notebooks/ONS Boundaries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ONS Boundaries\n",
    "\n",
    "Fragments associated with grabbing boundaries data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wards (December 2018) Full Extent Boundaries GB\n",
    "http://geoportal.statistics.gov.uk/datasets/wards-december-2018-full-extent-boundaries-gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-03-06 14:06:04--  https://opendata.arcgis.com/datasets/52182cdda64d4b15984f6446ca7ee7fd_1.zip?outSR=%7B%22wkid%22%3A27700%2C%22latestWkid%22%3A27700%7D\n",
      "Resolving opendata.arcgis.com (opendata.arcgis.com)... 34.197.13.10, 52.7.213.26\n",
      "Connecting to opendata.arcgis.com (opendata.arcgis.com)|34.197.13.10|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [application/octet-stream]\n",
      "Saving to: ‘wards_fullextent.zip’\n",
      "\n",
      "wards_fullextent.zi     [           <=>      ]  88.51M  39.0MB/s    in 2.3s    \n",
      "\n",
      "2019-03-06 14:06:07 (39.0 MB/s) - ‘wards_fullextent.zip’ saved [92814270]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://opendata.arcgis.com/datasets/52182cdda64d4b15984f6446ca7ee7fd_1.zip?outSR=%7B%22wkid%22%3A27700%2C%22latestWkid%22%3A27700%7D -O wards_fullextent.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  wards_fullextent.zip\n",
      "  inflating: Wards_December_2018_Full_Extent_Boundaries_GB.shp  \n",
      "  inflating: Wards_December_2018_Full_Extent_Boundaries_GB.shx  \n",
      " extracting: Wards_December_2018_Full_Extent_Boundaries_GB.cpg  \n",
      "  inflating: Wards_December_2018_Full_Extent_Boundaries_GB.prj  \n",
      "  inflating: Wards_December_2018_Full_Extent_Boundaries_GB.xml  \n",
      "  inflating: Wards_December_2018_Full_Extent_Boundaries_GB.dbf  \n"
     ]
    }
   ],
   "source": [
    "!unzip wards_fullextent.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpatiaLite version ..: 4.3.0a\tSupported Extensions:\n",
      "\t- 'VirtualShape'\t[direct Shapefile access]\n",
      "\t- 'VirtualDbf'\t\t[direct DBF access]\n",
      "\t- 'VirtualXL'\t\t[direct XLS access]\n",
      "\t- 'VirtualText'\t\t[direct CSV/TXT access]\n",
      "\t- 'VirtualNetwork'\t[Dijkstra shortest path]\n",
      "\t- 'RTree'\t\t[Spatial Index - R*Tree]\n",
      "\t- 'MbrCache'\t\t[Spatial Index - MBR cache]\n",
      "\t- 'VirtualSpatialIndex'\t[R*Tree metahandler]\n",
      "\t- 'VirtualElementary'\t[ElemGeoms metahandler]\n",
      "\t- 'VirtualXPath'\t[XML Path Language - XPath]\n",
      "\t- 'VirtualFDO'\t\t[FDO-OGR interoperability]\n",
      "\t- 'VirtualGPKG'\t[OGC GeoPackage interoperability]\n",
      "\t- 'VirtualBBox'\t\t[BoundingBox tables]\n",
      "\t- 'SpatiaLite'\t\t[Spatial SQL - OGC]\n",
      "PROJ.4 version ......: Rel. 4.9.3, 15 August 2016\n",
      "GEOS version ........: 3.6.2-CAPI-1.10.2 4d2925d6\n",
      "TARGET CPU ..........: x86_64-linux-gnu\n",
      "the SPATIAL_REF_SYS table already contains some row(s)\n",
      "========\n",
      "Loading shapefile at './Wards_December_2018_Full_Extent_Boundaries_GB' into SQLite table 'wardboundaries'\n",
      "\n",
      "BEGIN;\n",
      "CREATE TABLE \"wardboundaries\" (\n",
      "\"PK_UID\" INTEGER PRIMARY KEY AUTOINCREMENT,\n",
      "\"objectid\" INTEGER,\n",
      "\"wd18cd\" TEXT,\n",
      "\"wd18nm\" TEXT,\n",
      "\"wd18nmw\" TEXT,\n",
      "\"bng_e\" INTEGER,\n",
      "\"bng_n\" INTEGER,\n",
      "\"long\" DOUBLE,\n",
      "\"lat\" DOUBLE,\n",
      "\"st_areasha\" DOUBLE,\n",
      "\"st_lengths\" DOUBLE);\n",
      "SELECT AddGeometryColumn('wardboundaries', 'Geometry', 27700, 'MULTIPOLYGON', 'XY');\n",
      "COMMIT;\n",
      "\n",
      "Inserted 8652 rows into 'wardboundaries' from SHAPEFILE\n",
      "========\n"
     ]
    }
   ],
   "source": [
    "! spatialite adminboundaries.db \".loadshp ./Wards_December_2018_Full_Extent_Boundaries_GB wardboundaries UTF-8 27700\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpatiaLite version ..: 4.3.0a\tSupported Extensions:\n",
      "\t- 'VirtualShape'\t[direct Shapefile access]\n",
      "\t- 'VirtualDbf'\t\t[direct DBF access]\n",
      "\t- 'VirtualXL'\t\t[direct XLS access]\n",
      "\t- 'VirtualText'\t\t[direct CSV/TXT access]\n",
      "\t- 'VirtualNetwork'\t[Dijkstra shortest path]\n",
      "\t- 'RTree'\t\t[Spatial Index - R*Tree]\n",
      "\t- 'MbrCache'\t\t[Spatial Index - MBR cache]\n",
      "\t- 'VirtualSpatialIndex'\t[R*Tree metahandler]\n",
      "\t- 'VirtualElementary'\t[ElemGeoms metahandler]\n",
      "\t- 'VirtualXPath'\t[XML Path Language - XPath]\n",
      "\t- 'VirtualFDO'\t\t[FDO-OGR interoperability]\n",
      "\t- 'VirtualGPKG'\t[OGC GeoPackage interoperability]\n",
      "\t- 'VirtualBBox'\t\t[BoundingBox tables]\n",
      "\t- 'SpatiaLite'\t\t[Spatial SQL - OGC]\n",
      "PROJ.4 version ......: Rel. 4.9.3, 15 August 2016\n",
      "GEOS version ........: 3.6.2-CAPI-1.10.2 4d2925d6\n",
      "TARGET CPU ..........: x86_64-linux-gnu\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "! spatialite adminboundaries.db \"SELECT CreateSpatialIndex('wardboundaries', 'Geometry');\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatialite Read Demo\n",
    "\n",
    "Example of how to read data from Spatialite database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x7f13a34e4030>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set up the db connection and load in the spatialite extension\n",
    "import sqlite3\n",
    "\n",
    "#!whereis mod_spatialite.so\n",
    "shared_lib = '/usr/lib/x86_64-linux-gnu/mod_spatialite.so'\n",
    "dbpath = 'adminboundaries.db' \n",
    "\n",
    "# Create connection and load spatialite extension\n",
    "conn = sqlite3.connect(dbpath)\n",
    "\n",
    "# Enable SpatialLite extension\n",
    "conn.enable_load_extension(True)\n",
    "conn.load_extension(shared_lib)\n",
    "\n",
    "# Initialise spatial table support\n",
    "conn.execute('SELECT InitSpatialMetadata(1)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PK_UID</th>\n",
       "      <th>objectid</th>\n",
       "      <th>wd18cd</th>\n",
       "      <th>wd18nm</th>\n",
       "      <th>wd18nmw</th>\n",
       "      <th>bng_e</th>\n",
       "      <th>bng_n</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>st_areasha</th>\n",
       "      <th>st_lengths</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>E05000026</td>\n",
       "      <td>Abbey</td>\n",
       "      <td></td>\n",
       "      <td>544434</td>\n",
       "      <td>184378</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>E05000027</td>\n",
       "      <td>Alibon</td>\n",
       "      <td></td>\n",
       "      <td>549247</td>\n",
       "      <td>185196</td>\n",
       "      <td>0.150987</td>\n",
       "      <td>51.545921</td>\n",
       "      <td>1.364442e+06</td>\n",
       "      <td>6353.917640</td>\n",
       "      <td>b'\\x00\\x014l\\x00\\x00X\\xb1\\xbf\\xcc\\xb8\\xb8 A\\x0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>E05000028</td>\n",
       "      <td>Becontree</td>\n",
       "      <td></td>\n",
       "      <td>546863</td>\n",
       "      <td>185869</td>\n",
       "      <td>0.116912</td>\n",
       "      <td>51.552601</td>\n",
       "      <td>1.288082e+06</td>\n",
       "      <td>6341.645817</td>\n",
       "      <td>b'\\x00\\x014l\\x00\\x00\\x90~\\xfb\\x9a\\x9c\\xaa A\\xc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PK_UID  objectid     wd18cd     wd18nm wd18nmw   bng_e   bng_n      long  \\\n",
       "0       1         1  E05000026      Abbey          544434  184378  0.081291   \n",
       "1       2         2  E05000027     Alibon          549247  185196  0.150987   \n",
       "2       3         3  E05000028  Becontree          546863  185869  0.116912   \n",
       "\n",
       "         lat    st_areasha   st_lengths  \\\n",
       "0  51.539822  1.282925e+06  6244.885015   \n",
       "1  51.545921  1.364442e+06  6353.917640   \n",
       "2  51.552601  1.288082e+06  6341.645817   \n",
       "\n",
       "                                            Geometry  \n",
       "0  b'\\x00\\x014l\\x00\\x000\\x99*\\x98r\\x95 A@\\xb1\\xbf...  \n",
       "1  b'\\x00\\x014l\\x00\\x00X\\xb1\\xbf\\xcc\\xb8\\xb8 A\\x0...  \n",
       "2  b'\\x00\\x014l\\x00\\x00\\x90~\\xfb\\x9a\\x9c\\xaa A\\xc...  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can make a simple pandas query\n",
    "pd.read_sql('SELECT * FROM wardboundaries LIMIT 3', conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More usefully, we can read queries into a geopandas dataframe if we make sure we return a specified geometry column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wd18nm</th>\n",
       "      <th>Geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>(POLYGON ((544735.1960000005 184076.4979999997...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alibon</td>\n",
       "      <td>(POLYGON ((547932.3998999996 184916.5975000001...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wd18nm                                           Geometry\n",
       "0   Abbey  (POLYGON ((544735.1960000005 184076.4979999997...\n",
       "1  Alibon  (POLYGON ((547932.3998999996 184916.5975000001..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SQL must wrap the geometry in hex(st_asbinary(...))\n",
    "sql = \"SELECT wd18nm, Hex(ST_AsBinary(Geometry)) AS Geometry FROM wardboundaries LIMIT 2;\"\n",
    "gpd.GeoDataFrame.from_postgis(sql, conn, geom_col='Geometry').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "gpd.GeoDataFrame.from_postgis(sql, conn, geom_col='Geometry').plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>wd18nm</th>\n",
       "      <th>Geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>(POLYGON ((452228.4961999999 83628.70079999976...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  wd18nm                                           Geometry\n",
       "0  Arreton and Newchurch  (POLYGON ((452228.4961999999 83628.70079999976..."
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select ward by name - we need to make sure we return a geometry column from the geopandas dataframe\n",
    "sql = \"SELECT wd18nm, Hex(ST_AsBinary(Geometry)) AS Geometry FROM wardboundaries WHERE wd18nm='Arreton and Newchurch';\"\n",
    "gpd.GeoDataFrame.from_postgis(sql, conn, geom_col='Geometry')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select neighbouring wards too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Ward</th>\n",
       "      <th>Neighbour</th>\n",
       "      <th>Geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>(POLYGON ((452228.4961999999 83628.70079999976...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Brading, St Helens and Bembridge</td>\n",
       "      <td>(POLYGON ((463590.1529999999 90525.79900000058...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Central Wight</td>\n",
       "      <td>(POLYGON ((448688.1989000002 86183.19959999993...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Godshill and Wroxall</td>\n",
       "      <td>(POLYGON ((453855.7002999997 78615.09600000083...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Havenstreet, Ashey and Haylands</td>\n",
       "      <td>(POLYGON ((453283.6990999999 87799.39699999988...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Lake North</td>\n",
       "      <td>(POLYGON ((458851.6961000003 84974.19569999911...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Lake South</td>\n",
       "      <td>(POLYGON ((458190.9039000003 83607.99679999985...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Newport East</td>\n",
       "      <td>(POLYGON ((450126.4013999999 88779.29649999924...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Newport North</td>\n",
       "      <td>(POLYGON ((453283.6990999999 87799.39699999988...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Newport South</td>\n",
       "      <td>(POLYGON ((450438.1966000004 87920.80220000073...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Sandown South</td>\n",
       "      <td>(POLYGON ((460095.0017999997 85649.30240000039...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Shanklin Central</td>\n",
       "      <td>(POLYGON ((458290.2981000002 82271.9991999995,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arreton and Newchurch</td>\n",
       "      <td>Shanklin South</td>\n",
       "      <td>(POLYGON ((458705.1978000002 81625.30430000089...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Ward                         Neighbour  \\\n",
       "0   Arreton and Newchurch             Arreton and Newchurch   \n",
       "1   Arreton and Newchurch  Brading, St Helens and Bembridge   \n",
       "2   Arreton and Newchurch                     Central Wight   \n",
       "3   Arreton and Newchurch              Godshill and Wroxall   \n",
       "4   Arreton and Newchurch   Havenstreet, Ashey and Haylands   \n",
       "5   Arreton and Newchurch                        Lake North   \n",
       "6   Arreton and Newchurch                        Lake South   \n",
       "7   Arreton and Newchurch                      Newport East   \n",
       "8   Arreton and Newchurch                     Newport North   \n",
       "9   Arreton and Newchurch                     Newport South   \n",
       "10  Arreton and Newchurch                     Sandown South   \n",
       "11  Arreton and Newchurch                  Shanklin Central   \n",
       "12  Arreton and Newchurch                    Shanklin South   \n",
       "\n",
       "                                             Geometry  \n",
       "0   (POLYGON ((452228.4961999999 83628.70079999976...  \n",
       "1   (POLYGON ((463590.1529999999 90525.79900000058...  \n",
       "2   (POLYGON ((448688.1989000002 86183.19959999993...  \n",
       "3   (POLYGON ((453855.7002999997 78615.09600000083...  \n",
       "4   (POLYGON ((453283.6990999999 87799.39699999988...  \n",
       "5   (POLYGON ((458851.6961000003 84974.19569999911...  \n",
       "6   (POLYGON ((458190.9039000003 83607.99679999985...  \n",
       "7   (POLYGON ((450126.4013999999 88779.29649999924...  \n",
       "8   (POLYGON ((453283.6990999999 87799.39699999988...  \n",
       "9   (POLYGON ((450438.1966000004 87920.80220000073...  \n",
       "10  (POLYGON ((460095.0017999997 85649.30240000039...  \n",
       "11  (POLYGON ((458290.2981000002 82271.9991999995,...  \n",
       "12  (POLYGON ((458705.1978000002 81625.30430000089...  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q='''\n",
    "SELECT w1.wd18nm AS \"Ward\",\n",
    "  w2.wd18nm AS \"Neighbour\", Hex(ST_AsBinary(w2.Geometry)) AS Geometry \n",
    "FROM wardboundaries AS w1,\n",
    "  wardboundaries AS w2\n",
    "WHERE (Touches(w1.Geometry, w2.Geometry) OR w1.wd18nm=w2.wd18nm)\n",
    "AND w1.wd18nm='Arreton and Newchurch';\n",
    "'''\n",
    "\n",
    "gpd.GeoDataFrame.from_postgis(q, conn, geom_col='Geometry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": [
    "gpd.GeoDataFrame.from_postgis(q, conn, geom_col='Geometry').plot(facecolor=\"none\", edgecolor='black',);"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}