explore_mesa_schelling_on_the_netherlands.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cf0d3dda",
   "metadata": {},
   "source": [
    "# Adaptation of the ABM shelling modelling on the Netherlands"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ca01a96d",
   "metadata": {},
   "source": [
    "This notebook is directly inspired by the [Schelling segregation model example](https://github.com/projectmesa/mesa-schelling-example) made available by the Mesa package, and the [basic tutorial](https://mesa-geo.readthedocs.io/en/latest/tutorials/intro_tutorial.html#getting-started) of Mesa-Geo: GIS Extension for Mesa Agent-Based Modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import mesa\n",
    "import mesa_geo as mg\n",
    "import geopandas as gpd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0484001c",
   "metadata": {},
   "source": [
    "## Load the GIS data of the Netherlands\n",
    "\n",
    "We can get GIS info about the Netherlands from the [Princeton University Library](https://maps.princeton.edu/catalog/stanford-st293bj4601)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7b322f8",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<MULTIPOLYGON (((6.138 52.844, 6.157 52.83, 6.158 52.83, 6.159 52.829, 6.161...>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.read_file(\"netherlands-geo.json\")\n",
    "# Access the geometry information of the Netherlands\n",
    "gdf.geometry.values[1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5a2cb091",
   "metadata": {},
   "source": [
    "#### From the basic example of the mesa-geo package\n",
    "\n",
    "Create Agent and Model classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "693b3b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "class State(mg.GeoAgent):\n",
    "    def __init__(self, unique_id, model, geometry, crs):\n",
    "        super().__init__(unique_id, model, geometry, crs)\n",
    "\n",
    "\n",
    "class GeoModel(mesa.Model):\n",
    "    def __init__(self):\n",
    "        self.space = mg.GeoSpace()\n",
    "\n",
    "        ac = mg.AgentCreator(agent_class=State, model=self)\n",
    "        agents = ac.from_GeoJSON(GeoJSON=gdf, unique_id=\"name_1\")\n",
    "        self.space.add_agents(agents)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5724f647",
   "metadata": {},
   "source": [
    "Init the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7be84d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/evaviviani/src/mesa-geo/mesa_geo/geospace.py:138: UserWarning: Converting State from crs EPSG:4326 to the crs of GeoSpace - EPSG:3857. Please check your crs settings if this is unintended, or set `GeoSpace.warn_crs_conversion` to `False` to suppress this warning message.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# init model\n",
    "m = GeoModel()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eae1e565",
   "metadata": {},
   "source": [
    "Agents in this example are Dutch's regions. These are contained in the GIS file, together with their spatial configuration and stored in the `unique_id` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba484c9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This region is called: Overijssel\n"
     ]
    },
    {
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      "text/plain": [
       "<MULTIPOLYGON (((683289.175 6954157.175, 685421.396 6951656.729, 685485.041 ...>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent = m.space.agents[1]\n",
    "print(f\"This region is called: \" + str(agent.unique_id))\n",
    "agent.geometry\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c4d53191",
   "metadata": {},
   "source": [
    "We can get `neighbours` which in this case are regions confining the above one via the `get_neighbors` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24841bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Drenthe', 'Flevoland', 'Friesland', 'Gelderland', 'IJsselmeer']\n"
     ]
    }
   ],
   "source": [
    "neighbours = m.space.get_neighbors(agent)\n",
    "print([a.unique_id for a in neighbours])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "493055df",
   "metadata": {},
   "source": [
    "Or we can get confining regions which are within a specific distance, e.g., 600000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ff94007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Zeeland',\n",
       " 'Zeeuwse meren',\n",
       " 'Zuid-Holland',\n",
       " 'Noord-Holland',\n",
       " 'Noord-Brabant',\n",
       " 'Utrecht',\n",
       " 'IJsselmeer',\n",
       " 'Flevoland',\n",
       " 'Friesland',\n",
       " 'Limburg',\n",
       " 'Gelderland',\n",
       " 'Overijssel',\n",
       " 'Drenthe',\n",
       " 'Groningen']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[a.unique_id for a in m.space.get_neighbors_within_distance(agent, 600000)]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a1c662ff",
   "metadata": {},
   "source": [
    "## Adapt the schelling agent example from mesa with the geo info of the Netherlands\n",
    "\n",
    "From the Mesa schelling model example, we add the GIS info with mesa-geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08373887",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SchellingAgent(mg.GeoAgent):\n",
    "    \"\"\"Schelling segregation agent.\"\"\"\n",
    "\n",
    "    def __init__(self, unique_id, model, geometry, crs, agent_type=None):\n",
    "        \"\"\"Create a new Schelling agent.\n",
    "        Args:\n",
    "            unique_id: Unique identifier for the agent.\n",
    "            agent_type: Indicator for the agent's type (minority=1, majority=0)\n",
    "        \"\"\"\n",
    "        super().__init__(unique_id, model, geometry, crs)\n",
    "        self.atype = agent_type\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"Advance agent one step.\"\"\"\n",
    "        print(\"Hi, I am agent \" + str(self.unique_id) + \" And my happiness is \" + str(self.model.happy))\n",
    "        similar = 0\n",
    "        different = 0\n",
    "        neighbors = self.model.space.get_neighbors(self)\n",
    "        if neighbors:\n",
    "            for neighbor in neighbors:\n",
    "                if neighbor.atype is None:\n",
    "                    continue\n",
    "                elif neighbor.atype == self.atype:\n",
    "                    similar += 1\n",
    "                else:\n",
    "                    different += 1\n",
    "\n",
    "        # If unhappy, move:\n",
    "        if similar < different:\n",
    "            # Select an empty region\n",
    "            empties = [a for a in self.model.space.agents if a.atype is None]\n",
    "            # Switch atypes and add/remove from scheduler\n",
    "            new_region = random.choice(empties)\n",
    "            new_region.atype = self.atype\n",
    "            self.model.schedule.add(new_region)\n",
    "            self.atype = None\n",
    "            self.model.schedule.remove(self)\n",
    "        else:\n",
    "            self.model.happy += 1\n",
    "\n",
    "    def __repr__(self):\n",
    "        return \"Agent \" + str(self.unique_id)\n",
    "\n",
    "\n",
    "class GeoSchelling(mesa.Model):\n",
    "    \"\"\"Model class for the Schelling segregation model.\"\"\"\n",
    "\n",
    "    def __init__(self, density=0.6, minority_pc=0.2, export_data=False):\n",
    "        \n",
    "        if density>1:\n",
    "            raise ValueError('density should be < 1.')\n",
    "        \n",
    "        if minority_pc > 1:\n",
    "            raise ValueError('mnority_pc should be < 1.')\n",
    "            \n",
    "        self.density = density\n",
    "        self.minority_pc = minority_pc\n",
    "        self.export_data = export_data\n",
    "\n",
    "        self.schedule = mesa.time.RandomActivation(self)\n",
    "        self.space = mg.GeoSpace(warn_crs_conversion=False)\n",
    "\n",
    "        self.happy = 0\n",
    "        self.datacollector = mesa.DataCollector({\"happy\": \"happy\"})\n",
    "\n",
    "        self.running = True\n",
    "\n",
    "        # Set up the grid with patches for every NUTS region\n",
    "        ac = mg.AgentCreator(SchellingAgent, model=self)\n",
    "        agents = ac.from_file(\"netherlands-geo.json\")\n",
    "        self.space.add_agents(agents)\n",
    "\n",
    "        # Set up agents\n",
    "        for agent in agents:\n",
    "            if random.random() < self.density:\n",
    "                if random.random() < self.minority_pc:\n",
    "                    agent.atype = 1\n",
    "                else:\n",
    "                    agent.atype = 0\n",
    "                self.schedule.add(agent)\n",
    "\n",
    "    def export_agents_to_file(self) -> None:\n",
    "        self.space.get_agents_as_GeoDataFrame(agent_cls=SchellingAgent).to_crs(\n",
    "            \"epsg:4326\"\n",
    "        ).to_file(\"output/schelling_agents_nl.geojson\", driver=\"GeoJSON\")\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"Run one step of the model.\n",
    "        If All agents are happy, halt the model.\n",
    "        \"\"\"\n",
    "        self.happy = 0  # Reset counter of happy agents\n",
    "        self.schedule.step()\n",
    "        self.datacollector.collect(self)\n",
    "\n",
    "        if self.happy == self.schedule.get_agent_count():\n",
    "            self.running = False\n",
    "\n",
    "        if not self.running and self.export_data:\n",
    "            self.export_agents_to_file()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0fe5aa93",
   "metadata": {},
   "source": [
    "### Run the model and print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ad23e23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "density = 0.8\n",
      "Hi, I am agent 7 And my happiness is 0\n",
      "Hi, I am agent 10 And my happiness is 1\n",
      "Hi, I am agent 1 And my happiness is 2\n",
      "Hi, I am agent 12 And my happiness is 3\n",
      "Hi, I am agent 8 And my happiness is 4\n",
      "Hi, I am agent 2 And my happiness is 5\n",
      "Hi, I am agent 0 And my happiness is 6\n",
      "Hi, I am agent 13 And my happiness is 7\n",
      "Hi, I am agent 6 And my happiness is 7\n",
      "Hi, I am agent 5 And my happiness is 8\n",
      "Hi, I am agent 4 And my happiness is 9\n",
      "Hi, I am agent 11 And my happiness is 10\n",
      "Hi, I am agent 8 And my happiness is 0\n",
      "Hi, I am agent 4 And my happiness is 1\n",
      "Hi, I am agent 12 And my happiness is 2\n",
      "Hi, I am agent 5 And my happiness is 3\n",
      "Hi, I am agent 0 And my happiness is 4\n",
      "Hi, I am agent 6 And my happiness is 5\n",
      "Hi, I am agent 10 And my happiness is 6\n",
      "Hi, I am agent 2 And my happiness is 7\n",
      "Hi, I am agent 1 And my happiness is 8\n",
      "Hi, I am agent 7 And my happiness is 9\n",
      "Hi, I am agent 3 And my happiness is 10\n",
      "Hi, I am agent 11 And my happiness is 10\n",
      "Hi, I am agent 1 And my happiness is 0\n",
      "Hi, I am agent 5 And my happiness is 1\n",
      "Hi, I am agent 6 And my happiness is 2\n",
      "Hi, I am agent 0 And my happiness is 3\n",
      "Hi, I am agent 8 And my happiness is 4\n",
      "Hi, I am agent 13 And my happiness is 5\n",
      "Hi, I am agent 11 And my happiness is 5\n",
      "Hi, I am agent 10 And my happiness is 6\n",
      "Hi, I am agent 12 And my happiness is 7\n",
      "Hi, I am agent 4 And my happiness is 8\n",
      "Hi, I am agent 7 And my happiness is 9\n",
      "Hi, I am agent 2 And my happiness is 10\n",
      "Hi, I am agent 2 And my happiness is 0\n",
      "Hi, I am agent 5 And my happiness is 1\n",
      "Hi, I am agent 11 And my happiness is 2\n",
      "Hi, I am agent 0 And my happiness is 3\n",
      "Hi, I am agent 1 And my happiness is 4\n",
      "Hi, I am agent 8 And my happiness is 5\n",
      "Hi, I am agent 10 And my happiness is 6\n",
      "Hi, I am agent 6 And my happiness is 7\n",
      "Hi, I am agent 9 And my happiness is 8\n",
      "Hi, I am agent 4 And my happiness is 8\n",
      "Hi, I am agent 7 And my happiness is 9\n",
      "Hi, I am agent 12 And my happiness is 10\n",
      "Hi, I am agent 0 And my happiness is 0\n",
      "Hi, I am agent 1 And my happiness is 1\n",
      "Hi, I am agent 2 And my happiness is 2\n",
      "Hi, I am agent 10 And my happiness is 3\n",
      "Hi, I am agent 12 And my happiness is 4\n",
      "Hi, I am agent 11 And my happiness is 5\n",
      "Hi, I am agent 5 And my happiness is 6\n",
      "Hi, I am agent 6 And my happiness is 7\n",
      "Hi, I am agent 8 And my happiness is 8\n",
      "Hi, I am agent 4 And my happiness is 9\n",
      "Hi, I am agent 3 And my happiness is 10\n",
      "Hi, I am agent 7 And my happiness is 10\n"
     ]
    }
   ],
   "source": [
    "n_regions = len(neighbours)\n",
    "n_agents = 4  \n",
    "happiness_th = 0.9\n",
    "print(\"density = \" + str(n_agents/n_regions))\n",
    "\n",
    "model = GeoSchelling(density = n_agents/n_regions, minority_pc = happiness_th)\n",
    "for i in range(5):\n",
    "    model.step()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f1c858a3",
   "metadata": {},
   "source": [
    "In sum, we can easily extend the Mesa package with Mesa-Geo to adapt it to the Netherlands geo info, in case we would need it. This demonstrate the easiness by which we can use Mesa extentions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685afc26",
   "metadata": {},
   "source": []
  }
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