demonstration of solving a regionalization task using region

regionalization_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# enable inline plots in notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry import Polygon\n",
    "\n",
    "from region.p_regions.exact import PRegionsExact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# define input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geometry = [\n",
    "    Polygon([(x, y),\n",
    "             (x, y+1),\n",
    "             (x+1, y+1),\n",
    "             (x+1, y)]) for y in range(3) for x in range(3)\n",
    "]\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    {\"attribute\": [726.7, 623.6, 487.3,\n",
    "                   200.4, 245.0, 481.0,\n",
    "                   170.9, 225.9, 226.9]},\n",
    "    geometry=geometry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5294768518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot(column=\"attribute\", cmap='Blues')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.gca().set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running FLOW algorithm\n"
     ]
    }
   ],
   "source": [
    "cluster_obj = PRegionsExact()\n",
    "cluster_obj.fit_from_geodataframe(gdf, \"attribute\", 2)  # we build two regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# save regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gdf[\"region\"] = cluster_obj.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL25TSu2p3tppAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f529d7e9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot(column=\"region\", cmap='Blues')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.gca().set_axis_off()"
   ]
  }
 ],
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