geospatial_vector_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![datiaperti logo](images/datiaperti_small.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geospatial Data Analysis - Vector Data\n",
    "This notebook is about GDAL/OGR, a library for vector geospatial data formats used to traslate one format to another among those supported. GDAL is written in C/C++ with bindings in other languages such as Python and Java. GDAL also provides a set of programs that can be used from the command line. Install the gdal package before using this notebook  \n",
    "\n",
    "$ conda install -c conda-forge gdal  \n",
    "\n",
    "$ conda install -c conda-forge pyshp  \n",
    "\n",
    "Use [GDAL/OGR Cookbook](https://pcjericks.github.io/py-gdalogr-cookbook/index.html) for references."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy version: 1.23.1\n",
      "GDAL version: 3.5.1\n",
      "GeoPandas version: 0.11.1\n",
      "shapefile version: 2.3.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import struct\n",
    "import zipfile\n",
    "from shapely import wkt, geometry\n",
    "import fiona\n",
    "import geopandas as gpd\n",
    "import pyproj\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import utm\n",
    "import shapefile as shp\n",
    "from osgeo import gdal_array\n",
    "from osgeo import gdal, ogr, osr\n",
    "from PIL import Image, ImageDraw\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "print(\"NumPy version: %s\"%np.__version__)\n",
    "print(\"GDAL version: %s\"%gdal.__version__)\n",
    "print(\"GeoPandas version: %s\"%gpd.__version__)\n",
    "print(\"shapefile version: %s\"%shp.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compressed files\n",
    "### Zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACSST1Y2021.S1901-Data.csv\n",
      "ACSST1Y2021.S1901-Column-Metadata.csv\n",
      "ACSST1Y2021.S1901-Table-Notes.txt\n"
     ]
    }
   ],
   "source": [
    "zip = open(\"data/vector/US/us_census.zip\", \"rb\")\n",
    "zipShape = zipfile.ZipFile(zip)\n",
    "for fileName in zipShape.namelist():\n",
    "    print(fileName)\n",
    "    #out = open('data/vector/US/' + fileName, \"wb\")\n",
    "    #out.write(zipShape.read(fileName))\n",
    "    #out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector Data\n",
    "### Esri shape file\n",
    "The Esri shapefile format has a fixed location and data type in the file header from byte 36 to byte 37 for the minimum x, minimum y, maximum x, and maximum y bounding box values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(318573.31825141667,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.relpath(\"data/vector/stbnd/stbnd.shp\")\n",
    "f = open(path,\"rb\")\n",
    "f.seek(36) \n",
    "struct.unpack(\"<d\", f.read(8)) # min x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035233.9673716214,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struct.unpack(\"<d\", f.read(8)) # min y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(651004.9840024861,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struct.unpack(\"<d\", f.read(8)) # max x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1577852.5200537047,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struct.unpack(\"<d\", f.read(8)) # max y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GeoJSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gc = { \"type\": \"GeometryCollection\",\n",
    "       \"geometries\": [\n",
    "          { \"type\": \"Point\",\n",
    "            \"coordinates\": [-89.33, 30.0]\n",
    "          },\n",
    "          { \"type\": \"LineString\",\n",
    "            \"coordinates\": [ [-89.33, 30.30], [-89.36, 30.28] ]\n",
    "          }\n",
    "       ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'GeometryCollection',\n",
       " 'geometries': [{'type': 'Point', 'coordinates': [-89.33, 30.0]},\n",
       "  {'type': 'LineString', 'coordinates': [[-89.33, 30.3], [-89.36, 30.28]]}]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geospatial Python Toolbox\n",
    "### Shapely\n",
    "It is a generic geometry library, a Python port of the [Java Topology Suite](https://github.com/locationtech/jts) that provides support fot topology operators for geometric objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wktSquare = \"POLYGON((0 0,4 0,4 4,0 4,0 0))\" # a square on a 2D reference system\n",
    "square = wkt.loads(wktSquare)\n",
    "square.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.16 -0.16 4.32 4.32\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,4.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0864\" opacity=\"0.6\" d=\"M 0.0,0.0 L 4.0,0.0 L 4.0,4.0 L 0.0,4.0 L 0.0,0.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x26ed2022340>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check whether a point lies within the square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wktPoint = 'POINT (2 2)'\n",
    "point = wkt.loads(wktPoint)\n",
    "point.within(square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"1.96 1.96 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,5.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 2.0,2.0 L 3.0,2.0 L 2.0,3.0 L 2.0,2.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x26ecf6748b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wktTriangle = 'POLYGON((2 2, 3 2, 2 3, 2 2))'\n",
    "triangle = wkt.loads(wktTriangle)\n",
    "triangle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check whether the triangle lies within the square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triangle.within(square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fiona\n",
    "A simple Python API around the OGR library for data access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp_file_name = 'data/vector/US/cities_albers.shp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ESRI Shapefile'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = fiona.open(shp_file_name)\n",
    "f.driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-6282848.754867505,\n",
       " -62785.72259264253,\n",
       " 3393444.0014464697,\n",
       " 6186491.5102767795)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.bounds # pairs of geo coordinates that define a bounding box) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35432"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(f)) # number of polygongs in the shape file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'Feature', 'id': '44', 'properties': OrderedDict([('CITIESX020', 45), ('FEATURE', 'Populated Place'), ('NAME', 'Anvik'), ('POP_RANGE', '0 - 9,999'), ('POP_2000', 104), ('FIPS55', '03880'), ('COUNTY', 'Yukon-Koyukuk Census Area'), ('FIPS', '02290'), ('STATE', 'AK'), ('STATE_FIPS', '02'), ('DISPLAY', 0)]), 'geometry': {'type': 'Point', 'coordinates': (-3510436.406032449, 5549048.212929771)}}\n"
     ]
    }
   ],
   "source": [
    "print(f[44])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GeoPandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HQEnSpj3HVOhw+qHcqLofeWrKPQAkFwGIRJppYf5wrlD8jcmNOpNlgJSgCxSYhskyQDrQAkRq5Ce+lMIuMkA6EIBIhfzEl+Kdn2+66doFodcDIHp0gSIVik18KWTnodPMBgVSgABEKsw08aWQIF2lAOKNAAQKYCIMkHwEIFKhycq7nokwQPIRgEiFTyxfVNb1TIQBko8ARCps7VqquZc0l75w3OPPnAmxGgCNgABEagydHyl90TjGAIHkIwCRGuWM612a4a8GkHT8LUdqLP53wQOw3GUTAOKHAERqHHruXFnXsxgeSDYCEKkx4l7W9SyGB5KNAERqNFt5iwELnSoPIDkIQKTG7csXlv097X/2mK7auFedPQfoEgUSJnAAmlmzmfWb2d+HWRAQlq1dS7V+RXkL4s8N5eQaaw1u2nOcEAQSpJwW4KclPR1WIUA9bO1aqtmzKuv4GM6NMC4IJEigdwIze6ek1ZL+OtxygPCdv1D5EgcWyAPJEfSj8A5JfywFPlINaFjVbHTNJtlAcpQMQDP7bUmvuPuREtfdaWZ9ZtZ35gz7KKJxda9aUtH3ZTPNFX8vgMYTpAXYKenDZnZK0t9KWmlmO6df5O4PuHuHu3csWMBO+mhcXe2tgTfGbm3Jysb/vW3NUnW1t4ZbHIC6mVXqAnffJGmTJJnZjZI+5+7rwy0LCNd9H12q7m8+pdxI8cXx61cs0taupXWsCkA9lQxAIInyLbnt+07q5cFhXZpp0hsXRjXqYwvmb1++kPADEs68zO2hgujo6PC+vr6a3xcAgHKY2RF37yj0HDvBAABSiQAEAKQSAQgASCUCEACQSgQgACCVCEAAQCoRgACAVCIAAQCpRAACAFKJAAQApBIBCABIJQIQAJBKBCAAIJUIQABAKhGAAIBUIgABAKlEAAIAUokABACkEgEIAEglAhAAkEoEIAAglQhAAEAqEYAAgFQiAAEAqUQAAgBSaVbUBQBxt+7BJ3Tw2bNTHnv7ZZfo8N03R1QRgCAIQCCg3v4Bbd93Ui8PDuvKlqy6Vy3R5x4+qgt+8bU/e/W8rr37u3rmvg/Vv1AAgZh7gb+9Vero6PC+vr6a3xeISm//gLofeUq50fL+vpikdSsWqeNd8y8Kz6721nCKBTDBzI64e0fB5whAoLRf+tPvaTg3Gsq9583JaMut1xGIQAhmCkC6QIESevsHQgs/STo3lNOG3Ue1YfdRZTNN+rfcKK1EoA5oAQIltN37mAaHc5H87HlzMhocyhGIQIWqagGa2aWSfiBp9vj133T3LbUtEWhcUYWfNNY6lKSBwWHdtfuoJBGCmFBoBvL6FYu0tWtpRBXFS5Au0DckrXT318wsI+kfzOx77n4o5NoATDIqTXSV5ifXSNJDh1/UiLuazXT78oW8+aXE8vv262evnr/o8Z2HTuv5M69p1x03RFBVvJQMQB/rI31t/MvM+D+17zcFEJhr7I1ushF37Tx0Wg8/+aLmzp5F12mCrXvwiYLhl3fw2bPq7R/g/3sJgcYAzaxZ0hFJ75H0VXf/kwLX3CnpTklatGjRB1544YUalwpEY/HGvVGXUJVMkynTbBqaNpEnm2nStjXLIn+TLLS+MuqaGllv/4A2jHeHl3KqZ3W4xcRAzZZBmFmLpG9L+pS7/6jYdUyCQZLEPQDDYnqzK6glm9E9Hy5/KUdv/4A27Tmu4dzIxGPZTLO2rVlKCBbR2XNAA4PDga5lPLCGyyDcfdDMvi/pFklFAxBA8k3+6Dw4/OZSjmoN50a0fd9JArCIoOEnjXWTpz0AZxJkFugCSbnx8MtK+k1JXwq9MgCpNTA4PKXl3Xn1fCZ1jGs200gIy9fSKMhpEO+Q9LiZHZP0pKT97v734ZYFNIbNvcejLgEam9Sx7sEnoi6jIZQbfr39AyFVEn8lA9Ddj7l7u7svc/f3u/uf1aMwoBFMn2mJ6Bx89qza7n3sojf03v4BdfYc0FUb96qz50Ci3/Ar+bNt2H2UDw9FsBMMMAMmwDSmOZkmfXHNMvW9cPaiDymZJtP2j1+fyDHEcibATJfWI7rYDBuoQDnTzdFYspkmzZ87O3FLK6r9QJbGsVQ2wwYq0P3I0ahLQIWGc6MTLaWkbCNXi67d6dumpV2QSTBAKoV4AATqbFTSpj3Hoi6jYpt7j9MbEQJagEABTBpInuHc6JRdZ1rmZOQu/etw420Z19s/oM/vOXbR7j2oLcYAgQKY/JJuUeyg0ts/oM8+fFQjIS/xS9v2aIwBAmVI8jR6BJOfWRpmCPb2D+hPvnVMb1yoXytv/fgJIhjDGCAwDWMtkMJdA7ruwSe0YffRuoZf59Xz2RZtGgIQmGT5ffujLgENJIzegEKH2IZt/YpFqVv+EAQBCEwy0xlrSJ/PPHy0piHY2z9Q9/BrsnC7cuOMAATGse8npnMf6xKvVQhGsbb0/tva6v4z44IABMax7yeKqUVw9fYP1H1t6Y61bQ2ztKMREYAAUEJutPrxwHouxF+/YpFO9awm/EogAAGx9AGl3fOdE1V9/3Cdmn871rYx5hcQAQhI2r7vZNQloMENDueiLiEQWn3BsRAekCo+Ygbpsrn3eKDWVW//gO79uxM6N1T/0LzuC4/qvo8uJQgDIAABSc1mZZ+0jfTZeei0nj/zWtE1dVdt3Kuof4tePz8ysZkDITgzukABifBDYAefPVtwzLgRwm8ydjQqjQAEgDIVmhDTSOGHYAhApN57NnHyA8oTlwkxzG6eGQGI1LvAR3dUIA7hwuzmmRGASLU4vImhMU0Ol5vv/350hczgZWY3z4hZoEg1PiGjUvlwec+mvQ3bi3BlSzbqEhoaAYhU4xMyKuWSFm9s3PHjTJOpe9WSqMtoaHSBItX4hIwkaslmtP3j17MOsARagEi17lVLWC+F2Js3J6P+L3ww6jJihxYgUo1PyEiCLbdeF3UJsdSwLcDe/gFt33dSLw8O68qWrLpXLeHNCjXHLFDEHWf+Vc48hC2gOjo6vK+vr+Lv7+0f0KY9xzWcGyn4/Nsvu0SH77654vsDeW33PhabRc3AdKd6VkddQsMzsyPu3lHouYbsAt2+72TR8JOkn716Xsvv21/HipBUhB/iivCrXskANLOFZva4mT1tZifM7NNhFxVkavrPXj0fdhkA0HDyp72jekFagBckfdbdf0nSCkl/aGbvC7MopqajXubNyURdAlAWTnuvnZIB6O7/5O7/OP7fr0p6WlKoI67dq5Yom2kO80cAksZmzzVZ1FUAiEJZs0DNbLGkdkmHQ6lmXH5GE+uzEDZ+14D0CjwJxszeIulbkja4+y8KPH+nmfWZWd+ZM2eqLizItN7FG/dq8ca92tx7vOqfh/RiCjniovPq+VGXkCiBAtDMMhoLv13uvqfQNe7+gLt3uHvHggULalJc0IHenYdOE4IAEm/XHTdEXUKiBJkFapK+Julpd78//JKmOtWzOlAQ7jp0ug7VIKnmXsKYMxobv6O1F6QF2Cnp9yStNLOj4/98KOS6ytagp5EgJu77KDPr0Nj4Ha29ILNA/8Hdzd2XuXvb+D/frUdxQL0wDohGx+9o7TXkTjCVuqqBz+YCADSW2ATgpc2lF2u5pHUPPhF+MQCA2ItNAD5z34cUZL3ywWfPssM/gERpZXesUMQmACXp+YDLIjbsPkoIAkiM7lVLoi4hkWIVgFLwT0LdjxwNtxAAqBMmwIQjdgHYvWpJoK7Q3GjopSBh6GZCI5qTid3bdGzE7pXtam/VuhWLoi4DCcQm7GhEQ3yaD03sAlDiOBCEo6u9VdvW8LuFxvOeTSzxCkMsA3DZlkejLgEJxVgLGtEFZ4lXGGIZgL94YyTQdfzCoBLsuYhGxBKv2otdAJYTagefPRtiJUgq9lxEo9q051jUJSRK7AKQUEPY6AZFoxpmQkxNxS4AAQCohUQHIKcnA0iSpiCLoBFY7AKwnFDj9GRUijcaNKJPLGcNdC3FLgB33XGD3jq79Cy9INcAxdx/W1vUJQBTdF49nzXQNRa7AJSkY/feMmNL8K2zm3Xs3lvqWBGSpqu9VTvWtoldqNAIdqxto0crBObuNb9pR0eH9/X11fy+QFTes2mvLtT+rwpQ0uxZTTq59beiLiO2zOyIu3cUeo7Pt0AAP90W7CguoNa+9LFlUZeQWAQgENCpgOdRArWyY20b61JDNCvqAoA4mTcno3NDuajLQIKZgh/+jerQAgTKsOXW65RpZo0EwvOVtW1Rl5AatACBMuS7o7bvO6mBweGIq0ES0eVZP7QAgTJ1tbfq4MaV2sEnddQY48z1RQACFeKTOmplx9o2wi8CdIECQESY8BItWoBAFdavYG9GVI7wixYtQKAK+b0ZHzr8okZC2FUJycWHp+ixFRoQot7+AXV/8ynlRghHTMWYX32wFRoQka72Vm3/nevV2pKVSWptyU5MeODIpfTirNLGQBcoELKu9taCM0ZHaRSm0jVXzOVkhwZRsgVoZl83s1fM7Ef1KAhIi9aWbNQloM7Wr1ik/XfdGHUZGBekC/R/SuJwPaDGulctUTbDwc1p0WTiQNsGU7IL1N1/YGaL61ALkCpsq5Yu99/WFnUJmKZmk2DM7E4z6zOzvjNnztTqtkCiTd5WjTkxycbOQY2nZgHo7g+4e4e7dyxYsKBWtwVSoau9VV9Z26aWbCbqUoDUYBkE0CC62lt1dMsHdapntXasbdO8OcHDMJtp1voViyaWW2T4m91wevsHoi4B07AMAmhA05dO9PYP6PN7jmkoNzrxmElyjc0m7V615KIutuX37dfPXj1fp4pRyqY9x+gGbTAlA9DMHpJ0o6TLzewlSVvc/WthFwbgTcXWEs7k8N03a9mWR/WLN0ZCqgrlGJ704QWNoWRHibvf7u7vcPeMu7+T8APi49i9t7DnZAO5+f7vR10CJmGkAEi4rV1LdapnNUHYAH7yyuuMBTYQAhBICRZhN4YNu49GXQLGEYAAUGe0AhsDAQikSDlLKxAeWoGNgQAEUmTLrddFXULDMY1tUn2qZ7VO9azWrDptybPuwSfq84NQFOsAgRTpam/VZ3YfVZpPYmo207PbPlT0+Z9uW63Nvce189Dpgs+vX7FIh5/7uX7yyutV1XHw2bNVfT+qRwsQSJl1KZ8NOuKl439r11LtWNtW8CDjrV1Ltf+uG2tyqC3LIqJFCxBIma1dS/X8mdcCtUDyu8x85uGjCpAbsdBswfo4S20+sOuOG7R4496qaqm2FYnqEIBACu2644YZu/mksf1FJ2+xFtXEDdNYq7XYMo5Sf47pbl++sEaV1ca1d39Xz9xXvEsW4aELFEipfDff5BMomsYbR60tWW1bs3Qi/LraWyeeq6f1Kxbp+fFux2IK/Tlmul+jrYf8txHX5t7jUZeRSuYh9Gt0dHR4X19fze8LIDrltrSqtWNtW8NvHl1tF+hkp3pW1+xeeJOZHXH3jkLP0QIEEMjWrqWaPSv8t4xrrpirUz2rGz78JHGIccwRgAAC+9LHlinTXPu3/c6r50+sw9t/1401v39YnqfVFmtMggEQWL5Vtn3fSQ0MDld9v2aTvnxb43d1zuTtl13CuYsxRQsQQFm62lt1cOPKmtzr2W3x6OqcyeG7b466BFSIAAQQiSQdz8QElngiAAFUpJplEY24HKFaO9a2RV0CykQAAqjIJ5ZX1oLrvHp+4sJPGusavuaKuVGXgTIQgAAqUumyiIPPnk3seXj777pRb7/skqjLQEAEIICKfeljyyr6vrsSfB7e4btvrslG2QgfAQigYpXO4BytcR2NZtcdNzAmGAMEIICq0OVX2CN9wbeNmzen9D6mqD0CEEBVDt99MyFYQDkH3m659boQK0ExBCCAqh2++2ad6lkdeBYksyXftH7FothvBhBXBCCAmtl/14061bN6xkXu11wxN1b7fYYpiesh44S9QAHU3Naupal/Y++8en7RbtBspknb1iyj5RcxAhAAQrDrjhu07sEnpoRg59XzteuOGyKsCpMRgAAQEsKusTEGCABIJVqAAIDIbe49rocOv6gRdzWb6fblC0MfRw4UgGZ2i6S/lNQs6a/dvSfUqgAAibR4496S14y4a+ehsY0EwgzBkgFoZs2SvirpZkkvSXrSzL7j7j8OrSoAQCL09g9oQ4V7v+48dDrUAAwyBvirkn7q7s+5+3lJfyvpI6FVBABIhGrCL2/dg0/UppgCggRgq6QXJ3390vhjAAAUVW34SeVtKVeuIAFY6Nxnv+giszvNrM/M+s6cOVN9ZQAAhChIAL4kaeGkr98p6eXpF7n7A+7e4e4dCxYsqFV9AACEIkgAPinpGjO7yswukfS7kr4TblkAACjUw4VLzgJ19wtm9keS9mlsGcTX3f1EaBUBABLBVGC8rAxhbx0XaB2gu39X0ndDqwIAkDjP96wOtO5Pkt46u1nH7r0l5IqmYicYAEBoTs0QglEfjUUAAgBCdapnddQlFMRm2ACAVCIAAQCpRAACAFKJAAQApBIBCABIJQIQAJBKBCAAIJUIQABAKpl7NTu1Fbmp2RlJL9T8xqVdLulfIvi5ScBrVzleu8rx2lWO1y6Yd7l7wSOKQgnAqJhZn7t3RF1HHPHaVY7XrnK8dpXjtaseXaAAgFQiAAEAqZS0AHwg6gJijNeucrx2leO1qxyvXZUSNQYIAEBQSWsBAgAQSCID0Mw+ZWYnzeyEmf151PXEjZl9zszczC6Pupa4MLPtZvaMmR0zs2+bWUvUNTU6M7tl/O/pT81sY9T1xIWZLTSzx83s6fH3uE9HXVNcJS4AzewmSR+RtMzdr5P0FxGXFCtmtlDSzZJOR11LzOyX9H53Xybp/0naFHE9Dc3MmiV9VdJvSXqfpNvN7H3RVhUbFyR91t1/SdIKSX/Ia1eZxAWgpD+Q1OPub0iSu78ScT1x8xVJfyyJweEyuPtj7n5h/MtDkt4ZZT0x8KuSfuruz7n7eUl/q7EPrijB3f/J3f9x/L9flfS0pNZoq4qnJAbgeyX9BzM7bGb/x8x+JeqC4sLMPixpwN2firqWmPtPkr4XdRENrlXSi5O+fkm8iZfNzBZLapd0OOJSYmlW1AVUwsz+t6R/X+CpuzX2Z5qnsa6BX5H0sJm925nuKqnka/d5SR+sb0XxMdNr5+7/a/yauzXWRbWrnrXFkBV4jL+jZTCzt0j6lqQN7v6LqOuJo1gGoLv/ZrHnzOwPJO0ZD7z/a2ajGtsz70y96mtkxV47M1sq6SpJT5mZNNaF949m9qvu/s91LLFhzfR7J0lm9vuSflvSb/CBq6SXJC2c9PU7Jb0cUS2xY2YZjYXfLnffE3U9cZXELtBeSSslyczeK+kSsWFsSe5+3N2vcPfF7r5YY29Qv0z4BWNmt0j6E0kfdvehqOuJgSclXWNmV5nZJZJ+V9J3Iq4pFmzsE+rXJD3t7vdHXU+cJTEAvy7p3Wb2I40NrP8+n8ZRB/9F0mWS9pvZUTP7b1EX1MjGJwz9kaR9GpvE8bC7n4i2qtjolPR7klaO/64dNbMPRV1UHLETDAAglZLYAgQAoCQCEACQSgQgACCVCEAAQCoRgACAhmRmXzezV8Zn9Qe5/jYz+/H4JuHfKHk9s0ABAI3IzH5d0muS/sbd31/i2mskPSxppbufM7MrSu0FTQsQANCQ3P0Hks5OfszMrjazR83siJn90MyuHX/qDklfdfdz499b8iAEAhAAECcPSPqUu39A0uck/dX44++V9F4zO2hmh8Z3Z5pRLPcCBQCkz/gG4L8m6ZHxPYslafb4v2dJukbSjRrbW/aHZvZ+dx8sdj8CEAAQF02SBt29rcBzL0k65O45Sc+b2UmNBeKTM90MAICGN37s0/Nm9nFpbGNwM7t+/OleSTeNP365xrpEn5vpfgQgAKAhmdlDkp6QtMTMXjKzT0paJ+mTZvaUpBOSPjJ++T5JPzezH0t6XFK3u/98xvuzDAIAkEa0AAEAqUQAAgBSiQAEAKQSAQgASCUCEACQSgQgACCVCEAAQCoRgACAVPr/SbPMOBZFMWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf = gpd.GeoDataFrame\n",
    "census = gdf.from_file('data/vector/US/cities_albers.shp')\n",
    "fig, ax = plt.subplots(1, figsize=(10, 5))\n",
    "census.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-6282848.754867505, -62785.72259264253, 3393444.0014464697, 6186491.5102767795]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = shp.Reader(shp_file_name)\n",
    "r.bbox # pairs of coordinates of two points used to define a bounding box (min_xm, min_y, max_x, max_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35432"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.numRecords # number of polygons in the shape file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.shapeType # 5 means polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CITIESX020',\n",
       " 'FEATURE',\n",
       " 'NAME',\n",
       " 'POP_RANGE',\n",
       " 'POP_2000',\n",
       " 'FIPS55',\n",
       " 'COUNTY',\n",
       " 'FIPS',\n",
       " 'STATE',\n",
       " 'STATE_FIPS',\n",
       " 'DISPLAY']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[item[0] for item in r.fields[1:]] # record fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Record #2: [3, 'Populated Place', 'Point Lay', 'Undetermined', -99999, '61700', 'North Slope Borough', '02185', 'AK', '02', 1]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.record(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GDAL library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the OGR module to work with vector data. It provides a unique interface to read and write data in different formats such as Esri shape file, GeoJSON and many other. A dataset in OGR contains one layer that can contain one or more features. A layer also contains information such as the coordinate system that are used for the coordinates of the features. A feature is a geographic object: a point, a line or a polygon. A feature has attributes, one of this is its geometry. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bombo 75000 32.533299524864844 0.5832991056146284\n",
      "Fort Portal 42670 30.27500161597942 0.671004121125236\n",
      "Potenza 69060 15.798996495640267 40.642002130098206\n",
      "Campobasso 50762 14.655996558921856 41.56299911864397\n",
      "Aosta 34062 7.315002595706176 45.7370010670723\n",
      "Mariehamn 10682 19.949004471869102 60.09699618489543\n",
      "Ramallah 24599 35.206209378189556 31.90294475142406\n",
      "Vatican City 832 12.453386544971766 41.903282179960115\n",
      "Poitier 85960 0.3332765285345545 46.58329225573658\n",
      "Clermont-Ferrand 233050 3.080008095928406 45.779982115759424\n",
      "1249\n"
     ]
    }
   ],
   "source": [
    "fn = 'data/vector/global/ne_50m_populated_places.shp'\n",
    "ds = ogr.Open(fn, 0) # open in read-only mode\n",
    "if ds is None:\n",
    "    sys.exit('Could not open {0}.'.format(fn))\n",
    "layer = ds.GetLayer(0)\n",
    "num_features = layer.GetFeatureCount()\n",
    "i = 0\n",
    "for feature in layer:  # a feature is a geographic object: a point, a line or a polygon.\n",
    "    pt = feature.geometry()\n",
    "    x = pt.GetX()\n",
    "    y = pt.GetY()\n",
    "    name = feature.GetField('NAME')\n",
    "    pop = feature.GetField('POP_MAX')\n",
    "    print(name, pop, x, y)\n",
    "    i += 1\n",
    "    if i == 10:\n",
    "        break\n",
    "del ds\n",
    "print(num_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the west and east longitude and the south and nord latitude of the bounding box that contains all the features in the a layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-122.66148376464844, -117.4260482788086, 45.638729095458984, 48.759552001953125)\n"
     ]
    }
   ],
   "source": [
    "ds = ogr.Open('data/vector/Washington/large_cities.geojson')\n",
    "layer = ds.GetLayer(0)\n",
    "extent = layer.GetExtent()\n",
    "print(extent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can read the layer's coordinate system that is used by the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEOGCS[\"NAD83\",\n",
      "    DATUM[\"North_American_Datum_1983\",\n",
      "        SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
      "            AUTHORITY[\"EPSG\",\"7019\"]],\n",
      "        AUTHORITY[\"EPSG\",\"6269\"]],\n",
      "    PRIMEM[\"Greenwich\",0,\n",
      "        AUTHORITY[\"EPSG\",\"8901\"]],\n",
      "    UNIT[\"degree\",0.0174532925199433,\n",
      "        AUTHORITY[\"EPSG\",\"9122\"]],\n",
      "    AXIS[\"Latitude\",NORTH],\n",
      "    AXIS[\"Longitude\",EAST],\n",
      "    AUTHORITY[\"EPSG\",\"4269\"]]\n"
     ]
    }
   ],
   "source": [
    "print(layer.GetSpatialRef())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We print the attributes (fields) used for the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CITIESX020 Integer\n",
      "FEATURE String\n",
      "NAME String\n",
      "POP_RANGE String\n",
      "POPULATION Integer\n",
      "FIPS55 String\n",
      "COUNTY String\n",
      "FIPS String\n",
      "STATE String\n",
      "STATE_FIPS String\n",
      "DISPLAY Integer\n"
     ]
    }
   ],
   "source": [
    "for field in layer.schema:\n",
    "    print(field.name, field.GetTypeName())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the 1st feature's type of the layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT\n"
     ]
    }
   ],
   "source": [
    "feature0 = layer.GetFeature(0)\n",
    "print(feature0.geometry().GetGeometryName())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we can close the datasource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "del ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a new shape file copying the structure of another shape file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ogr.Open('data/vector/global/ne_50m_populated_places.shp', 1)\n",
    "if ds is None:\n",
    "    sys.exit('Could not open folder.')\n",
    "in_lyr = ds.GetLayer()\n",
    "#for field in in_lyr.schema:\n",
    "#    print(field.name, field.GetTypeName())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "if ds.GetLayer('capital_cities'):\n",
    "    ds.DeleteLayer('capital_cities')\n",
    "out_lyr = ds.CreateLayer('capital_cities', in_lyr.GetSpatialRef(), ogr.wkbPoint)\n",
    "out_lyr.CreateFields(in_lyr.schema)\n",
    "out_defn = out_lyr.GetLayerDefn()\n",
    "out_feat = ogr.Feature(out_defn)\n",
    "for in_feat in in_lyr:\n",
    "    if in_feat.GetField('FEATURECLA') == 'Admin-0 capital':\n",
    "        geom = in_feat.geometry()\n",
    "        out_feat.SetGeometry(geom)\n",
    "        for i in range(in_feat.GetFieldCount()):\n",
    "            value = in_feat.GetField(i)\n",
    "            out_feat.SetField(i, value)\n",
    "        out_lyr.CreateFeature(out_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We save the data by closing the datasource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "del ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "capital_cities = gdf.from_file('data/vector/global/capital_cities.shp')\n",
    "fig, ax = plt.subplots(1, figsize=(10, 5))\n",
    "capital_cities.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new data source\n",
    "We can create new data sources in any format for which a driver is available in OGR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_driver = ogr.GetDriverByName('GeoJSON')\n",
    "json_fn = 'data/vector/global/my_datasource.json'\n",
    "if os.path.exists(json_fn):\n",
    "    json_driver.DeleteDataSource(json_fn)\n",
    "json_ds = json_driver.CreateDataSource(json_fn)\n",
    "srs = ogr.osr.SpatialReference()\n",
    "srs.ImportFromEPSG(32632)\n",
    "if json_ds is None:\n",
    "    sys.exit('Could not create {0}.'.format(json_fn))\n",
    "layer = json_ds.CreateLayer('capital_cities', srs, ogr.wkbPoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coord_fld = ogr.FieldDefn('X', ogr.OFTReal)\n",
    "coord_fld.SetWidth(8)\n",
    "coord_fld.SetPrecision(3)\n",
    "layer.CreateField(coord_fld)\n",
    "coord_fld.SetName('Y')\n",
    "layer.CreateField(coord_fld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Real\n",
      "Y Real\n"
     ]
    }
   ],
   "source": [
    "for field in layer.schema:\n",
    "    print(field.name, field.GetTypeName())\n",
    "#del json_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_def = layer.GetLayerDefn()\n",
    "layer_def.GetFieldCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat = ogr.Feature(layer_def)\n",
    "point = ogr.Geometry(ogr.wkbPoint)\n",
    "point.AddPoint(1.0, 2.0)\n",
    "feat.SetGeometry(point)\n",
    "layer.CreateFeature(feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createPointFeature(layer_def, x, y):\n",
    "    feat = ogr.Feature(layer_def)\n",
    "    point = ogr.Geometry(ogr.wkbPoint)\n",
    "    point.AddPoint(x, y)\n",
    "    feat.SetGeometry(point)\n",
    "    layer.CreateFeature(feat)\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "createPointFeature(layer_def, 1.0, 2.0)\n",
    "createPointFeature(layer_def, 3.0, 5.0)\n",
    "createPointFeature(layer_def, 6.0, 3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vector file formats\n",
    "We define a function that will print the layers contained in a data source using its file name as input argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_layers(fn):\n",
    "    ds = ogr.Open(fn, 0)\n",
    "    if ds is None:\n",
    "        raise OSError('Could not open {}'.format(fn))\n",
    "    for i in range(ds.GetLayerCount()):\n",
    "        layer = ds.GetLayer(i)\n",
    "        print('{0}: {1}'.format(i, layer.GetName()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An Esri shape file can contain only one layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: ne_50m_populated_places\n"
     ]
    }
   ],
   "source": [
    "fn = 'data/vector/global/ne_50m_populated_places.shp'\n",
    "print_layers(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An SQLite database can contain several layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: countries\n",
      "1: populated_places\n"
     ]
    }
   ],
   "source": [
    "fn = 'data/vector/global/natural_earth_50m.sqlite'\n",
    "print_layers(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ogr.Open(fn)\n",
    "pp_layer = ds.GetLayer('populated_places')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ESRI shape files can be stored in a folder. Each shape file defines a layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: citiesx020\n",
      "1: cities_48\n",
      "2: cities_albers\n",
      "3: countyp010\n",
      "4: roadtrl020\n",
      "5: statep010\n",
      "6: states_48\n",
      "7: us_volcanos\n",
      "8: us_volcanos_albers\n",
      "9: volcanx020\n",
      "10: wtrbdyp010\n"
     ]
    }
   ],
   "source": [
    "fn = 'data/vector/US'\n",
    "print_layers(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSV files in a folder can be used to represent different layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: cities\n",
      "1: volcanoes\n"
     ]
    }
   ],
   "source": [
    "fn = 'data/vector/US/csv'\n",
    "print_layers(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ESRI file geodatabase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: countries_10m\n",
      "1: populated_places_10m\n",
      "2: countries_110m\n",
      "3: populated_places_110m\n"
     ]
    }
   ],
   "source": [
    "fn = 'data/vector/global/natural_earth.gdb'\n",
    "print_layers(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ogr.Open(fn)\n",
    "layer = ds.GetLayer('countries_10m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We copy a layer from an ESRI geodatabase to a shape file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdb_ds = ogr.Open('data/vector/global/natural_earth.gdb')\n",
    "gdb_lyr = gdb_ds.GetLayerByName('countries_110m')\n",
    "shp_ds = ogr.Open('data/vector/global', 1)\n",
    "shp_ds.CopyLayer(gdb_lyr, 'copy_countries_110m')\n",
    "del shp_ds, gdb_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering data with OGR\n",
    "We use the OGR library to filter features in a data source, e.g. a shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features 241\n"
     ]
    }
   ],
   "source": [
    "ds = ogr.Open('data/vector/global')\n",
    "country_layer = ds.GetLayer('ne_50m_admin_0_countries')\n",
    "featureCount = country_layer.GetFeatureCount()\n",
    "print('Number of features %d'%(featureCount))\n",
    "#for feature in country_layer:\n",
    "#    print(feature.geometry().GetY())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scalerank Integer\n",
      "featurecla String\n",
      "labelrank Real\n",
      "sovereignt String\n",
      "sov_a3 String\n",
      "adm0_dif Real\n",
      "level Real\n",
      "type String\n",
      "admin String\n",
      "adm0_a3 String\n",
      "geou_dif Real\n",
      "geounit String\n",
      "gu_a3 String\n",
      "su_dif Real\n",
      "subunit String\n",
      "su_a3 String\n",
      "brk_diff Real\n",
      "name String\n",
      "name_long String\n",
      "brk_a3 String\n",
      "brk_name String\n",
      "brk_group String\n",
      "abbrev String\n",
      "postal String\n",
      "formal_en String\n",
      "formal_fr String\n",
      "note_adm0 String\n",
      "note_brk String\n",
      "name_sort String\n",
      "name_alt String\n",
      "mapcolor7 Real\n",
      "mapcolor8 Real\n",
      "mapcolor9 Real\n",
      "mapcolor13 Real\n",
      "pop_est Real\n",
      "gdp_md_est Real\n",
      "pop_year Real\n",
      "lastcensus Real\n",
      "gdp_year Real\n",
      "economy String\n",
      "income_grp String\n",
      "wikipedia Real\n",
      "fips_10 String\n",
      "iso_a2 String\n",
      "iso_a3 String\n",
      "iso_n3 String\n",
      "un_a3 String\n",
      "wb_a2 String\n",
      "wb_a3 String\n",
      "woe_id Real\n",
      "adm0_a3_is String\n",
      "adm0_a3_us String\n",
      "adm0_a3_un Real\n",
      "adm0_a3_wb Real\n",
      "continent String\n",
      "region_un String\n",
      "subregion String\n",
      "region_wb String\n",
      "name_len Real\n",
      "long_len Real\n",
      "abbrev_len Real\n",
      "tiny Real\n",
      "homepart Real\n"
     ]
    }
   ],
   "source": [
    "for field in country_layer.schema:\n",
    "    print(field.name, field.GetTypeName())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribute filtering\n",
    "We select some features by one of their attibutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_layer.SetAttributeFilter(\"continent = 'Asia'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United Arab Emirates'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_layer.GetFeature(7).GetField('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_layer.SetAttributeFilter(\"continent = 'South America'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<shapely.geometry.multipolygon.MultiPolygon at 0x26ed8341be0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_layer.SetAttributeFilter(\"name = 'Italy'\")\n",
    "feat = country_layer.GetNextFeature()\n",
    "italy = feat.geometry().Clone()\n",
    "wkt.loads(italy.ExportToIsoWkt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial filtering\n",
    "We select the cities that lie within a country's boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_layer = ds.GetLayer('ne_50m_populated_places')\n",
    "city_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_layer.SetSpatialFilter(italy) # set the spatial filter\n",
    "city_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Potenza String\n",
      "Campobasso String\n",
      "Aosta String\n",
      "Vatican City String\n",
      "Bologna String\n",
      "Cagliari String\n",
      "Catanzaro String\n",
      "Bari String\n",
      "L'Aquila String\n",
      "Ancona String\n",
      "Perugia String\n",
      "Trieste String\n",
      "Trento String\n",
      "Turin String\n",
      "Genoa String\n",
      "Florence String\n",
      "Catania String\n",
      "Palermo String\n",
      "Naples String\n",
      "Milan String\n",
      "Rome String\n"
     ]
    }
   ],
   "source": [
    "for city in city_layer:\n",
    "    print(city.name, field.GetTypeName())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further select the cities by attributes, e.g. by their population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_layer.SetAttributeFilter('POP2010 > 1000')\n",
    "city_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a bounding box to spatially filter the features. We remove the attribute filter than we apply a spatial filter using a bounding box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ne_50m_populated_places\n"
     ]
    }
   ],
   "source": [
    "city_layer.SetAttributeFilter(None)\n",
    "city_layer.SetSpatialFilter(None)\n",
    "min_long = 6.671\n",
    "min_lat = 35.494\n",
    "max_long = 18.337\n",
    "max_lat = 47.095\n",
    "city_layer.SetSpatialFilterRect(min_long, min_lat, max_long, max_lat)\n",
    "print(city_layer.GetName()) # to be completed without using Garrard's script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we apply an attribute filter to select the cities with a population above 1 million."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_layer.SetAttributeFilter('POP2010 > 1000')\n",
    "#pb.print_attributes(city_layer, 0, ['NAME', 'ADM0NAME', 'POP2010'], geom=False) # to be completed without using Garrard's script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL spatial and attribute filtering\n",
    "We filter cities by two attributes and then by a geometry. In particular we select cities that have a population above 1 million and lie withing the Italian boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ogr.Open(r'data\\vector\\global')\n",
    "country_layer = ds.GetLayer('ne_50m_admin_0_countries')\n",
    "country_layer.SetAttributeFilter(\"name = 'Italy'\")\n",
    "feat = country_layer.GetNextFeature()\n",
    "country = feat.geometry().Clone() # we use a country's geometry for spatial filtering\n",
    "sql = \"SELECT NAME, POP2010 FROM ne_50m_populated_places WHERE POP2010 > 1000 ORDER BY POP2010 DESC\"\n",
    "layer = ds.ExecuteSQL(sql, country)\n",
    "#pb.print_attributes(layer) # to be completed without using Garrard's script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading ESRI shape files using the Python Shapefile Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34610"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_path = 'data/vector/US/cities_48.shp'\n",
    "sf = shp.Reader(shp_path)\n",
    "len(sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.shapeType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34610"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapes = sf.shapes(); len(shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapefile Reader\n",
      "    34610 shapes (type 'POINT')\n",
      "    34610 records (12 fields)\n"
     ]
    }
   ],
   "source": [
    "print(sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.shapeType == shp.POLYGON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Populated Place', 'Frenchville']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec = sf.record(3)\n",
    "rec[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('DeletionFlag', 'C', 1, 0),\n",
       " ['CITIESX020', 'N', 11, 0],\n",
       " ['FEATURE', 'C', 27, 0],\n",
       " ['NAME', 'C', 48, 0],\n",
       " ['POP_RANGE', 'C', 21, 0],\n",
       " ['POP_2000', 'N', 8, 0],\n",
       " ['FIPS55', 'C', 5, 0],\n",
       " ['COUNTY', 'C', 55, 0],\n",
       " ['FIPS', 'C', 5, 0],\n",
       " ['STATE', 'C', 2, 0],\n",
       " ['STATE_FIPS', 'C', 2, 0],\n",
       " ['DISPLAY', 'N', 1, 0]]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Record #0: [366, 'Populated Place', 'Saint David', 'Undetermined', -99999, '64955', 'Aroostook County', '23003', 'ME', '23', 0],\n",
       " Record #1: [367, 'Populated Place', 'Madawaska', 'Undetermined', -99999, '42485', 'Aroostook County', '23003', 'ME', '23', 1]]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.records()[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shapefile2pandas(sf):\n",
    "    \"\"\"\n",
    "    Converts a shapefile into a Pandas dataframe with a 'coords' \n",
    "    column holding the geometry information. This uses the pyshp\n",
    "    package\n",
    "    \"\"\"\n",
    "    fields = [x[0] for x in sf.fields][1:]\n",
    "    records = sf.records()\n",
    "    shps = [s.points for s in sf.shapes()]\n",
    "    df = pd.DataFrame(columns=fields, data=records)\n",
    "    df = df.assign(coords=shps)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34610, 12)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = shapefile2pandas(sf)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>CITIESX020</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>NAME</th>\n",
       "      <th>POP_RANGE</th>\n",
       "      <th>POP_2000</th>\n",
       "      <th>FIPS55</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATE_FIPS</th>\n",
       "      <th>DISPLAY</th>\n",
       "      <th>coords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8953</th>\n",
       "      <td>9319</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>Sturtevant</td>\n",
       "      <td>0 - 9,999</td>\n",
       "      <td>5287</td>\n",
       "      <td>77925</td>\n",
       "      <td>Racine County</td>\n",
       "      <td>55101</td>\n",
       "      <td>WI</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-87.89451599121094, 42.69807815551758]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>1249</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>Edwall</td>\n",
       "      <td>Undetermined</td>\n",
       "      <td>-99999</td>\n",
       "      <td>20785</td>\n",
       "      <td>Lincoln County</td>\n",
       "      <td>53043</td>\n",
       "      <td>WA</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-117.95293426513672, 47.50433349609375]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>545</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>Silverton</td>\n",
       "      <td>Undetermined</td>\n",
       "      <td>-99999</td>\n",
       "      <td>64470</td>\n",
       "      <td>Snohomish County</td>\n",
       "      <td>53061</td>\n",
       "      <td>WA</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-121.56964874267578, 48.07830810546875]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23132</th>\n",
       "      <td>23609</td>\n",
       "      <td>County Seat</td>\n",
       "      <td>Bardwell</td>\n",
       "      <td>0 - 9,999</td>\n",
       "      <td>799</td>\n",
       "      <td>03664</td>\n",
       "      <td>Carlisle County</td>\n",
       "      <td>21039</td>\n",
       "      <td>KY</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-89.00978088378906, 36.8706169128418]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28765</th>\n",
       "      <td>29300</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>100,000 - 249,999</td>\n",
       "      <td>173556</td>\n",
       "      <td>13392</td>\n",
       "      <td>San Diego County</td>\n",
       "      <td>06073</td>\n",
       "      <td>CA</td>\n",
       "      <td>06</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-117.08419036865234, 32.640052795410156]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CITIESX020          FEATURE         NAME          POP_RANGE  POP_2000  \\\n",
       "8953         9319  Populated Place   Sturtevant          0 - 9,999      5287   \n",
       "883          1249  Populated Place       Edwall       Undetermined    -99999   \n",
       "179           545  Populated Place    Silverton       Undetermined    -99999   \n",
       "23132       23609      County Seat     Bardwell          0 - 9,999       799   \n",
       "28765       29300  Populated Place  Chula Vista  100,000 - 249,999    173556   \n",
       "\n",
       "      FIPS55            COUNTY   FIPS STATE STATE_FIPS  DISPLAY  \\\n",
       "8953   77925     Racine County  55101    WI         55        0   \n",
       "883    20785    Lincoln County  53043    WA         53        0   \n",
       "179    64470  Snohomish County  53061    WA         53        0   \n",
       "23132  03664   Carlisle County  21039    KY         21        0   \n",
       "28765  13392  San Diego County  06073    CA         06        0   \n",
       "\n",
       "                                            coords  \n",
       "8953     [[-87.89451599121094, 42.69807815551758]]  \n",
       "883     [[-117.95293426513672, 47.50433349609375]]  \n",
       "179     [[-121.56964874267578, 48.07830810546875]]  \n",
       "23132     [[-89.00978088378906, 36.8706169128418]]  \n",
       "28765  [[-117.08419036865234, 32.640052795410156]]  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Aroostook County\n",
       "1        Aroostook County\n",
       "2        Aroostook County\n",
       "3        Aroostook County\n",
       "4        Aroostook County\n",
       "               ...       \n",
       "34605       Cayuga County\n",
       "34606     Onondaga County\n",
       "34607       Greene County\n",
       "34608       Warren County\n",
       "34609      Orleans County\n",
       "Name: COUNTY, Length: 34610, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.COUNTY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>CITIESX020</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>NAME</th>\n",
       "      <th>POP_RANGE</th>\n",
       "      <th>POP_2000</th>\n",
       "      <th>FIPS55</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATE_FIPS</th>\n",
       "      <th>DISPLAY</th>\n",
       "      <th>coords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>1603</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>Derby Line</td>\n",
       "      <td>0 - 9,999</td>\n",
       "      <td>776</td>\n",
       "      <td>17500</td>\n",
       "      <td>Orleans County</td>\n",
       "      <td>50019</td>\n",
       "      <td>VT</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-72.09799194335938, 45.00236892700195]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1644</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>Derby Center</td>\n",
       "      <td>0 - 9,999</td>\n",
       "      <td>670</td>\n",
       "      <td>17425</td>\n",
       "      <td>Orleans County</td>\n",
       "      <td>50019</td>\n",
       "      <td>VT</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-72.13287353515625, 44.94782638549805]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>1662</td>\n",
       "      <td>Populated Place</td>\n",
       "      <td>North Troy</td>\n",
       "      <td>0 - 9,999</td>\n",
       "      <td>593</td>\n",
       "      <td>52075</td>\n",
       "      <td>Orleans County</td>\n",
       "      <td>50019</td>\n",
       "      <td>VT</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>[[-72.39983367919922, 44.994483947753906]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CITIESX020          FEATURE          NAME  POP_RANGE  POP_2000 FIPS55  \\\n",
       "1237        1603  Populated Place    Derby Line  0 - 9,999       776  17500   \n",
       "1278        1644  Populated Place  Derby Center  0 - 9,999       670  17425   \n",
       "1296        1662  Populated Place    North Troy  0 - 9,999       593  52075   \n",
       "\n",
       "              COUNTY   FIPS STATE STATE_FIPS  DISPLAY  \\\n",
       "1237  Orleans County  50019    VT         50        0   \n",
       "1278  Orleans County  50019    VT         50        0   \n",
       "1296  Orleans County  50019    VT         50        0   \n",
       "\n",
       "                                          coords  \n",
       "1237   [[-72.09799194335938, 45.00236892700195]]  \n",
       "1278   [[-72.13287353515625, 44.94782638549805]]  \n",
       "1296  [[-72.39983367919922, 44.994483947753906]]  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.COUNTY == 'Orleans County'][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating geometries with OGR\n",
    "We want to represent a house with a small garden and other features. We start by representing some features as points. A point feature has a geometry and attributes such as the coordinates. A point can have only one pair of coordinates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59.5, 11.5)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firepit = ogr.Geometry(ogr.wkbPoint)\n",
    "firepit.AddPoint(59.5, 11.5)\n",
    "x, y = firepit.GetX(), firepit.GetY(); x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multipoint geometry\n",
    "A multipoint geometry is a collection of points that are associated to a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "faucets = ogr.Geometry(ogr.wkbMultiPoint)\n",
    "faucet = ogr.Geometry(ogr.wkbPoint)\n",
    "faucet.AddPoint(67.5, 16)\n",
    "faucets.AddGeometry(faucet)\n",
    "faucet.AddPoint(73, 31)\n",
    "faucets.AddGeometry(faucet)\n",
    "faucet.AddPoint(81, 24.5)\n",
    "faucets.AddGeometry(faucet) \n",
    "faucets_json = faucets.ExportToJson()\n",
    "faucets_gdf = gpd.read_file(faucets_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66.825, 81.675, 15.25, 31.75)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(3, 3))\n",
    "faucets_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line\n",
    "A line has a geometry to which two or more points are added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ \"type\": \"LineString\", \"coordinates\": [ [ 54.0, 37.0, 0.0 ], [ 62.0, 35.5, 0.0 ], [ 70.5, 38.0, 0.0 ], [ 74.5, 41.5, 0.0 ] ] }\n"
     ]
    }
   ],
   "source": [
    "sidewalk = ogr.Geometry(ogr.wkbLineString)\n",
    "sidewalk.AddPoint(54, 37)\n",
    "sidewalk.AddPoint(62, 35.5)\n",
    "sidewalk.AddPoint(70.5, 38)\n",
    "sidewalk.AddPoint(74.5, 41.5)\n",
    "sidewalk_json = sidewalk.ExportToJson()\n",
    "print(sidewalk_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the GeoJson data into a GeoPandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "sidewalk_gdf = gpd.read_file(sidewalk_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52.975, 75.525, 35.2, 41.8)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(6, 5))\n",
    "sidewalk_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(54.0, 37.0, 0.0), (62.0, 35.5, 0.0), (70.5, 38.0, 0.0), (74.5, 41.5, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "vertices = sidewalk.GetPoints()\n",
    "print(vertices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(54.0, 37.0, 0.0), (62.0, 35.5, 0.0), (66.5, 35), (70.5, 38.0, 0.0), (74.5, 41.5, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "vertices[2:2] = [(66.5, 35)]\n",
    "print(vertices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiline\n",
    "A multiline is a collection of lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "line1 = ogr.Geometry(ogr.wkbLineString)\n",
    "line1.AddPoint(61.5, 29)\n",
    "line1.AddPoint(63, 20)\n",
    "line1.AddPoint(62.5, 16)\n",
    "line1.AddPoint(60, 13)\n",
    "line2 = ogr.Geometry(ogr.wkbLineString)\n",
    "line2.AddPoint(60.5, 12)\n",
    "line2.AddPoint(68.5, 13.5)\n",
    "line3 = ogr.Geometry(ogr.wkbLineString)\n",
    "line3.AddPoint(69.5, 33)\n",
    "line3.AddPoint(80, 33)\n",
    "line3.AddPoint(86.5, 22.5)\n",
    "multiline = ogr.Geometry(ogr.wkbMultiLineString)\n",
    "multiline.AddGeometry(line1)\n",
    "multiline.AddGeometry(line2)\n",
    "multiline.AddGeometry(line3)\n",
    "multiline_json = multiline.ExportToJson()\n",
    "multiline_gdf = gpd.read_file(multiline_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58.675, 87.825, 10.95, 34.05)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAD9CAYAAAD6fWGBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAO60lEQVR4nO3de9CmdV3H8fdPFpGDuhzSxJZqchdYQORQ/1SEkYaYkfVH4yU5lYc84ZilMwnCKqlNiZYctJKy/viN03TWHKXSqCadAgQVeB5cmEA5LKCzuBwWhK7++N2393MjC7vP3tf1Ozzv18zO/cfe89zf2Znns7/ruj+/6xf6vkeSSvGU3ANI0kqGkqSiGEqSimIoSSqKoSSpKIaSpKIYSpKKYihJKoqhJKkohpKkohhKkopiKEkqiqEkqSiGkqSiGEqSirIu9wBapRgOBc7MPYbm3ETXX5F7iNoFH/JWqRhOAK7OPYa+x6kG094xlGoVw1OB7889hr5rHfB54FvAyXT9o5nnqZahJC1KDL8MfAJ4DV1/We5xamUoSYsSQwD+A9gIbKTrv515oir57Zu0KF3fA28FngW8M+8w9XKlJC1aDB8HXgFsputvyjxNdVwpSYv3TuA7wO/nHqRGhpK0aF1/O/B+4BeJ4dS8w9THyzdpCDHsD9wAbAdOsiKw+1wpSUPo+geBdwDHA7+WeZqquFKShmJFYFVcKUlDma8InJN3mHq4UpKGFsOfAx1WBHaLKyVpeOeQKgJ/kHuQGhhK0tBSReB9wMuJ4YW5xymdl2/SGKwI7DZXStIYUkXg7aSKwK9nnqZorpSksaSKwL8DR5IqAvdmnqhIrpSkscwqAodhRWCXXClJY4vhz4CzSBWBrbnHKY0rJWl85wAPYUXgcRlK0ti6/g5SReAXiOGnc49TGi/fpBxieBqwBNwLnGhFYMaVkpRD1+8kVQSeD7w68zRFcaUk5ZIqAlcAR2FF4LtcKUm5zFcEzs07TDlcKUm5xXAZ8CtYEQBcKUklmFYEPpB7kBIYSlJuXX8n8F7gTGI4Lfc4uXn5JpUgVQRuAHYAJ6zlioArJakEs4rAccBrMk+TlSslqRSpIvBvwGbgeWu1IuBKSSrFrCJwKPCuvMPk40pJKk0MHwNeBRxD138t9zhjc6Ukledc1vBTBAwlqTRrvCLg5ZtUolQRuB64n1QReCTzRKNxpSSVaFYROJY1VhFwpSSVKlUEPg8cQ3qKwPa8A43DlZJUqlQR+E1SRWDNPEXAlZJUujVWEShjpRTDemL4yclyVdK8c4CdrJGnCJQRSvBK0iF9h+ceRCpO128jVQR+nhh+Jvc4QysllJYnr0dmnUIq1x8CNwMfIoZ1mWcZVCmhtDR5NZSkx9P1D7FGKgKlhNJtpJLYUbkHkQr2d6SnCFxADOvzjjKcMkIpffW5jCsladfmKwLNPkWgjFBKlnGlJD2xrr8GuAx4CzFsyjzNIEoKpSXgCGI4IPcgUuHOBR6k0YpASaG0DARgY+5BpKKlisDvAi8jhhflHmfRSgsl8L6StDv+iEYrAiWF0o2TV0NJejKpIvDbpM26r808zUKVE0pd/wBwK97slnbX3zOrCBycd5TFKSeUkiVcKUm7Z3bQwCE0VBEoLZRSV8mNudLu6fprgY8BZxNDE/+hlxZKS8BBuDFX2hNNVQRKCyW/gZP2VNffBVwA/BwxvDj3OHur1FDyZre0Zz4M3AR8sPaKQGmhNN2Y60pJ2hPzFYHXZZ5mr5QVSrONua6UpD33D6SDBt5Tc0WgrFBKrAVIqzGrCBwMnJd3mNUrMZSWgR90Y660Cl3/ZVJF4M21VgRKDKXpUyjdmCutzruAB4ALcw+yGiWGkrUAaW/MKgIvJYafzT3OnioxlKbnWnmzW1q9i6i0IlBeKKWNubfgSklavVQR+C1gM/AbmafZI+WFUmItQNp7/wh8jsoqAqWG0hKwyY250l6YHTSwHjg/7zC7r9RQWsaNudLeSxWBPwXeRAxVXH2UGkrTWkAV/4hS4c6joopAqaFkLUBalFQReA9wBjGcnnucJ1NqKN0O3IcrJWlRLgK2kioC++Ye5omUGUqemCstVtc/TKoIHE3hFYEyQymxFiAt1ieBfwXeTQyH5B5mV0oOJU/MlRapkopAyaE0vdntxlxpUbr+K8CfkCoCR+ce5/HUEEpewkmLdR7pi6QiKwIlh9LXgB5vdkuL1fV3kyoCLyGGl+Qe57HKDSVPzJWGdDHpP/4LS6sIlBtKiY/GlYYwXxF4feZp5pQeSp6YKw3nU8C/UFhFoPRQWgIOBJ6bexCpOaki8DbgmcCWvMPMlB5K7oGThpQqAn8MvLGUikAtoeTNbmk451NQRaD0UJpuzHWlJA0lVQTeTSEVgbJDyRNzpbFcQqoIZH+KQNmhlFgLkIY2qwgcBbwh5yg1hNIybsyVxvAp4J+BLcRwaK4hagil6aNxN2WdQmrdfEUg21MEagglawHSWLr+q8BHSRWBzTlGqCGUphtzvdktjeN8YAeZKgLlh1LXP4gn5krj6fp7SBWB04nhjLE/vvxQSnxetzSuS4EbyVARqCWUUi3AjbnSOGYVgSOBN4750bWE0jJuzJXG9k/A5YxcEagllDwxVxrbrCLwdEZ8ikAtoWQtQMqh668jVQTeQAzHjPGRtYTSHbgxV8plC6ki8MEx7uvWEUppGbmEl2/S+GYVgRcDg1cE6gilxFqAlM8lpN/BwSsCNYXS9MTcA3MPIq05Xf8dUkVgEwNXBGoKJU/MlfL6NPBZUkXgsKE+pKZQshYg5TRSRaCmUNqKJ+ZKeXX99cBHgNcPVRGoJ5TcmCuVYgupIvChISoC9YRSYi1Ayq3rv0kKphcxQEWgtlCanphb29xSay5lVhF46iJ/cG2/3EvAAbgxV8orVQTexgAVgdpCyT1wUim6/tPAZ4BziWH/Rf3Y2kJpWgswlKQynA2cOvkiaiHWLeoHjeRO0l1/b3ZLJej6rYv+kXWtlGYn5rpSkhpVVygl1gKkhtUYSsvABjfmSm2qMZQ8MVdqWI2hZC1AaliNoTQ9MddQkhpUXyh1/U7gf/Fmt9Sk+kIpsRYgNarWUJqemFvr/JJ2odZf6mXcmCs1qdZQcg+c1KhaQ2laC/Bmt9SYWkPpTuDbuFKSmlNnKM025rpSkhpTZygl1gKkBtUcSku4MVdqTs2hNL3Z7cZcqSE1h5K1AKlBNYfS9MRcb3ZLDak3lGYbc10pSQ2pN5QSH40rNab2UFoGNrkxV2pH7b/MnpgrNab2UHIPnNSY2kNpeirnflmnkLQwtYfShsnrrVmnkLQwtYfSEZPXr2edQtLC1B5KG4D7gO2Z55C0ILWH0hHA1yePMpHUgBZCyftJUkNqD6UNGEpSU+oNpRj2A56NN7mlptQbSvADk1dXSlJDag4l6wBSg2oOJYuTUoNqDqXpSukbWaeQtFC1h9Jdk4e9SWpEzaFkHUBqUM2hlNrckppSZyjFELDNLTWpzlCCZwIH4UpJak6toWQdQGpUraFkcVJqVK2h5EpJalStoXQE8AiwLfcgkhar5lD6Bl3/aO5BJC1WraFkcVJqVK2hZHFSalR9oRTDPqQTcV0pSQ2qL5TS0yb3xZWS1KQaQ8k6gNSwGkPJ4qTUsJpDyZWS1KAaQ2l6Ku69uQeRtHg1hlJ6ZImn4kpNqjGULE5KDasxlCxOSg2rK5RieBrwLFwpSc2qK5Rmp+K6UpIaVVsoWZyUGldbKFmclBpXayh5Kq7UqNpCaQOwzVNxpXbVFkrWAaTG1RZKFielxtUTSjG8ANgIbM08iaQB1RFKMawH/ga4C/hA3mEkDWld7gGeVAwB+DjpftIpdP3deQeSNKTyQwneDpwJvJWu/0LuYSQNq+zLtxh+Cng/8FfAhzNPI2kEoS/1sUQxPAf4ErAd+FG6fkfegSSNoczLtxjWAZ8Ang6cZiBJa0eZoQTvA04BXknXX5d7GEnjKe/yLYaXA38LXErXvyn3OJLGVVYoxbARuBJYIn39/1DmiSSNrJxQiuEA4AukB7mdSNffknkiSRmUcU8pFSQvAY4DzjCQpLWrlJ7Sq4FfBS6g6z+TeRZJGeW/fIvhROC/gCtIq6RH8w4kKae8oRTDwcBVwL7ACXT9PfmGkVSCfPeUYngK8BekG9unGEiSIO+N7ncALwPeQtd/MeMckgqS50Z3DC8E3kvaSnJxlhkkFWn8e0oxHE7aaPtN4Mfo+vvGHUBSyca9fIthX9JjSA4ETjWQJD3W2PeUfg/4ceAVdP0NI3+2pAqMd/kWwy8Bfw1cTNefPc6HSqrNOKEUwybSRtvrSV//Pzz8h0qq0fChlDbafhE4nLTR1nPbJO3SsPeU0kbbjwDHAqcbSJKezNA3ul8LvArYQtdfPvBn1S+Gg4AXAF+i6+/PPI2UxXChFMNJwEXAZ4ELBvucWsWwP3A8cPKKP0eTCq2nAZ/LN5yUzzChFMMhpG/atgFn0fX/N8jn1CKG/YDnMwufk0iXtPtM3rEN+B/Sv9mVwNUZppSKsPhQShtt/xJ4LvATa26jbSqIHsv8Cug40pMQAO4hBc8nJ69XArfT5X6GjFSGIVZKvwO8FHgzXf/fA/z8cqSjoI5mPoCOB/abvGM7KXQuZBZAtxpA0q4tthIQw2nA5aSNtmc19csXwz7AJuYD6ARg/8k7dpCeDXXlij83N/VvII1g0SulAPwn8LqqfxnTJeiPMB9AJwIHTd5xP+m+z0eZBdDWNX/vTFqAxZcnYwhVBVLqUv0w8wF0EvCMyTt2kp5qsHIFtOxje6Vh5H9G95hSAG1gPoBOBg6evONh4FrmA+h6uv6R8YeV1qa2Qyk9u+mxAfR9k799BPgK8wH0VfflSXm1E0oxPJt02bUygJ4z+dtHgeuYvxH9Zbp+Z4ZJJT2BOkMphsOYBdD0dcPkb3vgBuZXQNfS9Q9kmFTSHio/lGJYz/eugH5oxTtuZD6ArqHrd4w7pKRFKSuUYngGqfuzMoCet+IdNzMfQFfT9feOPaak4eQLpRgOJO2IXxlAR5K6TgC3MH8P6Cq6/lvjDyppTGM9eXLljvjppdhmZkc83cb8Cugquv7u4QeTVJoxnjzZkTboTnfE30XaEb8ygO4YdghJtRjjNJNrSKeYTEPotqoa35JGVdaNbklrXp5juyVpFwwlSUUxlCQVxVCSVBRDSVJRDCVJRTGUJBXFUJJUFENJUlEMJUlFMZQkFcVQklQUQ0lSUQwlSUUxlCQVxVCSVJT/B+DZ0hczmfknAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
    "multiline_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygon\n",
    "A polygon can have one or more closed paths, also called ring. This happens because a polygon can have a hole that can be represented by another polygon inside the first one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "ring = ogr.Geometry(ogr.wkbLinearRing)\n",
    "ring.AddPoint(58, 38.5)\n",
    "ring.AddPoint(53, 6)\n",
    "ring.AddPoint(99.5, 19)\n",
    "ring.AddPoint(73, 42)\n",
    "yard = ogr.Geometry(ogr.wkbPolygon)\n",
    "yard.AddGeometry(ring)\n",
    "yard.CloseRings()\n",
    "yard_json = yard.ExportToJson()\n",
    "yard_gdf = gpd.read_file(yard_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.675, 101.825, 4.2, 43.8)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
    "yard_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygon with hole\n",
    "A polygon with holes is a geometry made up of one external polygon, for the external border, and one or more other polygons inside it for the internal borders. As an example we will add a closed path, a house, to the polygon that we have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "house = ogr.Geometry(ogr.wkbLinearRing)\n",
    "house.AddPoint(67.5, 29)\n",
    "house.AddPoint(69, 25.5)\n",
    "house.AddPoint(64, 23)\n",
    "house.AddPoint(69, 15)\n",
    "house.AddPoint(82.5, 22)\n",
    "house.AddPoint(76, 31.5)\n",
    "yard.AddGeometry(house)\n",
    "yard.CloseRings()\n",
    "yard_json = yard.ExportToJson()\n",
    "yard_gdf = gpd.read_file(yard_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.675, 101.825, 4.2, 43.8)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
    "yard_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector analysis with OGR\n",
    "In this section we use topological operators available from OGR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_ds = ogr.Open('data/vector/US/wtrbdyp010.shp')\n",
    "water_layer = water_ds.GetLayer(0)\n",
    "water_layer.SetAttributeFilter('WaterbdyID = 1011327')\n",
    "marsh_feature = water_layer.GetNextFeature()\n",
    "marsh_geometry = marsh_feature.geometry().Clone()\n",
    "marsh_json = marsh_geometry.ExportToJson()\n",
    "marsh_gdf = gpd.read_file(marsh_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-90.05351526934993,\n",
       " -89.14994916964991,\n",
       " 29.152351217250065,\n",
       " 30.222804331750048)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
    "marsh_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "nola_ds = ogr.Open(r'data\\vector\\Louisiana\\NOLA.shp')\n",
    "nola_layer = nola_ds.GetLayer(0)\n",
    "nola_feature = nola_layer.GetNextFeature()\n",
    "nola_geometry = nola_feature.geometry().Clone()\n",
    "nola_json = nola_geometry.ExportToJson()\n",
    "nola_gdf = gpd.read_file(nola_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-90.1657800161176, -89.59930409436842, 29.848918445570433, 30.216018360077328)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
    "nola_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection_geometry = marsh_geometry.Intersection (nola_geometry)\n",
    "intersection_json = intersection_geometry.ExportToJson()\n",
    "intersection_gdf = gpd.read_file(intersection_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-90.00556320429993, -89.61070675769994, 29.97105356561693, 30.18381850563734)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
    "intersection_gdf.plot(color='orange', ax=ax, edgecolor='0.9')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.7% of New Orleans is wetland\n"
     ]
    }
   ],
   "source": [
    "water_layer.SetAttributeFilter(\"Feature != 'Lake'\")\n",
    "water_layer.SetSpatialFilter(nola_geometry)\n",
    "wetlands_area = 0\n",
    "for feature in water_layer:\n",
    "    intersect = feature.geometry().Intersection (nola_geometry)\n",
    "    wetlands_area += intersect.GetArea()\n",
    "pcnt = wetlands_area / nola_geometry.GetArea()\n",
    "print('{:.1%} of New Orleans is wetland'.format(pcnt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A different way to get the same result (but it doesn't seem to work)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7910.9% of New Orleans is wetland\n"
     ]
    }
   ],
   "source": [
    "water_layer.SetAttributeFilter(\"Feature != 'Lake'\")\n",
    "memory_driver = ogr.GetDriverByName('Memory')\n",
    "temp_ds = memory_driver.CreateDataSource('temp')\n",
    "temp_layer = temp_ds.CreateLayer('temp')\n",
    "nola_layer.Intersection(water_layer, temp_layer)\n",
    "sql = 'SELECT SUM(OGR_GEOM_AREA) AS area FROM temp'\n",
    "layer = temp_ds.ExecuteSQL (sql)\n",
    "pcnt = layer.GetFeature(0).GetField('area') / nola_geometry.GetArea()\n",
    "print('{:.1%} of New Orleans is wetland'.format(pcnt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proximity tools\n",
    "We want to know how many US cities lie within a certain distance from volcanos. We begin by opening a layer about the US cities and a layer about volcanos in the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cities: 35432\n",
      "Volcanos: 155\n"
     ]
    }
   ],
   "source": [
    "shp_ds = ogr.Open('data/vector/US')\n",
    "volcano_layer = shp_ds.GetLayer('us_volcanos_albers')\n",
    "cities_layer = shp_ds.GetLayer('cities_albers')\n",
    "print('Cities: {}'.format(cities_layer.GetFeatureCount()))\n",
    "print('Volcanos: {}'.format(volcano_layer.GetFeatureCount()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need a layer that will contain the volcanos with a buffer area around them. The layer will be in memory since we need it only to compute the number of cities that lie within the buffer area of a volcano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "memory_driver = ogr.GetDriverByName('memory')\n",
    "memory_ds = memory_driver.CreateDataSource('temp')\n",
    "buffer_layer = memory_ds.CreateLayer('buffer')\n",
    "buffer_feature = ogr.Feature(buffer_layer.GetLayerDefn())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract the volcanos' geometries, compute the buffer areas around them and we add those buffer areas to the temporary layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = 16000 # 1 mile\n",
    "for volcano_feature in volcano_layer:\n",
    "    buffer_geometry = volcano_feature.geometry().Buffer(distance)\n",
    "    buffer_feature.SetGeometry(buffer_geometry)\n",
    "    buffer_layer.CreateFeature(buffer_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need another temporary (in memory) layer to store the result of the intersection between the volcanos with their buffer areas and the cities to know how many of the latter lie within a volcano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_layer = memory_ds.CreateLayer('result')\n",
    "buffer_layer.Intersection (cities_layer, result_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cities: 83\n"
     ]
    }
   ],
   "source": [
    "print('Cities: {}'.format(result_layer.GetFeatureCount()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform the same task following a different approach. We use a multipolygon to store all the volcanos' buffer geometries, then we apply a method, UnionCascaded, to create the union of the volcano buffer areas to be used as a filter for cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cities: 78\n"
     ]
    }
   ],
   "source": [
    "volcano_layer.ResetReading()\n",
    "multipoly = ogr.Geometry(ogr.wkbMultiPolygon)\n",
    "\n",
    "for volcano_feature in volcano_layer:\n",
    "    buffer_geometry = volcano_feature.geometry().Buffer(distance)\n",
    "    multipoly.AddGeometry(buffer_geometry)\n",
    "\n",
    "cities_layer.SetSpatialFilter(multipoly.UnionCascaded ())\n",
    "print('Cities: {}'.format(cities_layer.GetFeatureCount()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the number of cities is lower with this second method. This happens because with the first method some cities lie within more than one volcano buffer area and so they are counted more than once."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we look for the Mount St. Helens nearest city and we compute its distance from the volcano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cities: 1\n"
     ]
    }
   ],
   "source": [
    "distance = 32000 # 20 miles\n",
    "volcano_layer.SetAttributeFilter(\"NAME = 'St. Helens'\")\n",
    "feature = volcano_layer.GetNextFeature()\n",
    "st_helens = feature.geometry().Clone()\n",
    "buffer_geometry = st_helens.Buffer(distance)\n",
    "cities_layer.SetSpatialFilter(buffer_geometry)\n",
    "print('Cities: {}'.format(cities_layer.GetFeatureCount()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cougar\n"
     ]
    }
   ],
   "source": [
    "feature_city = cities_layer.GetNextFeature()\n",
    "name = feature_city.GetField('NAME')\n",
    "print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18920 meters (11.825 miles)\n"
     ]
    }
   ],
   "source": [
    "meters = round(st_helens.Distance(feature_city.geometry().Clone()))\n",
    "miles = meters / 1600\n",
    "print('{} meters ({} miles)'.format(meters, miles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suitability analysis: locating areas suitable for wind farms\n",
    "We look for areas suitable for wind farms. These areas must satisfy two criteria: \n",
    "\n",
    "1. be classified as 3 or more for wind\n",
    "2. population density less than 0.5 persons per square kilometer.\n",
    "\n",
    "First we get the population density from the 'popsqkm' field of the California census data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population density: 2830.2167844165187 persons/sqkm\n",
      "Population density: Census Tract 4251.03, Alameda County, California persons/sqkm\n"
     ]
    }
   ],
   "source": [
    "census_fn = 'data/vector/US/California/ca_census_albers.shp'\n",
    "census_ds = ogr.Open(census_fn, True)\n",
    "census_layer = census_ds.GetLayer()\n",
    "print('Population density: {} persons/sqkm'.format(census_layer[0].GetField('popsqkm')))\n",
    "#for row in census_layer:\n",
    "#    popsqkm = row.GetField('popsqkm')\n",
    "#    print('Population density: {} persons/sqkm'.format(popsqkm))\n",
    "print('Population density: {} persons/sqkm'.format(census_layer[0].GetField(7)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2nd, we get the geometry of the Imperial county for which we want to assess the suitabilty for wind farms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "county_fn = 'data/vector/US/countyp010.shp'\n",
    "county_ds = ogr.Open(county_fn)\n",
    "county_layer = county_ds.GetLayer()\n",
    "county_layer.SetAttributeFilter(\"COUNTY ='Imperial County'\")\n",
    "feat = county_layer.GetNextFeature()\n",
    "county_geom = feat.geometry().Clone()\n",
    "del county_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to project the county data onto the same reference system used for the census data, then we can spatially filter the census data to get the Imperial county census data to finally select the areas with a population density below 0.5 persons per square meter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_geom.TransformTo(census_layer.GetSpatialRef())\n",
    "census_layer.SetSpatialFilter(county_geom)\n",
    "census_layer.SetAttributeFilter('popsqkm < 0.5')\n",
    "census_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get the wind data filtering the areas that are classified 3 or above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124244"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_fn = 'data/vector/US/California/california_50m_wind_albers.shp'\n",
    "wind_ds = ogr.Open(wind_fn)\n",
    "wind_layer = wind_ds.GetLayer()\n",
    "wind_layer.SetAttributeFilter('WPC >= 3')\n",
    "wind_layer.GetFeatureCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before applying the two filters for our suitability assessment we create a new data source (Esri shapefile) where we will store the results. The data source has two attributes: wind and popsqkm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "suitability_fn = 'data/vector/US/California/wind_farm.shp'\n",
    "suitability_ds = ogr.GetDriverByName('ESRI Shapefile').CreateDataSource(suitability_fn)\n",
    "suitability_layer = suitability_ds.CreateLayer('wind_farm', wind_layer.GetSpatialRef(), ogr.wkbPolygon)\n",
    "suitability_layer.CreateField(ogr.FieldDefn('wind', ogr.OFTInteger))\n",
    "suitability_layer.CreateField(ogr.FieldDefn('popsqkm', ogr.OFTReal))\n",
    "suitability_row = ogr.Feature(suitability_layer.GetLayerDefn())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We iterate through the areas that meet the suitability criteria saving the data in the suitability data source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersecting census tract with 36 wind polygons\n",
      "Intersecting census tract with 957 wind polygons\n"
     ]
    }
   ],
   "source": [
    "for census_row in census_layer:\n",
    "    census_geom = census_row.geometry()\n",
    "    census_geom = census_geom.Intersection(county_geom)\n",
    "    wind_layer.SetSpatialFilter(census_geom)\n",
    "    print('Intersecting census tract with {0} wind polygons'.format(wind_layer.GetFeatureCount()))\n",
    "    if wind_layer.GetFeatureCount() > 0:\n",
    "        suitability_row.SetField('popsqkm', census_row.GetField('popsqkm'))\n",
    "        for wind_row in wind_layer:\n",
    "            wind_geom = wind_row.geometry()\n",
    "            is_intersect = census_geom.Intersect(wind_geom)\n",
    "            if is_intersect:\n",
    "                suitability_row.SetField('wind', wind_row.GetField('WPC'))\n",
    "                suitability_row.SetGeometry(census_geom.Intersection(wind_geom))\n",
    "                suitability_layer.CreateFeature(suitability_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "suitability_layer.GetFeatureCount()\n",
    "del suitability_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracking animals: Galapagos albatrosses\n",
    "In this example we use a dataset from a [research study](https://www.datarepository.movebank.org/handle/10255/move.331) that contains data about the movements of albatrosses between the Galapagos islands and the coast of Peru. The original dataset is a CSV file. We will first create a shapefile with a spatial reference system and two attributes, tag_id and timestamp, then we will copy the data from the CSV file to the shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp_fn = r'data/vector/global/Galapagos/albatross_dd.shp'\n",
    "shp_ds = ogr.GetDriverByName('ESRI Shapefile').CreateDataSource(shp_fn)\n",
    "sr = osr.SpatialReference()\n",
    "sr.ImportFromEPSG(4326)\n",
    "shp_lyr = shp_ds.CreateLayer('albatross_dd', sr, ogr.wkbPoint)\n",
    "shp_lyr.CreateField(ogr.FieldDefn('tag_id', ogr.OFTString))\n",
    "shp_lyr.CreateField(ogr.FieldDefn('timestamp', ogr.OFTString))\n",
    "shp_row = ogr.Feature(shp_lyr.GetLayerDefn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_fn = 'data/vector/global/Galapagos/Galapagos Albatrosses.csv'\n",
    "csv_ds = ogr.Open(csv_fn)\n",
    "csv_lyr = csv_ds.GetLayer()\n",
    "for csv_row in csv_lyr:\n",
    "    x = csv_row.GetFieldAsDouble('location-long')\n",
    "    y = csv_row.GetFieldAsDouble('location-lat')\n",
    "    shp_pt = ogr.Geometry(ogr.wkbPoint)\n",
    "    shp_pt.AddPoint(x, y)\n",
    "    tag_id = csv_row.GetField('individual-local-identifier')\n",
    "    timestamp = csv_row.GetField('timestamp')\n",
    "    shp_row.SetGeometry(shp_pt)\n",
    "    shp_row.SetField('tag_id', tag_id)\n",
    "    shp_row.SetField('timestamp', timestamp)\n",
    "    shp_lyr.CreateFeature(shp_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "galapagos_gdf = gpd.read_file(shp_fn)\n",
    "#fig, ax = plt.subplots(1, figsize=(10, 10))\n",
    "#galapagos_gdf.plot(color='orange', ax=ax)\n",
    "#ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to compute for each albastros the distances between the places in which it has been observed, in time order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unique(datasource, layer_name, field_name):\n",
    "    sql = 'SELECT DISTINCT {0} FROM {1}'.format(field_name, layer_name)\n",
    "    lyr = datasource.ExecuteSQL (sql)\n",
    "    values = []\n",
    "    for row in lyr:\n",
    "        values.append(row.GetField(field_name))\n",
    "    datasource.ReleaseResultSet(lyr)\n",
    "    return values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each bird it is possible to determine the areas that are mostly visited by computing the convex hull from the observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial reference systems\n",
    "A spatial reference system (or coordinate reference system) consists of three elements\n",
    "\n",
    "1. Coordinate system\n",
    "2. Datum\n",
    "3. Projection\n",
    "\n",
    "A datum defines the shape of the Earth: sphere, ellipsoid, geoid. A (geographic) coordinate system is a reference system with the origin at the center of the Earth. An example of datum is the World Geodetic System 1984, or WGS84. The (geographic) coordinates of a point on the datum's surface can be projected on a 2D surface, a plane. There exist many projections that can be classified depending on the property that is preserved after a projection from 3D to 2D. Conformal projections preserve the angles. Universal Transverse Mercator (UTM) is a conformal projection. Equal-area projections preserve what is stated in its name, one example is the Lambert projection. The Azimuthal-equidistance projection preserves the distance. The spatial reference system must always be included with the data, otherwise it would be impossible to say to which location on Earth a coordinate pair refers to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEOGCS[\"NAD83\",\n",
      "    DATUM[\"North_American_Datum_1983\",\n",
      "        SPHEROID[\"GRS 1980\",6378137,298.257222101,\n",
      "            AUTHORITY[\"EPSG\",\"7019\"]],\n",
      "        AUTHORITY[\"EPSG\",\"6269\"]],\n",
      "    PRIMEM[\"Greenwich\",0,\n",
      "        AUTHORITY[\"EPSG\",\"8901\"]],\n",
      "    UNIT[\"degree\",0.0174532925199433,\n",
      "        AUTHORITY[\"EPSG\",\"9122\"]],\n",
      "    AXIS[\"Latitude\",NORTH],\n",
      "    AXIS[\"Longitude\",EAST],\n",
      "    AUTHORITY[\"EPSG\",\"4269\"]]\n"
     ]
    }
   ],
   "source": [
    "path = 'data/vector/US/states_48.shp'\n",
    "ds = ogr.Open(path)\n",
    "sr = ds.GetLayer().GetSpatialRef()\n",
    "print(sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reference system has a datum, the North American Datum 1983, but is not projected. Spatial reference system like this are called 'geographic' or 'unprojected'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ds.GetLayer().GetSpatialRef().GetAttrValue('PROJCS'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use GeoPandas to open the dataset so that it will be easier to plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4269>\n",
       "Name: NAD83\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.\n",
       "- bounds: (167.65, 14.92, -40.73, 86.45)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us48_gdf = gpd.read_file(path)\n",
    "us48_gdf.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spatial reference system NAD83 is not projected but if we plot a 2D map Python will choose a projection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Continental US - Geographic Reference System NAD83'}>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10, 5))\n",
    "ax.set_title('Continental US - Geographic Reference System NAD83')\n",
    "us48_gdf.plot(ax=ax, edgecolor='0.9')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a spatial reference system\n",
    "The [EPSG](https://epsg.io) has a list of spatial reference systems. We can import a spatial reference system to be used in a layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr = osr.SpatialReference()\n",
    "sr.ImportFromEPSG(26912)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This patial reference system is a projected one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NAD83 / UTM zone 12N'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.GetAttrValue('PROJCS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have a spatial reference we can create a data source and a layer adding the spatial reference to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = ogr.GetDriverByName('ESRI Shapefile')\n",
    "ds = driver.CreateDataSource(r'data/vector/testdata.shp')\n",
    "lyr = ds.CreateLayer('counties', sr, ogr.wkbPolygon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add features to a layer, paying attention that their coordinates are valid according to the layer's spatial reference. In case a feauture has a different spatial reference than the layer, we have to transform its coordinates before adding that feature to the layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'World Map - Geographic Reference System WGS84'}>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "world_fn = 'data/vector/global/ne_110m_land_1p.shp'\n",
    "world = gpd.read_file(world_fn)\n",
    "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
    "ax.set_title('World Map - Geographic Reference System WGS84')\n",
    "world.plot(color='orange', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spatial reference used by the world dataset is WGS84 and it's not projected as we can see from the description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEOGCS[\"WGS 84\",\n",
      "    DATUM[\"WGS_1984\",\n",
      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
      "    PRIMEM[\"Greenwich\",0,\n",
      "        AUTHORITY[\"EPSG\",\"8901\"]],\n",
      "    UNIT[\"degree\",0.0174532925199433,\n",
      "        AUTHORITY[\"EPSG\",\"9122\"]],\n",
      "    AXIS[\"Latitude\",NORTH],\n",
      "    AXIS[\"Longitude\",EAST],\n",
      "    AUTHORITY[\"EPSG\",\"4326\"]]\n"
     ]
    }
   ],
   "source": [
    "world_ds = ogr.Open(world_fn)\n",
    "print(world_ds.GetLayer().GetSpatialRef())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reprojecting an entire layer\n",
    "We want to reproject, that is change the reference system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4269>\n",
       "Name: NAD83\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.\n",
       "- bounds: (167.65, 14.92, -40.73, 86.45)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us48_gdf.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We change the CRS from a unprojected one, with units in degree, to a projected one with units in metres. This is not a very useful change of CRS, it is used just to sho how to change the CRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "us48_epsg3395 = us48_gdf.to_crs(epsg=3395)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Continental USA - Geographic Reference System WGS 84 / World Mercator'}>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
    "ax.set_title('Continental USA - Geographic Reference System WGS 84 / World Mercator')\n",
    "us48_epsg3395.plot(color='orange', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert coordinates\n",
    "We can create a feature, e.g. a point with its coordinate pair, latitude and longtude, and assign the WGS84 spatial reference to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr_wgs84 = osr.SpatialReference()\n",
    "sr_wgs84.ImportFromEPSG(4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "eiffel_tower = ogr.Geometry(ogr.wkbPoint) \n",
    "eiffel_tower.AddPoint(2.294694, 48.858093)\n",
    "eiffel_tower.AssignSpatialReference(sr_wgs84)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can transform our point's coordinates to another reference system, e.g the one used by Google Maps [EPSG:3857](https://epsg.io/3857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web_mercator_sr = osr.SpatialReference()\n",
    "web_mercator_sr.ImportFromEPSG(3857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POINT (5438858.0338904 255512.483732941 0)'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eiffel_tower.TransformTo(web_mercator_sr)\n",
    "eiffel_tower_wkt = eiffel_tower.ExportToWkt()\n",
    "eiffel_tower_wkt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Great circle calculation\n",
    "We use [pyproj](https://pyproj4.github.io/pyproj/stable/index.html) to compute the distance between two cities on the ellipsoid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forward: 27.23284045673669\n",
      "back: -38.49148498662069\n",
      "dist: 9331.934878166698 km\n"
     ]
    }
   ],
   "source": [
    "la_lat, la_lon = 34.0500, -118.2500\n",
    "berlin_lat, berlin_lon = 52.5167, 13.3833\n",
    "geod = pyproj.Geod(ellps='WGS84')\n",
    "forward, back, dist = geod.inv(la_lon, la_lat, berlin_lon, berlin_lat)\n",
    "print('forward: {}\\nback: {}\\ndist: {} km'.format(forward, back, dist / 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "* [Smith - Geographic vs Projected Coordinate Systems](https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/)\n",
    "* [Karney - Algorithms for geodesics](https://arxiv.org/abs/1109.4448)\n",
    "* [Institute for Environment and Sustainability - Map Projections for Europe](http://mapref.org/LinkedDocuments/MapProjectionsForEurope-EUR-20120.pdf)\n",
    "* [Tenkanen - Map projections](https://automating-gis-processes.github.io/CSC/notebooks/L2/projections.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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