NB06-Cholera-Case-Study-Scipy-Spatial.ipynb

{
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
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   "source": [
    "%run ./resources/library.py\n",
    "style_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 6: Using SciPy-Spatial to create a Voronoi overlay on Soho District, London (Work-in-Progress)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SciPy** (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering (from SciPy [website](https://www.scipy.org/)). \n",
    "\n",
    "These are some of the core packages in SciPy (ones we have used are in <font color='red'>red</font>):\n",
    "1. <font color='red'>Numpy</font>\n",
    "2. SciPy Library\n",
    "3. <font color='red'>Matplotlib</font>\n",
    "4. <font color='red'>IPython</font>\n",
    "5. Sympy\n",
    "6. <font color='red'>Pandas</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[SciPy Spatial](https://docs.scipy.org/doc/scipy/reference/spatial.html#module-scipy.spatial), from the SciPy package consists of spatial algorithms and data structures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voronoi Diagrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Wikipedia: \"In mathematics, a <font color='red'>Voronoi diagram</font> is a __partitioning__ of a plane into regions based on distance to points in a specific subset of the plane. That set of points (called seeds, sites, or generators) is specified beforehand, and for each seed there is a corresponding region consisting of all points closer to that seed than to any other. These regions are called Voronoi cells.\"\n",
    "\n",
    "In outbreak investigations, Voronoi diagrams can be used to identify and correlate potential sources of infections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumps_df = pd.read_pickle('outputs/pumps_df.pickle')\n",
    "deaths_df = pd.read_pickle('outputs/mean_center_df.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>250</td>\n",
       "      <td>-0.136668</td>\n",
       "      <td>51.513341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>251</td>\n",
       "      <td>-0.139586</td>\n",
       "      <td>51.513876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252</td>\n",
       "      <td>-0.139671</td>\n",
       "      <td>51.514906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>253</td>\n",
       "      <td>-0.131630</td>\n",
       "      <td>51.512354</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>255</td>\n",
       "      <td>-0.135919</td>\n",
       "      <td>51.511542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>256</td>\n",
       "      <td>-0.133962</td>\n",
       "      <td>51.510019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>257</td>\n",
       "      <td>-0.138199</td>\n",
       "      <td>51.511295</td>\n",
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      "text/plain": [
       "   FID       LON        LAT\n",
       "0  250 -0.136668  51.513341\n",
       "1  251 -0.139586  51.513876\n",
       "2  252 -0.139671  51.514906\n",
       "3  253 -0.131630  51.512354\n",
       "4  254 -0.133594  51.512139\n",
       "5  255 -0.135919  51.511542\n",
       "6  256 -0.133962  51.510019\n",
       "7  257 -0.138199  51.511295"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pumps_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pump_points = pumps_df[['LAT', 'LON']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[51.513341, -0.136668],\n",
       "       [51.513876, -0.139586],\n",
       "       [51.514906, -0.139671],\n",
       "       [51.512354, -0.13163 ],\n",
       "       [51.512139, -0.133594],\n",
       "       [51.511542, -0.135919],\n",
       "       [51.510019, -0.133962],\n",
       "       [51.511295, -0.138199]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "death_points = deaths_df[['LAT','LON']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51.513418, -0.13793 ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_points[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pump_vor = Voronoi(pump_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[51.52725397, -0.13132661],\n",
       "       [51.51085006, -0.13245913],\n",
       "       [51.51766511, -0.13320518],\n",
       "       [51.5084689 , -0.13673946],\n",
       "       [51.51209239, -0.13713201],\n",
       "       [51.51122125, -0.13459749],\n",
       "       [51.51296157, -0.13504436],\n",
       "       [51.51452884, -0.13795826],\n",
       "       [51.51293002, -0.1382514 ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_vor.vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 0, -1, 1],\n",
       " [6, 4, 3, 5],\n",
       " [5, 1, -1, 3],\n",
       " [6, 2, 1, 5],\n",
       " [-1, 8, 7],\n",
       " [8, 4, 6, 2, 0, 7],\n",
       " [-1, 0, 7],\n",
       " [-1, 3, 4, 8],\n",
       " []]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_vor.regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draw the Voronoi cells and overlay death points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [8,6]\n",
    "fig = voronoi_plot_2d(pump_vor, point_size=15)\n",
    "plt.scatter(deaths_df['LAT'], deaths_df['LON'], c='r', alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. SciPy Spatial Voronoi web page. URL: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.Voronoi.html#scipy.spatial.Voronoi\n",
    "2. https://ipython-books.github.io/145-computing-the-voronoi-diagram-of-a-set-of-points/\n",
    "3. http://comet.lehman.cuny.edu/owen/teaching/datasci/makeVor.py\n",
    "4. https://python-visualization.github.io/folium/quickstart.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*For case study suggestions for improvement, please contact Herman Tolentino, Jan MacGregor, James Tobias or Zhanar Haimovich.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.7"
  },
  "nbTranslate": {
   "displayLangs": [
    "*"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "en",
   "targetLang": "fr",
   "useGoogleTranslate": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}