shapely-simplify.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Douglas-Peucker simplification to reduce spatial data set size\n",
    "\n",
    "This notebook uses shapely's implementation of the douglas-peucker algorithm to reduce the size of a spatial data set. The full data set consists of 1,759 lat-long coordinate points. More info: http://geoffboeing.com/2014/08/visualizing-summer-travels/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# magic command to display matplotlib plots inline within the ipython notebook webpage\n",
    "%matplotlib inline\n",
    "\n",
    "# import necessary modules\n",
    "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n",
    "from shapely.geometry import LineString\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load the point data\n",
    "df = pd.read_csv('data/summer-travel-gps-full.csv')\n",
    "coordinates = df.as_matrix(columns=['lat', 'lon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146.33453899578328 line length\n",
      "143.92611626180422 simplified line length\n",
      "1759 coordinate pairs in full data set\n",
      "178 coordinate pairs in simplified data set\n",
      "89.9 percent compressed\n"
     ]
    }
   ],
   "source": [
    "# create a shapely line from the point data\n",
    "line = LineString(coordinates)\n",
    "\n",
    "# all points in the simplified object will be within the tolerance distance of the original geometry\n",
    "tolerance = 0.015\n",
    "\n",
    "# if preserve topology is set to False the much quicker Douglas-Peucker algorithm is used\n",
    "# we don't need to preserve topology bc we just need a set of points, not the relationship between them\n",
    "simplified_line = line.simplify(tolerance, preserve_topology=False)\n",
    "\n",
    "print(line.length, 'line length')\n",
    "print(simplified_line.length, 'simplified line length')\n",
    "print(len(line.coords), 'coordinate pairs in full data set')\n",
    "print(len(simplified_line.coords), 'coordinate pairs in simplified data set')\n",
    "print(round(((1 - float(len(simplified_line.coords)) / float(len(line.coords))) * 100), 1), 'percent compressed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>41.044556</td>\n",
       "      <td>28.983285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>41.008992</td>\n",
       "      <td>28.968268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>41.043487</td>\n",
       "      <td>28.985488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>40.977637</td>\n",
       "      <td>28.823879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>48.357110</td>\n",
       "      <td>11.791346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lat        lon\n",
       "173  41.044556  28.983285\n",
       "174  41.008992  28.968268\n",
       "175  41.043487  28.985488\n",
       "176  40.977637  28.823879\n",
       "177  48.357110  11.791346"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the simplified set of coordinates as a new dataframe\n",
    "lon = pd.Series(pd.Series(simplified_line.coords.xy)[1])\n",
    "lat = pd.Series(pd.Series(simplified_line.coords.xy)[0])\n",
    "si = pd.DataFrame({'lon':lon, 'lat':lat})\n",
    "si.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process took 10.07 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "\n",
    "# df_label column will contain the label of the matching row from the original full data set\n",
    "si['df_label'] = None\n",
    "\n",
    "# for each coordinate pair in the simplified set\n",
    "for si_label, si_row in si.iterrows():    \n",
    "    si_coords = (si_row['lat'], si_row['lon'])\n",
    "    \n",
    "    # for each coordinate pair in the original full data set\n",
    "    for df_label, df_row in df.iterrows():\n",
    "        \n",
    "        # compare tuples of coordinates, if the points match, save this row's label as the matching one\n",
    "        if si_coords == (df_row['lat'], df_row['lon']):\n",
    "            si.loc[si_label, 'df_label'] = df_label\n",
    "            break\n",
    "            \n",
    "print('process took %s seconds' % round(time() - start_time, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>df_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>41.044556</td>\n",
       "      <td>28.983285</td>\n",
       "      <td>1730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>41.008992</td>\n",
       "      <td>28.968268</td>\n",
       "      <td>1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>41.043487</td>\n",
       "      <td>28.985488</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>40.977637</td>\n",
       "      <td>28.823879</td>\n",
       "      <td>1751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>48.357110</td>\n",
       "      <td>11.791346</td>\n",
       "      <td>1758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lat        lon df_label\n",
       "173  41.044556  28.983285     1730\n",
       "174  41.008992  28.968268     1739\n",
       "175  41.043487  28.985488     1745\n",
       "176  40.977637  28.823879     1751\n",
       "177  48.357110  11.791346     1758"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>41.044556</td>\n",
       "      <td>28.983285</td>\n",
       "      <td>07/08/2014 16:44</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>41.008992</td>\n",
       "      <td>28.968268</td>\n",
       "      <td>07/08/2014 20:03</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>41.043487</td>\n",
       "      <td>28.985488</td>\n",
       "      <td>07/08/2014 22:18</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>40.977637</td>\n",
       "      <td>28.823879</td>\n",
       "      <td>07/09/2014 09:03</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>48.357110</td>\n",
       "      <td>11.791346</td>\n",
       "      <td>07/09/2014 13:20</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat        lon              date      city  country\n",
       "1730  41.044556  28.983285  07/08/2014 16:44  Istanbul   Turkey\n",
       "1739  41.008992  28.968268  07/08/2014 20:03  Istanbul   Turkey\n",
       "1745  41.043487  28.985488  07/08/2014 22:18  Istanbul   Turkey\n",
       "1751  40.977637  28.823879  07/09/2014 09:03  Istanbul   Turkey\n",
       "1758  48.357110  11.791346  07/09/2014 13:20    Munich  Germany"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the rows from the original full data set whose labels appear in the df_label column of the simplified data set\n",
    "rs = df.loc[si['df_label'].dropna().values]\n",
    "\n",
    "#rs.to_csv('data/summer-travel-gps-simplified.csv', index=False)\n",
    "rs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAGJCAYAAAApPV86AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22630599da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the final simplified set of coordinate points vs the original full set\n",
    "plt.figure(figsize=(10, 6), dpi=100)\n",
    "rs_scatter = plt.scatter(rs['lon'], rs['lat'], c='m', alpha=0.3, s=150)\n",
    "df_scatter = plt.scatter(df['lon'], df['lat'], c='k', alpha=0.4, s=10)\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Simplified set of coordinate points vs original full set')\n",
    "plt.legend((rs_scatter, df_scatter), ('Simplified', 'Original'), loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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
}