analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Schelling Segregation Model\n",
    "\n",
    "## Background\n",
    "\n",
    "The Schelling (1971) segregation model is a classic of agent-based modeling, demonstrating how agents following simple rules lead to the emergence of qualitatively different macro-level outcomes. Agents are randomly placed on a grid. There are two types of agents, one constituting the majority and the other the minority. All agents want a certain number (generally, 3) of their 8 surrounding neighbors to be of the same type in order for them to be happy. Unhappy agents will move to a random available grid space. While individual agents do not have a preference for a segregated outcome (e.g. they would be happy with 3 similar neighbors and 5 different ones), the aggregate outcome is nevertheless heavily segregated.\n",
    "\n",
    "## Implementation\n",
    "\n",
    "This is a demonstration of running a Mesa model in an IPython Notebook. The actual model and agent code are implemented in Schelling.py, in the same directory as this notebook. Below, we will import the model class, instantiate it, run it, and plot the time series of the number of happy agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from model import SchellingModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we instantiate a model instance: a 10x10 grid, with an 80% chance of an agent being placed in each cell, approximately 20% of agents set as minorities, and agents wanting at least 3 similar neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = SchellingModel(10, 10, 0.8, 0.2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to run the model until all the agents are happy with where they are. However, there's no guarentee that a given model instantiation will *ever* settle down. So let's run it for either 100 steps or until it stops on its own, whichever comes first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46\n"
     ]
    }
   ],
   "source": [
    "while model.running and model.schedule.steps < 100:\n",
    "    model.step()\n",
    "print(model.schedule.steps) # Show how many steps have actually run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has a DataCollector object, which checks and stores how many agents are happy at the end of each step. It can also generate a pandas DataFrame of the data it has collected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_out = model.datacollector.get_model_vars_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>happy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   happy\n",
       "0     59\n",
       "1     62\n",
       "2     64\n",
       "3     64\n",
       "4     66"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_out.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can plot the 'happy' series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113b57470>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hjwQmAldHxETg98Dc2vaxEfF+4IvArblFmdEXvwhf/WprG6H95TVr1CUXM2tUmhr608Ca\niOhdZ/A7JAl9DXAbQETcL2mrpF0j4i2PVe7pc6tcqVSoVCpNhl3f2rXw8MPJcrd56501Only647p\nkotZd6pWq1Sr1aaOkWpikaQfAmdGxBOS5gGjgV8De0bEPEn7A/dGxLsG2LetE4sWLEialbe24e+F\nlSvh1FMbX+xrIH6WqJlBYxOL0o5yOQe4SdIo4EngDOA1YKGkh4DXgdOynDgvS5bASSe151wTJyZD\nF3/1q9Yk4AjfoZtZ4zpq6v8f/wjjxsGTT8Kuu7bllJx5JvzVX8F55zV/rJdeSpbN3bCh+WOZ2fDW\n9VP/77sPDjqofckcWjt80Q1RM2tGRyX0JUuSBNtOrZw16nKLmTWjYxJ6RDLi5IQT2nvevrNGm+U7\ndDNrRsck9J//HEaNggMOaP+5TzihNWUX36GbWTM6JqEvXZqUW1q9bksaJ5zQmlmjniVqZs3omIRe\nRP28V6tmjbrkYmbN6IiEvnYtPPEETJpUXAyteNaoSy5m1oyOSOjf+x4cd1zypJ+iTJ0Kd96ZNGcb\n5Tt0M2tGRyT0IsstvSZOTEa83HJLY/t7lqiZNWvYzxQtYnZoPStWwPTpydouWVd69CxRM+urK2eK\nFjE7tJ6jjoIpU+Dii7Pv63KLmTVr2Cf0MpRb+po/H264IVnCNwuXW8ysWcM6oRc1O3Qwu+2WPClp\n9uxsDVLfoZtZs4Z1Qi9yduhgPvc52LgxW4PUd+hm1qxhndCLnB06mG22gauvhjlzksSehmeJmlmz\nhnVCL1v9vK+jjoK/+Zv0DVKXXMysWakSuqQxkhZLWi3pEUlH9vne+bXnie6SX5hvVYbZoUPJ0iB1\nycXMmpX2Dv1K4K6IOAA4GFgNIGk8cBzwVD7h1VeG2aFD2X339A1S36GbWbOGTOiSdgImRcQigIjY\nHBG9leErgDk5xldXmcstfaVpkHqWqJm1QpqHRO8NrJe0iOTufCXweeBYYE1EPKQcupKvvw4vvjjw\n9zZtSiYULVjQ8tO2XG+DdPp0eP/74e1vf+tnNm5M/tLYfvv2x2dmnSNNQh8JTATOioiVkq4AeoDJ\nJOWWXnWzek9PzxuvK5UKlUplyJOefHKyHO2oUQN//8QTyzE7NI2jjoIZM+Doo+t/psy9ADPLX7Va\npVqtNnWMIddykTQOWBER+9TeH02S0A8EXiNJ5OOBZ4AjIuL5fvtnXsvllVeSevKzz8KOO2ba1cys\nI+SylktErAPWSNq/tukY4IGI2CMi9omIvYGngQn9k3mjli2DD3zAydzMLIs0JReAc4CbJI0CngTO\n6Pf9YJCSS1bDpeFpZlYmpVs+d8uWZLTH/fcnj3UzM+tGHbF87k9/Cnvs4WRuZpZV6RJ62VZPNDMb\nLkqX0F0/NzNrTKkS+m9/C88/D0ccUXQkZmbDT6kS+pIl8NGPJrMrzcwsm9IldJdbzMwaU5phixs3\nwp57enaomRkM82GL997r2aFmZs0oTUJ3ucXMrDmlKLl4dqiZ2ZsN25KLZ4eamTWvFAnd5RYzs+aV\nJqF7ur+ZWXMKT+i/+Y1nh5qZtULhCX3pUjj+eM8ONTNrVuEJ3fVzM7PWSDVsUdIY4NskzxHdCswE\nTgKmAq8DvwbOiIiNA+xbd9iiZ4eamQ0sz2GLVwJ3RcQBwMHAY8Ay4L0RcQjwS+DCLCcGPzvUzKyV\nhkzoknYCJkXEIoCI2BwRGyLiBxGxtfaxnwDjs5586VKXW8zMWiXNHfrewHpJiyQ9KOlaSdv1+8xM\n4PtZTrxlC9x1lxO6mVmrjEz5mYnAWRGxUtLXScorXwGQdBGwKSJurneAnp6eN15XKhUqlQoPPwy7\n7OLZoWZmANVqlWq12tQxhmyKShoHrIiIfWrvjwYuiIipkk4HzgQ+HBGv19l/wKbowoWwfDnceGNT\n8ZuZdaRcmqIRsQ5YI2n/2qZjgEclTQHmANPqJfPBrFwJhx2WdS8zM6snTckF4BzgJkmjgCeBM4CV\nwNuAeyUB/CQiZqU98QMPwIwZGaM1M7O6Clk+d9Mm2HlnWLcOdtgh19ObmQ1Lw2b53Ecfhb32cjI3\nM2ulQhL6Aw/AoYcWcWYzs85VSEJ3Q9TMrPV8h25m1iHa3hR1Q9TMbGjDoinqhqiZWT7antBdbjEz\ny0fbE/rKlU7oZmZ5KOQO3SNczMxar61NUTdEzczSKX1T1A1RM7P8tDWhuyFqZpaftiZ0N0TNzPLT\n9jt0N0TNzPLRtqaoG6JmZumVuinqhqiZWb5SJXRJYyQtlrRa0iOSjpQ0VtIySY9LukfSmMGO4Yao\nmVm+0t6hXwncFREHAAcDjwFzgR9ExHuA5cCFgx3ACd3MLF9DJnRJOwGTImIRQERsjogNwInAdbWP\nXQd8bLDjeA10M7N8pblD3xtYL2mRpAclXStpNDAuItYBRMRaYPd6B9i0CR5+GCZMaE3QZmb2ViNT\nfmYicFZErJR0BUm5pf/wmLrDZc4+u4fttoOvfQ0qlQqVSqXhgM3MOlG1WqVarTZ1jCGHLUoaB6yI\niH1q748mSej7ApWIWCdpD+C+Wo29//6xYEGwfDnceGNTsZqZdY1chi3WyiprJO1f23QM8AhwJ3B6\nbdunge/WO4YbomZm+UtTcgE4B7hJ0ijgSeAMYBvgVkkzgaeAk+vtvHIlnHJKs6Gamdlg2jJTdPTo\nYO1a2HHHXE9lZtYxSjtTdK+9nMzNzPLWloTu+rmZWf6c0M3MOoQTuplZh2hLU3TjxnAN3cwsg0aa\nom19SLSZmaVT2lEuZmaWPyd0M7MO4YRuZtYhnNDNzDqEE7qZWYdwQjcz6xBO6GZmHcIJ3cysQzih\nm5l1iFQPuJD0W2ADsBXYFBFHSDoCuAoYBWwCZkXEyrwCNTOzwaW9Q99K8vzQCRFxRG3bV4F/jIgJ\nwDzgsjwCzEOzD2LNQxljgnLG5ZjScUzplTWurNImdA3w2eeAMbXXOwPPtCqovJXxf14ZY4JyxuWY\n0nFM6ZU1rqzSPlM0gHslbQGujYh/BeYCP5Z0OUnC/0BOMZqZWQppE/oHI+I5SbsByyQ9BnwFODsi\n7pA0HVgIHJdXoGZmNrjMy+dKmge8CnwlIsb02b6h7/s+2712rplZA7IunzvkHbqk0cCIiHhV0vbA\nR4CLgV9J+uuI+KGkY4AnWhGQmZk1Jk3JZRxwe+1OeyRwU0Qsk/Q74GpJbwP+CHw2xzjNzGwIuT+x\nyMzM2iO3maKSpkh6TNITki7I6zxZSfqtpJ9LWiXpvwqKYYGkdZJ+0WfbWEnLJD0u6R5Jb+lHFBDT\nPElPS3qw9t+UNsc0XtJySY9IekjSObXthV2rAWI6u7a96Gu1raSf1n6uH5H0v2rbi7xW9WIq9FrV\nYhhRO/edtfeF/vvrE9OqPjFlvk653KFLGkFSUz8GeBa4H/i7iHis5SfLSNKTwKER8VKBMRxN0li+\nPiIOqm37KvBiRFxa+wU4NiLmFhzTPOCViPjf7YqjX0x7AHtExM8k7QA8AJwInEFB12qQmE6hwGtV\ni210RLwmaRvgx8D5wDSK/bkaKKZjKf5anQccCuwUEdOK/vdXJ6bM//7yukM/AvhlRDwVEZuAfyf5\noS+DgSZJtVVE/F+g/y+UE4Hraq+vAz5WgpgguV6FiIi1EfGz2utXgdXAeAq8VnVi2rP27UIHAETE\na7WX25L8jL9E8T9XA8UEBV4rSeOBjwLf7rO50OtUJybIeJ3ySmx7Amv6vH+aP//QF613ktT9ks4s\nOpg+do+IdZAkDWD3guPpNVvSzyR9u4g/Q3tJejdwCPATYFwZrlWfmH5a21Toter9kx1YC1Qj4lEK\nvlZ1YoJir9UVwBySXNCr6J+pgWKCjNepG1db/GBETCT5bXhWrdRQRmXoVv8LsE9EHELyD7Ko0ssO\nwH8A59buivtfm7ZfqwFiKvxaRcTW2tpK44FJkioUfK36xTRZ0l9T4LWSdDywrvZX1mB3v227ToPE\nlPk65ZXQnwH26vN+PCVZ6yUinqt9fQG4naQ8VAbrJI2DN+q0zxccDxHxQvy5yfKvwOHtjkHSSJLE\neUNEfLe2udBrNVBMZbhWvSJiI3AXcBgl+bmqxfQ94LCCr9UHgWm1XtotwIcl3QCsLfA6DRTT9Y1c\np7wS+v3AfpLepWSc+t8Bd+Z0rtQkja7dWaE/T5J6uKhwePNv4zuB02uvPw18t/8ObfCmmGo/2L0+\nTjHXaiHwaERc2Wdb0dfqLTEVfa0kvaP3T3JJ25Esw7GKAq9VnZh+VuS1iogvRcReEbEPSV5aHhGn\nAkso6DrViem0Rq5T2rVcsga4RdJsYBnJL40FEbE6j3NlNOAkqXYHIelmoALsKun/kSw/PB9YLGkm\n8BRwcgli+pCkQ0iWT/4t8Lk2x/RB4FPAQ7U6bABfIlm6+dYirtUgMX2yyGsFvBO4TlJv0/+GiPg/\ntRgLuVaDxHR9wddqIPMp7jrVc2nW6+SJRWZmHaIbm6JmZh3JCd3MrEM4oZuZdQgndDOzDuGEbmbW\nIZzQzcw6hBO6mVmHcEI3M+sQ/x/op9QrO1TI4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b15898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_out.happy.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For testing purposes, here is a table giving each agent's x and y values at each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_positions = model.datacollector.get_agent_vars_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>AgentID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>(0, 0)</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 2)</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 5)</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 6)</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x  y\n",
       "Step AgentID      \n",
       "0    (0, 0)   3  6\n",
       "     (0, 1)   5  1\n",
       "     (0, 2)   1  3\n",
       "     (0, 5)   4  6\n",
       "     (0, 6)   1  5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_positions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of Homophily on segregation\n",
    "\n",
    "Now, we can do a parameter sweep to see how segregation changes with homophily.\n",
    "\n",
    "First, we create a function which takes a model instance and returns what fraction of agents are segregated -- that is, have no neighbors of the opposite type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mesa.batchrunner import BatchRunner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_segregation(model):\n",
    "    '''\n",
    "    Find the % of agents that only have neighbors of their same type.\n",
    "    '''\n",
    "    segregated_agents = 0\n",
    "    for agent in model.schedule.agents:\n",
    "        segregated = True\n",
    "        for neighbor in model.grid.neighbor_iter(agent.pos):\n",
    "            if neighbor.type != agent.type:\n",
    "                segregated = False\n",
    "                break\n",
    "        if segregated:\n",
    "            segregated_agents += 1\n",
    "    return segregated_agents / model.schedule.get_agent_count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we set up the batch run, with a dictionary of fixed and changing parameters. Let's hold everything fixed except for Homophily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parameters = {\"height\": 10, \"width\": 10, \"density\": 0.8, \"minority_pc\": 0.2, \n",
    "              \"homophily\": range(1,9)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_reporters = {\"Segregated_Agents\": get_segregation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_sweep = BatchRunner(SchellingModel, parameters, iterations=10, \n",
    "                          max_steps=200, model_reporters=model_reporters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "param_sweep.run_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = param_sweep.get_model_vars_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Utism6TmjLrvsMv/0MY15Dd/lpKuri5Urb+LYsd1AL1DF3LmrefzxB1i6dGmk2bxWHk5r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163e1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.homophily, df.Segregated_Agents)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}