12_geospatial_09_set_operations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set operations of Geo Objects\n",
    "\n",
    "Tutorials from: https://geopandas.org/set_operations.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from shapely.geometry import Polygon\n",
    "import geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]),\n",
    "                              Polygon([(2,2), (4,2), (4,4), (2,4)])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]),\n",
    "                              Polygon([(3,3), (5,3), (5,5), (3,5)])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "polys1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "polys2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to geopandas objects.\n",
    "df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1':[1,2]})\n",
    "df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2':[1,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "ax = df1.plot(color='red');\n",
    "df2.plot(ax=ax, color='green', alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. union.\n",
    "res_union = geopandas.overlay(df1, df2, how='union')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((1.00000 2.00000, 2.00000 2.00000, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POLYGON ((3.00000 4.00000, 4.00000 4.00000, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((2.00000 3.00000, 2.00000 4.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MULTIPOLYGON (((1.00000 2.00000, 1.00000 3.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POLYGON ((3.00000 4.00000, 3.00000 5.00000, 5....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df1  df2                                           geometry\n",
       "0  1.0  1.0  POLYGON ((1.00000 2.00000, 2.00000 2.00000, 2....\n",
       "1  2.0  1.0  POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3....\n",
       "2  2.0  2.0  POLYGON ((3.00000 4.00000, 4.00000 4.00000, 4....\n",
       "3  1.0  NaN  POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....\n",
       "4  2.0  NaN  MULTIPOLYGON (((2.00000 3.00000, 2.00000 4.000...\n",
       "5  NaN  1.0  MULTIPOLYGON (((1.00000 2.00000, 1.00000 3.000...\n",
       "6  NaN  2.0  POLYGON ((3.00000 4.00000, 3.00000 5.00000, 5...."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sw: Union might be the thing I need in combining road and land use shapefiles.\n",
    "ax = res_union.plot(alpha=0.5, cmap='tab10')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. intersection\n",
    "res_intersection = geopandas.overlay(df1, df2, how='intersection')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((1.00000 2.00000, 2.00000 2.00000, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((3.00000 4.00000, 4.00000 4.00000, 4....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df1  df2                                           geometry\n",
       "0    1    1  POLYGON ((1.00000 2.00000, 2.00000 2.00000, 2....\n",
       "1    2    1  POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3....\n",
       "2    2    2  POLYGON ((3.00000 4.00000, 4.00000 4.00000, 4...."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = res_intersection.plot(cmap='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. symmetric difference.\n",
    "res_symdiff = geopandas.overlay(df1, df2, how='symmetric_difference')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((2.00000 3.00000, 2.00000 4.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MULTIPOLYGON (((1.00000 2.00000, 1.00000 3.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POLYGON ((3.00000 4.00000, 3.00000 5.00000, 5....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df1  df2                                           geometry\n",
       "0  1.0  NaN  POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....\n",
       "1  2.0  NaN  MULTIPOLYGON (((2.00000 3.00000, 2.00000 4.000...\n",
       "2  NaN  1.0  MULTIPOLYGON (((1.00000 2.00000, 1.00000 3.000...\n",
       "3  NaN  2.0  POLYGON ((3.00000 4.00000, 3.00000 5.00000, 5...."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_symdiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = res_symdiff.plot(cmap='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. asymmetric difference.\n",
    "res_difference = geopandas.overlay(df1, df2, how='difference')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>df1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTIPOLYGON (((2.00000 3.00000, 2.00000 4.000...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  df1\n",
       "0  POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....    1\n",
       "1  MULTIPOLYGON (((2.00000 3.00000, 2.00000 4.000...    2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = res_difference.plot(cmap='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
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 },
 "nbformat": 4,
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}