ8svCmbMp+VMoLSjlohMvojBjYK/l3BHooLqlOpy1q22rpbq1mvPGnMew1GG8UfkGD7//cGd5ay0+6+OFBS8wJmsMj25+lJ+u+ykASZ6kcEB3/zn3Myx1GG9+9CabDmwiJzmHnXU7eXrb07QH2sPPn5yQzOWnXM6kvEnh7kC/9XPZyZcB8OTWJ1m/bz3NvmaXvepoIjMpk4fOewiAK1dcyd+q/tbtmEqyS3j2888C8MUXv8jGAxvDz5XmTWNy/mR+fvbPAbh9ze1Ut1aT6k0NB00nZp/IhSdeCMDqitUYY8JlaYlpZCVnMSRpSFT+H4ePWQOXnb151s3MP2E+HYEODAavx8ublW/yy3d/ydbqrbT4WsLbPn3R04waMory+nJafC2cmHUiiQmJUamvSH9zrPdYNBhj1ltrS4+5nYK1jydeo205fnVtdS5ztm8t6/auY2v1Vr408Uvh7qiH33v4mJkzcZmsxo5G0rxpJHgS2Fm7k00HNrmsXZeA7s6z7iQtMY17NtzDr9/79Sd6jpSEFNZevhaAH731I9746A3SvGkuYEpMIz8tnx+c8QMAXvr7S+xt2hsOpNIS0xiaMpSp+VMBqGmtIcGTQKo3lURP/whYPunnkD/gp7y+nC3VWyg7VMZ1p11HoieRO966g6Vbl5LoSWRczrhwBu6ST12iCSoyKLX4Wpj/zPwjsuXgxhav+JcVUXleBWsRFItoW6Knrq2OvU17OWnoSQRsgDlPzaG2rZbkhGSm5E3htILTmFM0Jzz2S6KnI9BBXVsdZ//ubCxH/yx66oKnwtmrUFAmx+ejxo/YdGATWw65IG5L9RbSvGm8eumrANy9/m5q22o5ZagL4sbljNNEFOnX/AE/+5r3UdFQQWVjJXsa9lDZWMmNM250Gf+N93PfpvuO+rcGw7uL3o1KvT5usKYBDB/Dkg1LjjoL6ydrf8LMETPJTck95ngciZ26tjrW71vP2r1rWbdvHR9Uf8DYrLE8t+A5PMbDf8/8b/LT8jl12KkkJSTFurqDSqInkWGpwyhILzjqeKwR6SMUNEfByIyRjMwYyfljzwdcVrS6tTpcXt1azao9q3hmu1uzPMEkMG/MPO466y4AttVsoyijSIGzxA1rLfXt9VQ0VLCncQ+VDZVUNFawaPwixmSN4fkdz3PTmzeFt08wCRSkF1DdWk1OSg5zR8/lia1PUNtWe8S+C9IL+vJQjkrB2sewt2nvUR8/1HqIz/zuMyyetpivnPoValpreOT9RyjKLKI4s5jizGIK0gs0qLeP1bXVsenAJs4sPBNjDHe+fScv7HwhnDn72pSvMb1genj70BeWxM7iaYt7nC0r0WeM6TYbObTOX1VTFWWHyth8aDPDUt1FZPwBP5e/eDlt/jbGDhkb7kKdOWIm43LGxeoQZBBo87dR2VgZzo5VNFRw3pjzmJQ3ibf2vsVXV3y12/Y5yTnMGz2PMVljKB1eyg/O+AFFmUUUZhRSkF7QbfjDuJxx3Djjxrj9HFIU8TH0dNbfdZkDcF0Lj5c93m2QtNd4+cmcn3DO6HOoaKhg5e6V4UCuKLOIVG9qnx3HQNXQ3hCeqRnKnFksyy9ezqgho/ji+C9yybhLlDmLY9GYLSvHxxgTzsDNHT03/LjFcueZd1JWXUbZoTLernqbZTuXcc3kaxiXM46G9gZuW3Mb44eO55TcUzgl95SoTbqQgSVgA+xv3h8OxiobK5mSP4VZI2dRXl/OBc9e0G375IRkSrJLmJQ3iZLsEm4ovSEcjBVlFpGemB7etnhIMcVDer8yTDx/DmnM2sfwScashV5sexr2hG8XnnghJ2SdwMt/f5kbXruh2/Z5qXncO/dexueOZ0ftDrZWb6U4s5hRmaPISs5S9+pR1LbWsn7fek7OPZnCjEJW7FrBt//6bVISUpicP5npw6czvWA6pw47VTPdRPrAwZaDGFx2bnvNdr6+8uvdTnCLMor4/unfZ1bhLJo7mmn3t5Odkh3DGkushLoqKxsrqWyopDizmLmj59Lqa2X20tndkh0Gw9WTr+ZrU75Gm7+NR95/JHw5vsKMQoalDuv335GaYBBhkZoNWtdW1y2Q29Owh29O/SZ5aXk8+N6DLNmwJLxtZmImRZlF3HfOfQxLHcb2mu3UtNZQnFnM8PThg2bWVpu/jdcrXmftPpc921azDYDvlH6HRRMW0dDewPaa7UwcNlGZM5E4UdNaE568sOXQFq6adBUnDT2Jl/7+Et997bvh8YihSQylBaXqaRgAOvwdfNT0UTggS09MD39Xfu6Zz7GnYU+37S884ULuOPMOAO7b6L7rijKKKMosYkT6iAF/wq1grR9q9bW6wZHBIG53w24qGiv4+dk/J9GTyA/X/JAnP3gScAOzCzMKGT1kNPecfQ8e42Fn7U4w7iy2PwctocxZUkISZxadSXNHM7OXzsbr8TIlfwrTC1zmbGLuxAH/RhYZaHbV7WLVnlXhQK68vhyAFZesYETGCP6y5y+UHSoLj4XLT8uPcY2lK2stB1sOUtFYQUVDBb6Aj4s/dTEAV796NW9WvtltZvfU/Kk8dv5jADz43oN4jTfcVVmYWTjou8gVrA1A+5v3s6N2B3sa9oSDuqaOJh6Y9wAA1/75Wv6y5y8YDMPTh1OcWcyE3Al8u/TbAFQ0VDAkeUhcvjleq3iNNyrfYO2+tWyv2Q7A6SNO59fz3DpcW6u3agFPkQGosb2RrdVbOW34aRhj+J91/8Ojmx8Nf+EPSx3GxNyJLDl7CR7jobmjuccrYmg9zGP7OG3U1NFERUMFFY0VVLdWc+m4SwG47W+38fyO57sNCcpPy2flpSsBeLzscera6jrHjWUUkZeWN2h6gf4RCtYGoQ+qP2BbzbZu2bms5CzunXsvAAtfWEhZdRlZyVkUZ7jBlqXDS1l40kLAZbT6YpxcTWsN6/etZ1f9rvClfa5+5WrW71uvzJmI0NzRzNbqrZRVl7Hl0BaaO5q5+zN3A+4yXx9Uf+C6UIPZtwm5E3hn/ztaD/MYlu1Yxi1/u6VbGyV6Ern5jJu5qOQiHnzvQR7b/Bg1bTXhco/xsO7f15GYkMgz259hR+0OijKLKMooojCzkMKMQi0WfhwUrMkRXqt4LZyZC90m5U0Kr50056k5NHc0uzdicPmRM0acwZlFZwLgC/h6XIbkWGdr7x54l+U7l3fLnKV501i1cBVpiWnsb95PTnKOgjMR6dUfP/wjG/ZtoKy6jA9rPsRnfZw+4nTK68uPOms/KymLe86+h2nDp+EL+Fi6dSkBGwjfLJbJeZOZXjCd5o5mHn7/4c5yAlhrmV04m9NHnE5tay33brwXay1+68di8Qf8zD9hPmeMPIOqxiruXn93uCxgA/itn8tPuZyZI2byYc2H3LX2rvB+/daPtZZvTP0G0wums3H/Ru54647w34ZuN8+6man5U1ldsZofvvXDbmUBG+AXc3/BhGETWLZzGbevud0dV5f9//7C31OSU8LspbOpb68/oo3yU/NZuXAlf9r1J9ZUrQmPGQv9HJI0pN8P5I9XWhRXjnBW0VmcVXRWt8dCwbq1lmsmX8Puht3hbtY1H63BYDiz6Exafa2c8cQZ4e7VUDA3a+QsdtTu6HZGW9VUxfff+D7Pffgct86+lYL0ArYc2sKzHz7LlLwpnD/1fKYXTGdC7oRwcKZxKSLycSwoWcCCkgWAm3z0Yc2HLiB68fKjbl/XXsfLu15m2vBpWGu5a+1dR2zz5YlfZnrBdNr97Tzw7gN4jAdjDAkmAY/xMDRlKKePOJ1Wfysrdq3AGIPHeMK304afFq5PWXVZ+G+NMXhwXbcAAQI0+5rd/nH78Hq8GFwglJSQ5LoN6dy3MYY0r1t8ODs5m2n507o9t8d4wkNbRmeO5uKSi8P1DtUjNPP2aIEaEL7E0nljzuO8Mef9Q/8XiS5l1qRH1lraA+0kJyTT2N7II5sf6TZerratlhtKb+C3Zb896hmtwfDLc37JrMJZtPha8BqvMmciEhXz/jDvqJ9D+Wn5/PHzfyQzKRNrLQ0dDd2CIY/xkGASSPAkxKDWfaunNormtS+ld8qsyXEzxoTHImQkZXDt1Gu7lTe0N2Aw/HTdT3vcx6zCWQCaki8iUdXTVTCuP+16MpMyAfeZFo8TrPqKrhTSfylYk39Y6AOwpys8xMP11ERkcIjn1efjhdqo/1I3qBy3T3KFBxEREXHUDSp9RmdrIiIi0aNgTSJi/gnzFZyJiIhEgZYVFhEREYljCtZERERE4piCNREREZE4pmBNREREJI4pWBMRERGJYwrWREREROKYgjURERGROKZgTURERCSOKVgTERERiWMK1kRERETi2IC6kLsx5gBQHuWnGQYcjPJz9Gdqn2NTG/VO7XNsaqPeqX2OTW3Uu75qn9HW2rxjbTSggrW+YIxZZ60tjXU94pXa59jURr1T+xyb2qh3ap9jUxv1Lt7aR92gIiIiInFMwZqIiIhIHFOw9sk9EOsKxDm1z7GpjXqn9jk2tVHv1D7HpjbqXVy1j8asiYiIiMQxZdZERERE4tigD9aMMcXGmFXGmC3GmM3GmMXBx4caY14xxmwP/swJPn6yMeZvxpg2Y8x3DtvXw8aY/caY92NxLNEQqfbpaT8DQQTbKMUY87YxZlNwP7fE6pgiKZLvsWB5gjHmHWPMsr4+lmiJ8OfQLmPMe8aYjcaYdbE4nkiLcPtkG2P+YIzZaowpM8acEYtjirQIfg6dFHzthG71xpjrYnVckRLh19C3gvt43xiz1BiTEvX6D/ZuUGPMCGCEtXaDMSYTWA8sAP4TqLbW/tgYcyOQY639njEmHxgd3KbGWvvTLvs6C2gEHrPWTuzrY4mGSLVPT/ux1m6JwWFFVATbyADp1tpGY0wi8Dqw2Fq7JgaHFTGRfI8F93c9UAoMsdZe0JfHEi0R/hzaBZRaawfMGloRbp9HgdXW2geNMUlAmrW2tq+PKdIi/T4L7jMBqARmWmujvYZpVEXwc7oQ99k83lrbYoz5HfCitfY30az/oM+sWWurrLUbgvcbgDKgEPg88Ghws0dx/zCstfuttWuBjqPs6zWgui/q3Vci1T697Kffi2AbWWttY/DXxOCt359NRfI9ZowpAuYDD/ZB1ftMJNtoIIpU+xhjsoCzgIeC27UPhEANovYamgvs6O+BGkS8fbxAqjHGC6QBH0W5+grWujLGjAGmAm8Bw621VcGivcDwGFUrbkSqfQ7bz4ByvG1kXBffRmA/8Iq1dkC1UQReQ/8LfBcIRKN+8SACbWSBFcaY9caYK6NSyRg6zvYZCxwAHjGuK/1BY0x6tOoaKxH8LvsCsDSilYsDx9M+1tpK4KfAbqAKqLPWrohaZYMUrAUZYzKAp4HrrLX1Xcus6yvu9xmO4xGp9ultP/1dJNrIWuu31k4BioAZxpgB0Z0Ox98+xpgLgP3W2vXRq2VsReh99k/W2mnA+cDXg8MzBoQItI8XmAbcb62dCjQBN0ajrrESwc/qJOAi4PcRr2QMReBzKAeXjRsLjATSjTGXR6m6YQrWgOD4oKeBx621zwQf3hfs4w71de+PVf1iLVLt08N+BoRIv4aCXTOrgM9Guq6xEKH2mQ1cFByT9SRwtjHmt1Gqcp+L1GsoeOaPtXY/8CwwIzo17lsRap8KoKJLxvoPuOBtQIjw59D5wAZr7b7I1zQ2ItQ+5wB/t9YesNZ2AM8As6JV55BBH6wFB3U/BJRZa3/Wpeh5YFHw/iLgub6uWzyIVPv0sp9+L4JtlGeMyQ7eTwXOBbZGvsZ9K1LtY639L2ttkbV2DK575s/W2qif0faFCL6G0oODpwl2780D+v3s9Ai+hvYCe4wxJwUfmgv0+0lOEJXvsssYQF2gEWyf3cDpxpi04D7n4sa/RZe1dlDf34mGlgAAAmRJREFUgH/CpT3fBTYGb58DcoGVwHbgVWBocPsC3NlZPVAbvD8kWLYU14fdEXz8y7E+vnhpn572E+vji7M2mgS8E9zP+8BNsT62eGqfw/b5aWBZrI8t3toIOAHYFLxtBv5frI8tntonWDYFWBfc1x9xs/9ifoxx1kbpwCEgK9bHFaftcwvuRPp94P+A5GjXf9Av3SEiIiISzwZ9N6iIiIhIPFOwJiIiIhLHFKyJiIiIxDEFayIiIiJxTMGaiIiISBxTsCYig45xXjfGnN/lsUuNMS/Hsl4iIkejpTtEZFAKXsrr97hrBHpxa9x91lq74zj26bXW+iJURRERQJk1ERmkrLXvAy8A3wNuAh6z1u4wxiwyxrxtjNlojLnPGOMBMMY8YIxZZ4zZbIy5KbQfY0yFMebHxph3gItjcjAiMqB5Y10BEZEYugXYALQDpcFs28XALGutzxjzAO7SVk8AN1prq40xXmCVMeYP1trQpYr2W3dhcBGRiFOwJiKDlrW2yRjzFNBorW0zxpwDTAfWucv+kQrsCW5+mTHmy7jPzZHAeDqvK/lU39ZcRAYTBWsiMtgFgjcAAzxsrf1+1w2MMZ8CFgMzrLW1xpjfAildNmnqk5qKyKCkMWsiIp1eBRYaY4YBGGNyjTGjcBdJbwDqjTEjgPNiWEcRGWSUWRMRCbLWvmeMuQV4NTixoAO4GliH6/LcCpQDb8SuliIy2GjpDhEREZE4pm5QERERkTimYE1EREQkjilYExEREYljCtZERERE4piCNREREZE4pmBNREREJI4pWBMRERGJYwrWREREROLY/wfvSA9A6DXRiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop_canada_air_travel = travel['canada-air'] / travel['total']\n",
    "prop_canada_travel = travel['canada'] / travel['total']\n",
    "prop_death_canada = canada_deaths['deaths'] / all_deaths['deaths']\n",
    "fig4, ax4 = plt.subplots(figsize=(10,6))\n",
    "ax4.plot(travel['year'][9:17], prop_canada_travel[9:17] * 100, 'o--', label='Travelling to Canada')\n",
    "ax4.plot(travel['year'][9:17], prop_canada_air_travel[9:17] * 100, 'o--', label='Travelling to Canada by air')\n",
    "ax4.plot(travel['year'][9:17], prop_death_canada[9:17] * 100, 'o--', label='Deaths in Canada')\n",
    "ax4.set_xlabel('Year')\n",
    "ax4.set_ylabel('Proportion of US citizens')\n",
    "ax4.legend()\n",
    "plt.show(fig4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This only gives us Canada's proportions, but what if we plot both mexico and canada on the same graph? What if we do two things to try to control for all of our data issues?\n",
    "1. only look at travel by air to both countries\n",
    "2. only look at deaths in Mexico in the top 10 tourist destination cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plot Canada deaths and travel by air versus Mexico deaths (in top tourist locations) and travel by air"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "year = []\n",
    "ndeaths = []\n",
    "for y in range(2002, 2019, 1):\n",
    "    start_date = str(y) + '-01-01'\n",
    "    end_date = str(y) + '-12-31'\n",
    "    df_mexico_tourist = df_mexico.loc[(df_mexico['Date'] >= start_date) & (df_mexico['Date'] <= end_date)]\n",
    "    nd_tour = (len(df_mexico_tourist[df_mexico_tourist['City'].str.contains('Cancun')]) +\n",
    "               len(df_mexico_tourist[df_mexico_tourist['City'].str.contains('Cabo San Lucas')]))\n",
    "    year.append(y)\n",
    "    ndeaths.append(nd_tour)\n",
    "data = {'year':year, 'deaths':ndeaths}\n",
    "mexico_tourist_deaths = pd.DataFrame(data)\n",
    "prop_tour_deaths_mexico = mexico_tourist_deaths['deaths'] / all_deaths['deaths']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5, ax5 = plt.subplots(figsize=(10,6))\n",
    "ax5.plot(travel['year'][9:17], prop_mexico_air_travel[9:17] * 100, 'or-', label='Travelling to Mexico by air')\n",
    "ax5.plot(travel['year'][9:17], prop_tour_deaths_mexico[9:17] * 100, 'or--', label='Deaths in Mexico tourist locations')\n",
    "ax5.plot(travel['year'][9:17], prop_canada_air_travel[9:17] * 100, 'ok--', label='Travelling to Canada by air')\n",
    "ax5.plot(travel['year'][9:17], prop_death_canada[9:17] * 100, 'ok-', label='Deaths in Canada')\n",
    "ax5.set_xlabel('Year')\n",
    "ax5.set_ylabel('Proportion of US citizens')\n",
    "ax5.legend(loc='right', bbox_to_anchor=(0.9,0.7))\n",
    "plt.show(fig5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What is this telling us?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "top tourist spots in mexico are safer than going to canada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How do we understand our Spring Break risk even using the very conservative numbers:\n",
    "\n",
    "In 2018:\n",
    "\n",
    "*   196 Total deaths in Mexico \n",
    "*   67 Total homicides in Mexico \n",
    "*   36,923,011 US travelers to Mexico\n",
    "\n",
    "That means there were\n",
    "\n",
    "\n",
    "*   .53 Deaths per 100,000 travelers\n",
    "*   .18 Homicides per 100,000 travelers\n",
    "\n",
    "What could we compare that to?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exercise:\n",
    "\n",
    "Find something to compare that to.\n",
    "\n",
    "[This](https://ourworldindata.org/does-the-news-reflect-what-we-die-from) is a good place to start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview8.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Communication of this Data Science Information\n",
    "\n",
    "Now that we have found the real truth behind the data using data science techniques, how do we properly communicate this information?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We pick a headline that asks the question we wanted to know and gives a teaser about what data science question we are choosing. \n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview9.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "1.  *The issue in a nutshell*\n",
    "    - **a tragic death created headlines about Mexico being dangerous**\n",
    "    \n",
    "2.  *The question we need to answer*\n",
    "    - **is Mexico dangerous and will your be at risk**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "3. *What is the data?*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview10.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "4. *What are issues with data?*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview11.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "5. *What other data do we need?*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview12.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "6. *Deeper dive into the data with a further question*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview13.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "7. *Issues with the conclusion from the data*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview14.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "8. *Why do we care? How is this important to the stakeholders?*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview15.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "9. *Overall data science issue at play*\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview16.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Homework 1: Communicate the Data Science Case\n",
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview17.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The Transport for London (TFL) (authorizing authority for taxis, transportation etc.) has stripped Uber of its license to operate. \n",
    " \n",
    "Why, while there are a lot of theories about it, TFL said that it was because Uber was dangerous. And the public went along with it. Why? Because somehow they believe that Uber dangerous. \n",
    " \n",
    "Where did that idea come from?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview18.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The licensed taxi drivers association (LTDA) but up a very striking billboard in London:\n",
    "\n",
    "“Last year there were 154 rapes and sexual assaults by minicab drivers in London. At least 32 of these were uber drivers. Don’t take a risk with a minicab, only use a black cab”\n",
    " \n",
    "For some context, taxis are black cabs and minicabs (which include ubers)\n",
    "Here is an image of a crying young woman who has obviously just been sexually assaulted by one of these minicab or uber drivers.  \n",
    "\n",
    "If you notice the Met Police logo and the Transport for London logo are on this very clearly. \n",
    " \n",
    "Pretty powerful stuff there. \n",
    " \n",
    "Very powerful. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Except it wasn’t true. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview19.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What the freedom of information request from the Met police actually said is that over the year there had been “154 allegations of rape or sexual assault had been made where the suspect was alleged to be a taxi driver. 32 of these stated they were by an uber driver.“\n",
    "\n",
    "The black cabs, Licensed Taxi Driver’s Association, took themselves out of it. \n",
    "\n",
    "When eventually the London Taxi Driver’s Association were asked about it, they said sorry, it was a mistype. They were then told to take it down and they did. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview20.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "And just  replaced it with another one.\n",
    " \n",
    "“Last year there were 32 allegations of sexual assault made against Uber drivers. Why up your risk with a minicab?”\n",
    " \n",
    "Ok, that is the truth, fair enough. So is it true that you do up your risk with Uber?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/stat10/DS10-Case-Studies/master/Images/preview21.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's look"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "*How many Assaults are Uber Drivers responsible for?*\n",
    "Just over 20% of sexual assaults by taxis drivers are by an Uber driver - is that disproportionately high or low compared to the number of journeys? Because, if for example, uber does all the journeys, all the taxis rides, the black cabs and other minicab drivers are out of business, than Uber should be associated with 100% of the sexual assaults by taxis in London.\n",
    " \n",
    "According to TFL, every week there are 2-3 million journeys in all kinds of taxis including Uber. \n",
    " \n",
    "Uber in London do more than a million of these. \n",
    " \n",
    "So Uber does more than 1/3 of all taxi journeys in London but they are only associated with 1/5 of the sexual assaults. \n",
    " \n",
    "So no reason to believe that Uber drivers are any more dangerous than any other kind of taxi driver, actually quite the opposite. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "*What about the other headlines “Rates of Sexual Assault by taxis at an all time high!”*\n",
    " \n",
    "In 2015 136 reports of rape or sexual assault\n",
    "But by definition, a 5 year high means that it was as high 5 years ago. \n",
    "In 2009 (3 yrs before uber began operating in london) the number of sexual assaults and rapes was the same 136 \n",
    " \n",
    "Uber in no way contributed to an increase in sexual assaults by taxi drivers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Homework Assignment:\n",
    "Write an article about the Uber ban in London like the Mexico for Spring break article. Include the same questions/issues in the Mexico for Spring break article including data, data issues, and risk. "
   ]
  }
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