Under the hood.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equal Area Cartogram Mapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heuristic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Get the x_coord (latitude) and y_coord (longitude) of each area\n",
    "2. Normalize the x_coords and y_coords to align all points into a rectangular grid with a width of `num_x_grid` and length of `num_y_grid`.\n",
    "3. If a bin (i.e., an x-y coordinate pair) has more than 1 point, select a point from the bin and shunt it to a neighbouring bin if neighbouring bin is empty. If no neighbouring bin is empty find a direction (up, down, left, or right) with the most proportion of empty spaces, shift all existing points in that direction by 1, and then move the selected point into that direction\n",
    "4. Iterate 3. until all bins only have 1 point in them.\n",
    "5. Convert grid to hexmap SVG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from chorogrid import Chorogrid\n",
    "import pandas as pd\n",
    "import geopandas as gpd #not needed if loading from csv or excel\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1\n",
    "Get the x_coord (latitude) and y_coord (longitude) of each area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geodf = gpd.read_file(\"sample_data/gujarat.json\")\n",
    "geodf = geodf[geodf.AC_NO != '0']\n",
    "\n",
    "geodf['centroid'] = geodf['geometry'].apply(lambda x: x.centroid)\n",
    "geodf['latitude'] = geodf['centroid'].apply(lambda x: x.coords.xy[0][0])\n",
    "geodf['longitude'] = geodf['centroid'].apply(lambda x: x.coords.xy[1][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2\n",
    "Normalize the x_coords and y_coords to align all points into a rectangular grid with a width of `num_x_grid` and length of `num_y_grid`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_x_grid = 25#25\n",
    "num_y_grid = 20#20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if num_x_grid * num_y_grid < geodf.shape[0]:\n",
    "    print \"Add more dimensions to the grid. I.e., increase the num_x_grid, num_y_grid, or both\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xmax, xmin = geodf['latitude'].max(), geodf['latitude'].min()\n",
    "ymax, ymin = geodf['longitude'].max(), geodf['longitude'].min()\n",
    "x_range = xmax - xmin\n",
    "y_range = ymax - ymin\n",
    "\n",
    "geodf[\"x_bin\"] = geodf[\"latitude\"].apply(lambda x: int(num_x_grid*(x - xmin)/x_range))\n",
    "geodf[\"y_bin\"] = geodf[\"longitude\"].apply(lambda y: int(num_y_grid*(ymax-y)/y_range))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps 3 and 4\n",
    "If a bin (i.e., an x-y coordinate pair) has more than 1 point, select a point from the bin and shunt it to a neighbouring bin if neighbouring bin is empty. If no neighbouring bin is empty find a direction (up, down, left, or right) with the most proportion of empty spaces, shift all existing points in that direction by 1, and then move the selected point into that direction.\n",
    "\n",
    "Repeat until all bins only have 1 point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def is_valid(coord_points):\n",
    "    for coord in coord_points:\n",
    "        if len(coord_points[coord]) > 1:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def delete_old_point(x, y, ac_to_shunt):\n",
    "    coord_points[str(x) + \"_\" + str(y)].remove(ac_to_shunt)\n",
    "    x_coords_points[x].remove(ac_to_shunt)\n",
    "    y_coords_points[y].remove(ac_to_shunt)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def update_new_point(x, y, ac_to_shunt):\n",
    "    point_position[ac_to_shunt] = {\"x_bin\": x, \"y_bin\": y}\n",
    "    if str(x) + \"_\" + str(y) not in coord_points:\n",
    "        coord_points[str(x) + \"_\" + str(y)] = []\n",
    "    coord_points[str(x) + \"_\" + str(y)].append(ac_to_shunt)\n",
    "    if x not in x_coords_points:\n",
    "        x_coords_points[x] = []\n",
    "    x_coords_points[x].append(ac_to_shunt)\n",
    "\n",
    "    if y not in y_coords_points:\n",
    "        y_coords_points[y] = []\n",
    "    y_coords_points[y].append(ac_to_shunt)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shunt_point(coord):\n",
    "    x, y = coord.split('_')\n",
    "    ac_to_shunt = coord_points[coord][1]\n",
    "    x = int(x)\n",
    "    y = int(y)\n",
    "    \n",
    "    #check if a neighbouring bin is empty and move to neighbouring bin\n",
    "    for point in [(min(x+1, num_x_grid), y), (min(x+1, num_x_grid), min(y+1, num_y_grid)), (min(x+1, num_x_grid), max(0, y-1)), \n",
    "                  (max(0, x-1), y), (max(0, x-1), min(y+1, num_y_grid)), (max(0, x-1), max(0, y-1)), \n",
    "                  (x, min(y+1, num_y_grid)), (x, max(0, y-1))]:\n",
    "        if len(coord_points.get(str(point[0]) + \"_\" + str(point[1]), [])) == 0:\n",
    "            #delete old point\n",
    "            delete_old_point(x, y, ac_to_shunt)\n",
    "            \n",
    "            #add new point\n",
    "            update_new_point(point[0], point[1], ac_to_shunt)\n",
    "            return\n",
    "    \n",
    "    #move to a neighbouring bin in the direction that is most sparse\n",
    "    prop_x_plus_empty = 1.*len([i for i in range(x+1, 51) if len(coord_points.get(str(i) + \"_%d\"%y, [])) == 0])/(num_x_grid-x) if x != num_x_grid else 0\n",
    "    prop_x_minus_empty = 1.*len([i for i in range(x-1, -1, -1) if len(coord_points.get(str(i) + \"_%d\"%y, [])) == 0])/(x) if x != 0 else 0\n",
    "    \n",
    "    prop_y_plus_empty = 1.*len([i for i in range(y+1, 41) if len(coord_points.get(\"%d_\"%x + str(i), [])) == 0])/(num_y_grid-y) if y != num_y_grid else 0\n",
    "    prop_y_minus_empty = 1.*len([i for i in range(y-1, -1, -1) if len(coord_points.get(\"%d_\"%x + str(i), [])) == 0])/(y) if y != 0 else 0\n",
    "    \n",
    "    delete_old_point(x, y, ac_to_shunt)\n",
    "    if prop_x_plus_empty is max(prop_x_plus_empty, prop_x_minus_empty, prop_y_plus_empty, prop_y_minus_empty):\n",
    "        for idx in range(x+1, 51)[::-1]:\n",
    "            if str(idx) + \"_\" + str(y) in coord_points:\n",
    "                for ac in coord_points[str(idx) + \"_\" + str(y)]:\n",
    "                    delete_old_point(idx, y, ac)\n",
    "                    update_new_point(min(idx+1, num_x_grid), y, ac)\n",
    "\n",
    "        update_new_point(min(x+1, num_x_grid), y, ac_to_shunt)\n",
    "        return\n",
    "    if prop_x_minus_empty is max(prop_x_plus_empty, prop_x_minus_empty, prop_y_plus_empty, prop_y_minus_empty):\n",
    "        for idx in range(x-1, -1, -1)[::-1]:\n",
    "            if str(idx) + \"_\" + str(y) in coord_points:\n",
    "                for ac in coord_points[str(idx) + \"_\" + str(y)]:\n",
    "                    delete_old_point(idx, y, ac)\n",
    "                    update_new_point(max(idx-1, 0), y, ac)\n",
    "\n",
    "        update_new_point(max(x-1, 0), y, ac_to_shunt)\n",
    "        return\n",
    "    if prop_y_plus_empty is max(prop_x_plus_empty, prop_x_minus_empty, prop_y_plus_empty, prop_y_minus_empty):\n",
    "        for idx in range(y+1, 41)[::-1]:\n",
    "            if str(x) + \"_\" + str(idx) in coord_points:\n",
    "                for ac in coord_points[str(x) + \"_\" + str(idx)]:\n",
    "                    delete_old_point(x, idx, ac)\n",
    "                    update_new_point(x, min(idx+1, num_y_grid), ac)\n",
    "\n",
    "        update_new_point(x, min(y+1, num_y_grid), ac_to_shunt)\n",
    "        return\n",
    "    if prop_y_minus_empty is max(prop_x_plus_empty, prop_x_minus_empty, prop_y_plus_empty, prop_y_minus_empty):\n",
    "        for idx in range(y-1, -1, -1)[::-1]:\n",
    "            if str(x) + \"_\" + str(idx) in coord_points:\n",
    "                for ac in coord_points[str(x) + \"_\" + str(idx)]:\n",
    "                    delete_old_point(x, idx, ac)\n",
    "                    update_new_point(x, max(idx-1, 0), ac)\n",
    "\n",
    "        update_new_point(x, max(y-1, 0), ac_to_shunt)\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "point_position = geodf.set_index(\"AC_NO\")[['x_bin', 'y_bin']].to_dict(orient='index')\n",
    "\n",
    "x_coords_points = {}\n",
    "y_coords_points = {}\n",
    "\n",
    "for point in point_position:\n",
    "    if point_position[point]['x_bin'] not in x_coords_points:\n",
    "        x_coords_points[point_position[point]['x_bin']] = []\n",
    "    if point_position[point]['y_bin'] not in y_coords_points:\n",
    "        y_coords_points[point_position[point]['y_bin']] = []\n",
    "    x_coords_points[point_position[point]['x_bin']].append(point)\n",
    "    y_coords_points[point_position[point]['y_bin']].append(point)\n",
    "\n",
    "coord_points = {} #{str(value['x_bin']) + \"_\" + str(value[\"y_bin\"]): key for key, value in point_position.iteritems()}\n",
    "for point, value in point_position.iteritems():\n",
    "    if (str(value[\"x_bin\"]) + \"_\" + str(value[\"y_bin\"])) not in coord_points:\n",
    "        coord_points[str(value[\"x_bin\"]) + \"_\" + str(value[\"y_bin\"])] = []\n",
    "    coord_points[str(value[\"x_bin\"]) + \"_\" + str(value[\"y_bin\"])].append(point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "while not is_valid(coord_points):\n",
    "    coord_to_shunt_from = max(coord_points, key=lambda x: len(coord_points[x]))\n",
    "    ac_to_shunt = coord_points[coord_to_shunt_from][1]\n",
    "    shunt_point(coord_to_shunt_from)\n",
    "    iteration += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f8bd0e990>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ISCghI8uUlBRMmDAB169fx9ixY/HZZ5/B19cXo0aNwosvvigiJBERaZBWrlkKGVneunUL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\ni0auqiMACwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f8c455f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.crosstab(geodf[\"y_bin\"], geodf[\"x_bin\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f89b60150>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f8c468250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = pd.DataFrame([i.split('_') for i in coord_points.keys()]).astype(int)\n",
    "sns.heatmap(pd.crosstab(temp_df[1], temp_df[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5\n",
    "Convert grid to hexmap SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geodf['hex_x'] = geodf['AC_NO'].apply(lambda x: point_position[x.split(\"_\")[-1]][\"x_bin\"])\n",
    "geodf['hex_y'] = geodf['AC_NO'].apply(lambda x: point_position[x.split(\"_\")[-1]][\"y_bin\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ids = geodf.AC_NO.tolist()\n",
    "colors = [\"#eeeeee\"]*len(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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   "source": [
    "cg = Chorogrid(geodf, ids, colors, id_column=\"AC_NO\")\n",
    "cg.draw_hex()\n",
    "cg.done(save_filename=\"gujarat_ac_hex.svg\", show=True)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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