spatial_relationships.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c18eae82",
   "metadata": {},
   "source": [
    "# Spatial relationships\n",
    "Spatial objiects such as point, line and polygon, may have one or more topological relationships such as containment, overlap, neighbor, and also metric relationships that is the distance between them. The Python package Shapely, included in the GeoPandas package, provides such functions for planar geometry. Shapely is a Python port of [GEOS](https://libgeos.org/), the C++ port of the [Java Topology Suite (JTS)](https://github.com/locationtech/jts/), a Java implementation of topological operators. We begin with a simple example with two datasets, a set of points that may represent point of interest (POI) and a set of polygons that represent the neighborhoods in NYC. We will find out the POIs that lie within one of the neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4ccd499",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from shapely.geometry import Point\n",
    "from geopandas import datasets, GeoDataFrame, read_file\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6483db48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NYC Boros\n",
    "zippath = datasets.get_path('nybb')\n",
    "neighborhoods_df = read_file(zippath)\n",
    "\n",
    "# Generate some points\n",
    "b = [int(x) for x in neighborhoods_df.total_bounds]\n",
    "N = 8\n",
    "pois_df = GeoDataFrame([\n",
    "    {'geometry': Point(x, y), 'value1': x + y, 'value2': x - y}\n",
    "    for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),\n",
    "                    range(b[1], b[3], int((b[3] - b[1]) / N)))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999a9ecf",
   "metadata": {},
   "source": [
    "We have to assign a Coordinate Reference System (CRS) to the POIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf93b31c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pois_df.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dadb60ba",
   "metadata": {},
   "source": [
    "The CRS used by the polygons is EPSG:2263"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa482a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:2263>\n",
       "Name: NAD83 / New York Long Island (ftUS)\n",
       "Axis Info [cartesian]:\n",
       "- X[east]: Easting (US survey foot)\n",
       "- Y[north]: Northing (US survey foot)\n",
       "Area of Use:\n",
       "- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.\n",
       "- bounds: (-74.26, 40.47, -71.8, 41.3)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 New York Long Island zone (US Survey feet)\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_df.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5101d984",
   "metadata": {},
   "source": [
    "We assign the same CRS to the points \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8aff110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:2263>\n",
       "Name: NAD83 / New York Long Island (ftUS)\n",
       "Axis Info [cartesian]:\n",
       "- X[east]: Easting (US survey foot)\n",
       "- Y[north]: Northing (US survey foot)\n",
       "Area of Use:\n",
       "- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.\n",
       "- bounds: (-74.26, 40.47, -71.8, 41.3)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 New York Long Island zone (US Survey feet)\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pois_df.crs = neighborhoods_df.crs\n",
    "pois_df.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1915f9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(905464.7390380859, 1075092.8783935546, 112485.76063504723, 280480.4142594267)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0fi2uB34F3E17nTLKmYQlNZ6DdYcdwewZ2aT7rKpBe71/WlP4CshpmVR2MNSSi0rOx1ry/JLqxuecFPGaE9um7ZTeXxP4EWZobpUoiwFo0Jkp8lXVV1TGosnXUo11qZ4BXWJLLE/p3w3u0Qxj0r02xPKgqhmfVzFH9goZ3K8n8Gp4pyVSEeYhwCIZDjsFix5tgTl6K5mASXyA1YptUGOcdYC/S5QNNeib6b1NsSTE1dL3m2Jbmaew1cTVWK+vWeFxibIIFvJvtntFNYPZCaYBK2rQ97t0v47jKyCnVRbCkgOzYgKwnQadANSK8PxCg74lUTYGftpgvAWBhUrZ0Bp0sga9UYP+RoPugvlyltKgpbH+ia2qynkHC5P3GktjSpPDBjdATqscRPXtQTu8C2ygQUvO5pOAM4HyEPk/gT+nEHc1p3SJt7Gw/ZFYxGrxGluruYHPpxSC07A8nEGkXJvdsCr1/2CpBr2Sf/NziVK3yaNEWVCifKkIgvfuhHaaJvlXnqN1oflaPKFBV05jb6hB70rfjwQOBCZq0HMlyuaY32fuOmNdj8mmzgm8j0WoNtKgs1o9p9qs9TCp1KeARYFlNejkagOm5MPp2M/7Kt2Ri22Gp4B168z7QmAX4Cxg315OXnQD5DSFRJkP+AFWo5UVMzCt5PHAHBr0rSr33Qgr9WhUET+j4pz3gI9p0EfSONditWSVq6Krgb006AvVBk3RtMWwrWJWhjcLjgN+XJlBnYTOxpe9dTuwY69GzXwL5jTLQgxNKKwaI4GPa9B3k49nS4lyqkQZJ1FGSJQjsW1XI+NTGqucEQx2Do+metDlM8AjEuW71bYsSZ3wFeAfzfxAXWRnquscfabi9SeAeyVKK80hu4YbIKdZTsWcoFmzDszqIf8XrFvEBdjK5CAsQtUOcwFnSZTl0ut6bXQWwDSFrk0FrINI2dXdaBfUCssBn6/yfrVnehngNomyX6/5hdwAOXWRKGMkypoMlCJkyXTMEQywOwNqiktmdL8PAdMlynrAyk2cvxXwkEQ5qEqGdGWkrBc4IG0Ry/kj5gOrZBRwPHB2ymLvCdwAOTVJn5ZbYfVQnXgADwGelSirY+Jgb2c8/hyYM/qfWP5PM5R6ht0vUcYBSJS5gA9nPLcsqJaWsAAWXazF14DbJcqyHZtVC7gBcuqxDCZlkbXWD8C5WNj9FuBTGvQa7IHKmna3HAtjNWlgOUGPZTOdzAkS5Ztlr4/G/HX1WAfzC322c9NqDjdATlVS1Gtjsq33KjEeS2Y8A/vEvjAJeR3YgXu1y+2kSvTkiL4v19nU52SJskn6/uYmr/kQcKVE+WmefiE3QM5spD/IlYGXqd6yeKh8Fyun+AwwDus6+gCzi4/lwZvA48ChFSUPlVGwafQOIzHfzodpvPqpJGKJnkPtZtIWboCcaiyGGZ5tmL0F8VA5F6uzOg2T8/gM9hB0wtC1wwkadBUNWlmEejWwB3Al5nc5veszq89KmHDahtTRS6rBTsDdyYB1FU9EdAaRVj8bYgJZv8GW6lnxArAGpve8IVbu8DDZlXZkwTRs9TdRg86oPJiKcZ/AwuC9yr0MaGm3wmQsafGGjOdTE18BOZXMj/llTidb4wPwdSwatS/W1O/n9JbxAYv2nUKVFVlSI7yN3jY+YCqVjwGzZZY3YDRwjUT5n8xnVANfATmDSCugK7BuElnyYyzZ736sE+md2FaspxLjyngI2yJehxmlD2OFsh/Jc1It8C6mL7Rem9cfCxxUbRWYJW6AnFkkAfb/xbKRs+ScNO55WIbybpghWiXj+3SC97CeZLmHrNvkLUyipB2uA3bRoJOym85g3AA5g5Aof6F6in+7PIblnWyNaT4vhxmjLDupOrWZiiVkNlNPV41HgW016GyyJVngBsiZhUTZFLi14YnN8wHwaSyH5kHgh5h28yMMlF04vc8bwFc16JVZD+xOaKecozIe7ydYYtwpWNj9QuBk3PgUjYWBKyTKEUmrKTPcADkASJSVsK4MWXEn1jHi05jBORKLgm2b4T2c7nIwGfsH3QA5pQ6c48ju72Eq1rRwJiYC9gXMB3FsRuM7+ZFpsqIbIAdsib1rhuOF1PpYgEMxKYhf0Xs5P07rLJblYG6AHLDclqykMJ4Cjpcoc2jQGRp0GhCAvYGXMrqHkx+ZitK5AXLAktaycAwr1mn0A0yjudTCeRdsC9at/llO51ggy8HcADklGnUQbYZzSy12koQFmBO6E1KuTj5kWp7jBsgB632VBWeXv0jO7TNorKb4V+qr+Dm9wxJVZGDbxg1Qn5Nqvz7HQK/1ofBkxeuPYD3PwSJi1bo4vI9t/zw3qBiMwjS7MxvM6WM0qEqUBzA9maEwuUpvrW2wDOjTgLUwH9ELWIHkdsDdWLb0p4Z4b6d7PAy81vCsJnED5ICVRgyV2TJkNehxEuXfmIhXeSeGx7EuGOth6oi9WhHvzM7hFUqRQ8K3YA5ko3p4aKW2sERZGDif2VsarwwcgG37jqB1BT8nH54ELslyQDdAfY5EWRJrvTNUxqcGfqVxR2DZ0EvUOH8tLKv2cKyXldP7HKVBq/nx2sYNUJ+jQV9icC/xdlm74vVIoJ6EQ6nxn9BbAu9OdcZjet6Z4gaoz5Eo3wW+l8FQ35AosxINUzLiVVj0qxF7YPKsboh6lyOyXv2AGyAHFiUbZcKlqKgnS0boxSbn8F3gLMwQHYsJYTm9wYN0YPUDHgXra1IvqFsxJ/Q+GQw5qWL8hbDuqs2wAIOlHk7ABPJ7ooVwHzEd03D6B6aLfR/wVLl/L0vcAPUxGvRdiXITptOTBZW+pNHYJ+fywCazn16X7wJbAJeTcf2RU5XbsWDARRr09W7d1A1Qn6NBp0uUZlcpjXio4vVWtN/tVLCV2TcwEftPAIu0PzWnCoqpVB6uQbPIBWsZN0B9jkQZA3w0g6FexVo5l8YdjUlwDIVdMAmPLEXynQFu0KBZ6kC1jDuhnS1pvZ94NR6o8BMcRvs9qUpsh3UhdTrDpnn1hC/hBqiPkSg/BDbOaLg7ysZdC2u9M1TGY9sED893hrmADfKcgBugPiVlKv8c2D+jIa8t+/4HDN1x/Buskv6Y9O+DIY7nVGf9PG/uBqh/WRr7BMyCV0kroKSAuMMQx/sz9sm8DBZF2w8vWO0UH8vz5m6A+peZwNMZjXVRWQ/xjbHwe7tMwwzahun1LsBzeMCkU/gKyMmFTYHFMxjnA+D0stdD9SlMwTKjSyyCb786yfIpEpoL/qnSv2yEZRo3w7vAv7GVydbAhPTvfOBeDVpeNjHUkPnCzC4PsjTZCec7s7MwFVns3cINUP/SqEPFu1g7nSuBxzXozOS41mpp+RJlCUznJwu/0oYVrz8E3AJshvuChhVugPqQJBy2ZoPTPlvqcFGirNMFqUf4SGBG8v9simlA3wqsM8QpVmtgOA64FxhLNgJqzgBz53VjN0B9SNKBfgFYocrhG7HuFrOq2CXKOOBZLGnxk1iC4Uxsa7SvRPkrlrF8AnAelrvTiZXKuuk+j5BN9rZjVCpWdg03QP3L3sxeu3UYcCnwkAadkVZK3wJOwaqkq/29HIOtTEri8z/ARMsXrXJuFiyJ+YKm0LwPy6lPbr41j4INM1IvroZo0IeBCLyHGY89gaM16P1lIfWNGND4qTXuUsCZWO7PUljpRKeMT4l5sDk72eAGyBk6EmUd4B8S5ZtNXhKB1bGq9XM06KzmgBJlRczvsmkT4ywM/Br4LOYH6jRz4uUZWZKb3IlvwYYXh2O9th5o5uQUzaqlB/0ZZtd5rsXK6euYJs/PgnGYaNZaVHdaO82TVUZ8y7gBGl7sB0zXoBOGMkjy/VwDHJXBnDrJxlg+0otYhw2nPRq1zu4YboCGERr0qYyG+irW1bQITt6xWLfOdrgPeAuL2G2W0XyKiIfhne4hUVbBolVTgVOBFzXopHRsDJYIuE1e82uDqcAbNJ8fNAlzlpfKRu7pwJyKRG5heHdCDwMkyqoSZWyT544EjsOiXt/GMozHlJ2yCTAZC3MXhfVorvsGWA7RVAbXrGVRE1dkclsBuQEaHiwD/KtJbefDMQdziQ+RVsISZQUswW8csGDWk+wwY4F3GpzzCNZlo7Jb6zL0d1TNDZDTPhr0OmBVGqwCJMqXgIPK3poMHFfmO9oKi2htyNAkNfJgfky2oxYTMENT7ecaQTbdYYtKFpK8beE+oGGCBv1PveMpr+fs9PJd4BfA7zToy+n4xsCKwI6dnGeHeaPG+//GQvX18l1eJZsGjUUkt24jboD6h/cwx+szwCHAo6WqdonyKeACYLH8ppcJnwD+hRmal7GVzQgso7sRHWm8VxByy6NyA9QnaNAXgbUlymgNOhlAoswJ/Az4LfB+jtPLkiWANzGj08rf9zzA6/Rn77HcBN/cB9RnlBmfDwF/wh68sxk+LZCXxspLWv1wXR+ou40dxozJ68a+AupD0srnZmC1nKfSayyPOavH5juNrpObyJuvgPqTkTQOWfcjcwPP5z2JHLg3rxu7AepPRtFkwWof0o8+oKvyurEboD4k+YFuBx4FZjQ4vd9YAVN77Bfew2R0c8ENUP/yNhb9uibvifQYc2GpCv3Cqxp0el43dwPUhyS5jUWAs7D8nyfKDpcevvex0oV+ZCY5rgq6zGt53twNUB+SEhCX06BTgL8C/4sZnIewBD4w45SbTkzOfJjhk5bQiJcbn9I53AD1L+9JlHk06NvAGlj+zC5Yl9PdgCeBlXKcX96MxUpWhjuT8ry5G6D+5VFgE4myE7CUBn0dGK9Bz8ES8g7Lc3I9gGA5QcOdMXne3A1Q/3IZpgb4GPDz9N4qEqUkVPatvCbWI0xk+JSn1KNab7iuITp7l12nj0gKiO9gDQkvwaRJt8a2H51ur9PL3M1g0bJKHsWKOOu1uH4FeAETzh+Z3dQy5UYNukVeN3cD1MdIlA2w7N9vYPIcf8Mq4nv1Yekmt2DCbLU4HWtDVE0E7gPgWEzkbGXgKex3+t/UN1h5MANYQYPW01LqGF4L1qdIlHkwPehlMAmLG+nvFU+JezAf2LYNzlsDS1MoGaC3sQZ/o7A6uyWBPTozxUwZCewF/CSPm7sPqH/ZHVv1jMYkWt34GOfQ3AfzugzkTF2JpSyUcmr+AXwq85l1jk/ndWM3QH2IRFkYqwU7hhw7IvQoCwMvNXHeXAwI93+YAdVFMMf+ctlPrWOsJFFysQVugPqM1D3jN9j2YGyuk8mHidTXzj4cy4RuRhtoavq6JBYxK+lNL9Hk9b3CQtjP0HXcAPUREmVp7JN5J/o3zL4s8HSDc3bDDPQL6fVUqqsGLoJpSY9mcEnDx4EbhjbNrnItpiLZddwA9Qmp/utQzO/TD/kt9fgo8GCd43MDX8GiYAdiPrJqNVNrl41zI7Z1m5nevzObqXaE1zED+Uvgoxp0Gw2aS9a3R8FyRKKsDmyOhXzfB57SoJnLY6Rt13OYWPvHsx6/h/k7JjexVcX7i2CO4jXqXPuSBn0aOEaiXEL1LcpawK+ATbHM6W2BNbGV0wRgb3qr08brWNLpbzXo1EYndwM3QDkgUZbDWiPvy+BV6AMS5asa9P6Mb/k2sAM5Fx7mwCeAgzFDU9mMcBy2CqiVhHcngESZD9uyVmMe4I/AT2FWke+/SwclyhewsH43Hf0vAr8HvoP1SisxAfhEak7QMwy7RMQU4ZnBgINwlQ480PXuvwS2fB8DzJHmchsWIVkMa/q3K7WN/6vAl4Bbs1gNSZTRmJHbEvjzUMcrIO9jWc2bVjl2OpYYuE2VY0dp0IMBJMoJwHfT+1OxFc6zmEP7WA36cK2bp0zzPYBA97rNvgUcif0drp7eyzXjuRbDZgUkURYC9sGWwWtgWapLAudJlLGVll+inIKJMYWM7j8Ka+p3ArMv13/cwlCLYv6EbwMnD2E+c2L9vzbBwsT1mvINZ+bCHvxvY76ZPcuOLYStDN/AkghLvMrgZ2M/rIPIeOA/pX5qzaBBJwHHS5RTMX/S9+n8/8WCwBHY393PsJVQT1b2D4sVUPqUvwFrrTIVcyJOwD7dRgDXYbq3E4BnNOj9EuU44EENena1McvGFmwfvwT2iTcea2I3Eks+mxv7w94d09XJgrew7dmfNWhbDmOJcjz24DjGS9iH04mYUS6xCfa73hVLLNwq+X46QmqHdCy2Oul0ycsrwKWYL+p3GvQbHb5fyxR+BSRR5sVEtdZPb82dvo4tO20LBhyRd0mUx4G9NOi0OuOuCOyPNbhbN739LvZJeAO2r/865oict9oYQ+Bd4CbabJeSkspc63kwSwLnMbsBOgbbIs0D7KNBm0lCrItEmbdWVEmDvgbskVZER9HZjOnFGejy0ZMrjUKvgNI243IsTNoKb2J6yOdigkyvYwZrZeCr2JZlOWprpSid7aX0MqZYWNNA1kOiLIaFh4vearkTnIAlCR7CwAfH7tgK+d1av3OJMhL4kgY9v8qxz6Zrb06v9wR+r0HrdhxNq+v/wfxLa7fxszTiLazebwvMZ/WVXol+lSh6HtDJtG58wPb+B2HFhE9iMhS3YqubL2CrmjF1ru90I7fngXXTH2g7rIcbn1p8D3NM74j93++OOWgnNTD4W2P+xM9XOTYK+JtE2T29Xpgmqt41qCYXwHrYNumN5n+MprgHeBjYHjO8u2U8/pAp7BZMoqwJfC2DoeYDtstgnCyZS4MOJZEti9/LcGY1Dfor4CMtXLMF1sqoWkT1Jsz3eLZE+SEmZfssjTOuAdCgM4HfSpSzMX9ltYhdqzwHnKZB/wwgUTZPc+opCrkCkigrYM61OfKeS4eozFlpGomyAPaJ51TnMiwiVheJMkKifCRFS8EkKw7UoLM9xKnP2hXY3+PaWNRpy1YmlVa7iq3I9m7l2gpewaJtq5eMT5rj+Rq058pDCrcCkihLYXk1S+U9lw4ylL5UOzDgiB9uvIytNnZt8/pfAIdp0PeaOPckzBD8MxmHL2rQf9Q5v9KZuizMMizL1hP8kihzATPTFnAithp6EHOQb9zEXMFykwJwXruR0zwo4gpoS4a38QEYK1FWaVMiod2HswgsjGUab0HrPcteAA5u0viAOar/gNXPLYaVddTjYxWvN0pBknHAsxKl5rZYg75f6bDWoLcDn8P8VPV4FfNrrapBzyyS8YFiGqB2nM5FYzEspX4VidLqNnOtDsynV5gTWxWciKVIHITVetXj/XTN37HoZlUkymrJYJS4H4uMXYdtqa5POTzVrl2d2aVNFsAiqiU/0OYN5lmNEQw4s6uFq/8MjNWgJ6ZtYOEoogHql+jOPBr0kUah3HLSimnxDs6pV1gNk5BYB4tO/V+N86YB22nQA4HdNegTlSdIlJESJWACbTuUHfosFhWdgq1CXkk5PEiUFSVKef7OY8BpVe5/GuZQPgnYMZXplN97M4nygES5WaL8V8WxEVhzyLmwreO8zO67OiivKvasKKIB6qliug5S9dO2AYtQzP/TdtkFM0T3YYbo3orjZ2rQ6wGq1dUlfaQbsHIFBW4vS314ApNavRJLYDxaoswlUZZJmdI3lQ9F9QjTCMx4jMRWRJuX7itRfp7uvUY69l2Jskw6LsBx2BbseeBwDTpVg56M+XkA/qJBn2r0C+p1ivjHulreE+gS7Qia92Mr5XmxavSNsTY638AiQWdj/puqSJRPYtusUueLKzC/z5oAGvRYDbodZuT+DjyS/CvfkShzl+rBUoLizlguz+tVbvVVbLs4hQHJ1t9jiZClINADWO3WzDTeIZhfB+AnFaucI4ALGVqgomcoXBSM/pGU2E+inKlBH23hmkmdmkwB+BlWhDkJWEuDvlLrxCSTcQ4DBgHg88CaGvRf6ZxRwHc06PESZcN0zulpO1fOslhpxw5Ul93YB/ugfxhLdoXB+UfPYEZzg3T8LQaSYB/EsvUHTR8TPTu11s9XJIq4AqopfTDMGAncIFFWbyEjeipQSGdkRuwPRKz/VlUkSqkmbL7KQwzOK5sfWE2ibAucoUFPrzHkSpjkRS3Nn89jW+OLGXCC35y+TgJuSOH3vdMcxqRjFwI7lW8d0+poJnA+cJVEWanWz1kUimiAqi1zhytLYj6Iesp9s0h/rPWkRvuFeqHrT2CO3UpuoUxMLJVm7KlBr9Sgd9QZbxT1i5Ev0aAXYwal9P9YklmZGzhIoiyCOb3LeZO0HSyb0wzsmd0fWJGUa1RkimiA+q3K+7+AyyojKHVoNT+myPwCK4+opF4JxHgsOgbm4D0MWEmDfrLVHJrU3HF36stqKIAGfT7dG8zn8wr2/L2BaRSNqbhuM2BcldXvjxiooF+6lfn2IoUyQOk/Yzgn2tViBeDC5JdoxN2dnkzOTMIeXrCat+PKXpeo2dVUg96LdbGYU4Muq0HDEPR/FsEylx/BolP3MmDcSmwnUdaVKKM16EVpDu9hOUaXYEGVX1QZewHgi1hkbkzZ+7/A9Kn2I/vi1a5TKAOEOerWyXsSObEZzSVhnkvj7Nki830Gcp2WxJyxP2RwQuLREuURifKVagNo0Gmt5FeVkCgiUa6WKJ9K/piPA0GDfkWDHoZlQ2+A6VOVWBCT8aj0zf0fFu2qbBLwFnAK5pxeEnM5lF97KvAo8GvgColSMyo8BDWFrlE0A9TvXTyjRKmrK5z0XrancYZwEZmBbXfKt+GLYauP3SrenwrsI1Ha9pNIlPlSouIJEmUV7HkZC/wF8xedjEm7ALPkNe7HDOJUBiJYs/ktNejlGvQZzJiczYCG+W2YRtBKmFb1DhU5TOMZiHYKVoF/mkRZtWzeC0uUA4F7JcrdSSe9JylaGH5s3hPImXUwKc+T6p2kQR+VKH8BvtyVWXWP07GHs9LnshJWI3gG8M303jpYx4qaiauphGIzDXpKjVP2xpQ0l8J6aI1M9xqFOZQP0qD/Sf65hTDj8HENeotEOYABJ/IOWDnIbGjQv0uUf2H6zUdhEbIHMFH8KWmei2FdLl7EVj4XYLlEm2NKoKsAn5Mob2OlJ6tV/I5+hJWt9BxFWwFVi170G79OoeRGfIthkqyWmAz8jtqO1xUZ3IbmfeDrDTqLPIPJo64vUZavcvxSTB53cw06EYtUlWqybgHOkCj/DdyF5fDMx0BR6u8ZCLtXXbVKlP+VKK+ke6yLJR9O0qCHlBmf+YHrsaTKw7Bk0+cZnCk/GtuurYwZvUoDvXcbNYVdoWgG6IG8J9ADjAR+V0rbr4UGfQtzYr7VlVl1ntGY1k55+cH52KpnBrYVK63o7we+2iiqpUHfAa7B6ryqGetngF016GsSZRy21XsIa9O8X6oNWxuLVN6PPfwlI/g1BrKs/1A5cNoa7oAZk3OB5TXoFA3617JzBDO6pfD9O8kwjaC1hocLYqurnqNoBuhOGssi9APbYgJZdUlZvethiXcPMHuEpmicChyNdTcBUA26F6ZouSVWvrI8sI4GvUiizClRvi5RbpUodyQ/TiUnARMlymwaSsmnU/LN3IX9Hi8Cjmcg3eFAbIu2lQa9WYPG9H6pB9e/gNPKsqlLTMd8ms9h0bRqBbUfZvA2+rH0dTVaF+P7gUT5UYvXdJzCidJLlMWxKM/ovOeSMxdq0JZ8PCmMvwL2cByCRZN60Q+4N2Y496xy7DXMAM+FJV0qtv1aDXPajsFWSWOw7dBHy679aGVpS6o6nxdYWoM+nrYqozXobCHu9Pubp5H0RRrzDiwidhHm//kn1kBhp0Z9xSTK2ulnug7T+ynV+G2rQa+SKCszYIxaZaMhyv1mSi/+8dVFg74iUc7AskH7mYai55Vo0OlYlfcTwKkpke6z2Kf4+vWu7TJ7YC2PFsUieodihnNXzPdxO/ZgTsA6zTa7kt8QizqVs6YGvQ94PL0+GfMLPY/l2ZyiQc+EWb+/Zkpd1sGMD1jE7Jk03x0wP9GUWhdKlHWwrOlxmBGbM117dRrzKga2du2wIqntdC9QtC1Yib/kPYEe4I2hOhY16Hsa9BIsF+UArM6oF9gAe/huwpo9Hot1i90B29KMwpyuG9Ha33C1BMVK5/NGafyxmGP4DImyc7M3SBIf8zEQAHgqraa+g4XP3y87dzGJsrVE2VmifFNMlP5a7Of8JbZ6ewJzLp8IBDE99KH0oZs0hGszp3AroESl7ku/8U/gy+0k01UjdWU4VqLcj20ZutXDvB7zYyHn72FbxvMwh/qumFO3Hb2kNSvf0KCXSZSldKB1926Yv6eUczaDBhnHKSq5bbpmf6yOa3nMCN2b7nOhRLm79H8mUb6EOdErDejlWGuoUgudkZgBOjO9PhkzIndhK7pWmAS80+I1HaVwPiCYJadwcd7zyImJwPr15CaGgkRZH1ttzN/o3C6yM+bf2Qvz7xyNRYfaYVUNOst/kiJN85f7dSTKdphBmQz8VoOOrxxEomyNOYJvwLZv1bbEX9Gg51W5dh4s7+enVa45FVvhZN1aaTI2z5M06O8zHrttCmeA0h/MfVT5NOsDPgA+oUHv6eRNJMq+NEh27CBTsFKE/RmI9LyK5cDMh9VC/RITCmvZDwZsOZT2NEk3+kQs4VHT19ewVcprmF7V25iD/1oN+nbF9SOwhMXLsRVMZSnGC9jPnYX08HRstXgz8BL2+3rEndBtkozP0fSv8dm+08YncRYWVdqD7rf4mR/z/+zCQOb7osBvsBKHuzBH6kTaM0CbYKuWoXAvVki6M5adfYQGram+WCKJ2v8KK7cYh23VKsmqwv1R4PO9LttaqBWQRNkMy0DtR76dNIG7QorG7IJtFbrN6ZhKINi2YeX0/XTMB/Q45hv6ehtj/xPYMPm9gFlCX+ekl/uXxOfrka65B4t4/VWDVmvZXDp3bqyE5EDMqL6HaUi3kkwI5o/6D5a8OD59fztWlzYXJoy2MbYSO7fa1rHXKMwKKHn/r817HjlxFbYt6SYPYE7gZ4HlunzvJbCw87xYPlCJUZjjdmnazwNbL40zDWatqs/HjC3YFqphwp4GnZHUEr9B9XbNpPFXxbZs5Z1S56E54zMNi/hehxmaJ1IqQC0exOrECkNhDBC2/x+uHT/rMQU4plHyWgdYCHOSroxtG7rplF4Ly0v6NYNzXl7DkhRfY6Au8O/YdvFRrO3yodSP4gmwKgNGYwMGjA9YBf0Bzfy+NehLmATs4BtYi50x2O/uDEzbpxnewwzNw9iHzh1F7ffVLEUyQD0VPuwS04BPa/2WwJ1iXmwlUFPcq4MsioWwd0jz+AAzxFdjgu57YgWa52GSte+XtlQpU/6HDcY/kIEw98oVxx5p19hLlEWxCvzSaqeRHs+7mAG9HhOSuyPpQ/cNRTJA/dKQsJxv5mR8wB7MTcte38LQMnBboZRodxWWbjETW/Gch2UGvwfMV8NQ/I3GBmhziTIyVcqX55RNx6JtLSFR5gV2xNIEav2OZmK+m6cxo3Mt8MBwX+E0okgGaO28J9BlrtCg53TzhiUFvfRgv4Z1Bt0Y24JNZ+gG6B9Y5fdEzMc0J7a6WRzbXs8DLJPevxpLAByFRb12xPxRE9LrbdI5g9Cg10mUJ6nThhk4vkym45k07kvAFzToC5UnS5TRmPP4qSSnWgrHhzSX7Zi9y0aJu7FQ+HlJocApozBRMInyN0wcqh94Aquufq7WCRJlXs2oLW96wPbFyjHAPqHfSPM4RoNOlyhfxfSX78Ee/qHyH2xV9a/0eg7M8MyLNeObTdExGciPYMbw5SSnUXnO3FjeUC2f1SRgufL8HIkyZ7WtTyp1ORDbii6AGcuJmAN8IWp/gL+ABQ0uLU96dGanSAboBUz2YLgzCatYrvuHK1Fu16CfGOrNUmLc7cyeEFc+n6sxw/MGViaw3VDv24DNNOht7VyYsoyfxfxI5UzGtnDHaNAJTYyzMrZy2aDRuYmpWLTqHCwsn0mZzHCnEFuwVODXD8bnbewBr9ulIa0Ebq13TgssTm3jAxbN2ZXudiPZEEvWaxkN+p5EOR2rov8TJlvxHHBvA3VEYNbv9lvYtrMZDfJXgMOBP/S7P6cdCmGAgE/mPYEuMBnYpZkygeSjyUTjV4O+JFFepPsGfjqWkbwQtl16l4GtU0v9uarwM2wb19LyPmk7n01zW8w3sXKVo6ttBZ3mKIoBWjHvCXSYyVjHzodyuv9PscziVWhdaa8WMzBfyLNY9GciZnRexfJcHi6rQM+UBsl6s5FWPV/Cyj0aVdlPwGrRznHDM3QK4QOSKLdgfbGGI+8D22nQ6/OeiESZD6tvOgKLRjXDe9g25EGszOGB9PWl0pZHokgOiZRNkbSZT6GxX+tJ4OfABe7fyY6eN0Cp5mYiJkA1HNlFrXd4zyBRdsUaAI7EwsvTsDD1o5hPZSIWxXoaSxCcjukz94qgWUOS8/2bWHFzvbKOuzAN6EtaXVk5jSnCFmwcw9f4/LrXjE/iYg16AVRfvaQty8hefiBTOH6kBn0nJQrOgxnTJTG/0yFYZXwtbsTKLG7r1dXbcKAIK6CTgX3ynkfGvAecABw8lD/uXt7a5IlEGYuVaHwY8zethK3iGnUIfRPzwx3SbhqA0xo9vQJKn2K75z2PDvB9DfrbDMZZRKKsNRSBraIi1qL67RoGeA8GumGs3cRwN2N+oEuwraQb9S7R0yugYar/czeWaFgYf0kvIVG2wpzk62Ptbg7VoB+krOU5sUjWHk0MNRnLEzpTg97Vqfk69enpFRDNJYIVie8BF7nxaY7ka1oLi8gthVXmlwt/HQB8RqK8hWU+L0Bjf+FkLMnw116blT+9boAWyXsCGfIQcHIz2bjOrJSA39E4A3uNBsfBtJtvBk7DyiQyqaFzhk6vG6AtGp9SCG7E/D5ufJpAoiyFSXGsNcShngZ+j5VJPDvkiTmZk6sPKC2xpdqWRKKMw/4Ih9KErRdQYNlqMg/OACnfaxfMebwHrYuzP4GVcdyH5Sr9EXjQkwZ7m9xWQEnj+XhgU4kyCVOFewJLeNsA6yTZSFGu15mBPUwv5z2RXiYlBV7KYP9OM7yL1WOdqkEneFpC8ej6CqiUxIaFPFv9gysK0zCxqtEa9JC8J9PrSJSq4mJ1mAr8FisEdeNeYPJYAR2O9fsertnNAN/RoKeXFAadhqzb5HkzsM6hRyZBeKfgdNUApW4Bn2Z4G587SG2DfTvQNI10dKZjrXMO06B1tZKcYtEVA5RWAgtgAuMf68Y9c+ID4CA3PC1TKzr4Htak8Lh68rROcem4AZIoX8BU/tbDanKKyCuYbOkaVBc7PwXrYTXBoy5tURnpnIlJmx7iPp7hTUcNkERZDevU2Ov5RvX4GXCSBn09reSOYaDty9PYqi70Wz+njCklnL6Ddc34Va/3NHeyodOG4QdduEcnuQg4QYNOAvPpSJSDsejdEljTQPdJDJ0FMb/ZQRr09bwn43SPjobhk9rceIpphO4CPqdBX608IFEuAG7QoGd0f1rDD4myoNdl9ScjOjm4Bp2IdYAsInNVGh+JIhJlFUzQ6qx8pjX8cOPTv3TUACWu7MI9OkE1Z/JywDVYHopXtDvOEOmGAXoai2oUiWeBkEoEAJAoYzCf0L0aNKueXI7T13SlFCM5bo/o+I2GzlTgKKzlynMSZUdgHaygdCssh+mjHqFxnGzolnP4pi7dZ6jspEHLa5JWAH5S9voANz6Okx3d2IIB3EvtbNdeYDy2sqksiDwTy00B8/38qquzcpxhTrcM0KpYBXwv8iiwtQZ9tPJAyv+5BPgLtjoqmi/LcXqabhmgz3XpPq1yErBpvWRCDfo1YEcN+l73puU4/UG3fEC9VID6ElZecQMwvpnCUS8udZzO0I1i1JHA2E7fp0kuA/bydH/H6Q06vgVLQuz/1+n71GEScChWQPoFNz6O0zt0aws2EZiCiYZ3kyeBbTXok12+r+M4TdAtJ/TvgOO6cJ/HsD5SS2MO5q3c+DhO79KxTGiJMkKDzpQoBwEb0flI2O3ANhp0Sofv4zhORnTEACXhrg2wxnK/wXp2d5LngXU06Gsdvo/jOBnSKR/QxzHH84c6NH4572C6PW58HKdgZG6AJMocwMlkb3wmAf9IX78IzJHe31uD3pfxvRzH6QKdcEKvhlWQZ8WVacyFNei2wI+x1jcAp2vQczO8l+M4XSTTFVDy/Wyd4ZBPYDVYswTfNehEibIlsBNWo+U4TkHJ1AktUTYA7iSbnu4KbKFBiyLl4ThOi2S9BROyMT7PYzk8bnwcZxiTtRM6K7mK3TTobRmN5ThOj5K1Afo3VnaxbJPnv4H1BV8KU018CrjcjY/j9AeZJyJKlB9h3UNr8SbmPD4OeARbNS1Yav7nOE7/0IlExNOBjYEdqhy7DvhilT5QkzowD8dxepxOlWIsi23HFsG2Vo9h1fA/1aBTM7+h4ziFpJPFqCtjJRl/cEVBx3Gq0ZW+YI7jONXolh6Q4zjObLgBchwnN9wAOY6TG26AHMfJDTdAjuPkhhsgx3Fyww2Q4zi54QbIcZzccAPkOE5uuAFyHCc33AA5jpMbboAcx8kNN0CO4+SGGyDHcXLDDZDjOLnhBshxnNxwA+Q4Tm64AXIcJzf+H41ujl3qQwWXAAAAAElFTkSuQmCC\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",
    "neighborhoods_df.plot(color='green', ax=ax)\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41187131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(905465.0, 1075085.0, 112485.0, 280477.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "pois_df.plot(color='blue', ax=ax)\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b351f7c",
   "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>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>330470.010332</td>\n",
       "      <td>1.623820e+09</td>\n",
       "      <td>MULTIPOLYGON (((970217.022 145643.332, 970227....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>896344.047763</td>\n",
       "      <td>3.045213e+09</td>\n",
       "      <td>MULTIPOLYGON (((1029606.077 156073.814, 102957...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>741080.523166</td>\n",
       "      <td>1.937479e+09</td>\n",
       "      <td>MULTIPOLYGON (((1021176.479 151374.797, 102100...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>359299.096471</td>\n",
       "      <td>6.364715e+08</td>\n",
       "      <td>MULTIPOLYGON (((981219.056 188655.316, 980940....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>464392.991824</td>\n",
       "      <td>1.186925e+09</td>\n",
       "      <td>MULTIPOLYGON (((1012821.806 229228.265, 101278...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BoroCode       BoroName     Shape_Leng    Shape_Area  \\\n",
       "0         5  Staten Island  330470.010332  1.623820e+09   \n",
       "1         4         Queens  896344.047763  3.045213e+09   \n",
       "2         3       Brooklyn  741080.523166  1.937479e+09   \n",
       "3         1      Manhattan  359299.096471  6.364715e+08   \n",
       "4         2          Bronx  464392.991824  1.186925e+09   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((970217.022 145643.332, 970227....  \n",
       "1  MULTIPOLYGON (((1029606.077 156073.814, 102957...  \n",
       "2  MULTIPOLYGON (((1021176.479 151374.797, 102100...  \n",
       "3  MULTIPOLYGON (((981219.056 188655.316, 980940....  \n",
       "4  MULTIPOLYGON (((1012821.806 229228.265, 101278...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "257d8278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NwCEVrJa9XmP7TvL4sDMwV9Zwzy3AwRUuU79aQ/tOY+gT2oA8rSq+Zau8fgxwYBX7Qy9W2b7TOJYKbUCeVhXf5UClNblfBo5AtfJVUtWvgHdrsMtJHhdfUFRnAocVnP2m4PWHwF7A+qg+V8NTvPdLJz7sDI7q48AVuVcvYSnop2NFWPYFhqJ6J6rza3zCB/Ub6SRAaiIbUuVoGoBTgZ2BQ1F9A5EtgDF1CC6Kiy+dpKbGR+v2fACq07A9uzG51y/GJDyAccA/gfdjas+Jh26hDcjT2uKDfNGVJNp9EtVd8YIuaSM1ZcJcfMkzJrQBTgdSk8fFxZc8jwBPhDbCWcDnoQ3I4+JLGtU3gT+HNsNZwNjQBuRx8TUGH3qmh9GhDcjj4msME+i8ie80nudR/SS0EXlcfI3Ati/GhTbD4c7QBkRx8TUOH3qGJ1V/Axdf4xgb2gAnXek9XHyNYwzwNb7tEJKvQhsQpTXydqYBy6CW50tS5GnRQiyKqvt2thyWwElzmdQ83KjxzARmhDYiiosvDC+FNqAFmUDKhnkuvjCMDW1ACzIrtAGFuPjC4Ht+jSeuULHY8AWXEIh0xzxeUpPGrgWYCgxANTU9oPd8IbBkTN77NZa+wIDQRkRx8TUakZ8i8hSwXmhTWoyvSJl/rYuv8QwCNqO6IedMLN1hLUVepmPpD1t9frEoMC20EVFcfI2nlj2+j4DbqC7h1VzgYmAjrDrTqsAOwO+AJ0nh6l/C9AK2D21EFBdfIxHpAUjZ6zrzPWBOFde/AqwJfAY8AzyEJfG9AVgfeJDa61VkmZNCGxDFVzsbgchiwEHABdReJWdtbPhY+IU5BxtSLhQ59/+wb/oza3xWM7MmquNDGwHe8yWPSDcsjmwY8K86WlqfjvlHXsKctGfS2WH4eWDrOp7VzOwU2oA8Lr6kseIqO2ACGVRHS0PoWP1oGpaJqycdFxJmYh40Q+t4VjOzbWgD8rj4GoGN7R/Heq9a2YiO87Q1sJXMcUCPyPmngNVJUQXWlLFMaAPyuPgaxzjg9DruHwJEC7YsDTyLrVoOjJyvtIxZq7IWIn1DGwEuvkbyJpZIqdYM2T2wWvJRcS2E9abRPcOhWGXcmTU+p9npDiwX2ghw8TUOm/v9BzgXuLfGVlano1vaV3TeeO8LrAg8UOMzWoHpoQ0AF19jUZ0AvA0cSUePk++obPN9PeC1yOuh2IZ5IZvgvqPFeALYH6u9GBwXX+OZAKwArIvVhh+BuZtdSXkXsA3oKL4hwCF0jtDeCHg6DmObgK+A24FNUB2G6q1VlPdOFN9kD4GIdIiqFlkSmEjHjfJiTAd2xeo/5FkR28q4Eit1PRabAx6AlSdLTWGQBvEdlp7/IWA88HnaItjztHpxzDB0FJ5gc8BywgNzDgZbTOmJzfdmAFcDLwCroHoHIkfkrjsA+Ae1e9VkibmY8/m5qH4W2phK8J4vNCLrUV1aiX2woWp/oCeqbxdpszsgqM5B5FjgkhgsDUG+uOjRZa57F9gP1ecTtyhGfM4XnmocpgHWRXUGqh8VFR5YsK5qvt3LsYiIWsKRQrML1mvvAEzp4rpjsiY8cPGFR/V1LPSnK2YD72A+m99W2f5cVPcDFgF+Q4pqklfIYcBpwG6UTjicyfAoH3amAZv3nYM5X7+OLZx8hC3CfAJMQvXrmJ61LHAott2xdCxtNoYJwHDgLmBwwXuboPpcpztSjouvVRFZGetR9ic7CzKfAjtje5uLRM6vh+rLYUyqHRdfqyOyEhZk+8PQplTIZdh06cjIuYGoTgxkT834nK/VUX0P2DJ3vBfUlsrYC7gv8vo7qp0HpwQXXzNi6SoqR3UeqqOBtYDjSVk1nwJ60DEL2ddkdL/axZdVRHog0nnBxBZvzq2pTdvCuARYDbi+LvuS41Ng2dzPn2HxeZuHM6d2XHzZZR/gLUS263DWJvHfr6tl1SmoHgT8ANsGqXYvMklexXxh3wSuwrx4tiwowZYJXHzZ5TmgD3A/Ij8veG91ROr36VR9GtUTMIfu/9TdXjw8gzmgX4HqGajenDu/SUCbaiKTY2UHUH0bkbewIeIVuURNz2HDsOWAVYDJMT3rVUQ2AfbFXL02i6Xd2ngBuHSBf6zIptg8tTcmzMzgWw1ZRuRs4Ncl3j0N1XMSeOaiWDxco6Ml3gauBf7YISRIZG1gZeAZVL9ssE114eLLMiIbYb1dsenDs6huGvPzBEvMtCPW4/6W5DfoP8Hi8U5PU0nnOHDxZR2R8RRfYJmManx130UWxkQ+MzLk2wHLRZrE2sEH2Ib6lahmch+vHC6+LCOyOLbcXqzoylyg94J6dIUBvJU/owfQDZiNaucCkyIrAP+D9YJxrCG8DZwN3JyWiPOk8NXObLMJpasddQc2jrw+C5GPEflllc/ogerMosIDUP0Q1bOBE6gvY9p7mIA3RPWGZhceuPiyzgpl3o+mRt8Iy+95ds6fszIqnWepXkZtGdM+xSI61kD1LFRTVUMvSVx8WUWkN3BqmasOyw1NoT3xUhv1pa0vZc8iVJ+K/SpgNVRPS1O55kbh4ssu87GM1V3RD7gEkWWAGyPnjy3bushmiOyGSKUJZn8JLJb7eTJwN3A+pfcaLYViky6mVISq+pHVA/orXKegZY7nFBZX2E9hXO7cyiXa7KZwWO4eVZiqcHAZO4YrzI88b4/Ie4MVJhWx6cTgv7/Ah/d8WUb1K2BqBVcOwYaEF9Ne12F4p6tE+mAb2VfTXuVIAEXkJ4isW+SewVh0ed638t/APREbx2IBsNEFlCnAdRXY3dS4+LKKyKKI7IIFwU6t4I7rsERE+e2AIZG2uiFyJVZ886DIPd9imbQvxFzWOhaVFBkE3E/7cFOBX6BauKXxHJYMKX/+JlQrsbmpcfFll22x3qsP8EWZaw8Flkd1DPDX3LloT/QT4HA6L8T0xpITDUH1PNozooHIQGAUHUtu3Yhq5zT1JsaRwC9yZyrJUdr0uPiySxu2oPIl5upV7tp8cZA+uX+jTsg7lrjvTWA3VN9DZDAi4xD5LSJnYFmzV41cO4uuS6B1xxZh5mG9ZcvjUQ3ZRbBsZ+X2+gCuAI5C5ELgIqwnfCzy/tQS9/0q0tv9Aqsv0XneZ/wZ1eIFSCziYh7Wuz4GPFiBzU2P93zZZW8qEx7Yl+xgYG1Ux2Np+KLxb4WhOPm5mRVbsT3FPUu0/RbwY0pHV9jzVediuUevwoKAj6rQ9qbFxZddqi37fA7tw8JHgGjEwx10zNvyEpaSPj+XPKCL59lCjursou+KtNEeCX8TcAw2t1ynOvObDxdfdqlWfDfleh+AP2I13Q3Vmdj2Qp71gANp9+c8oIt2BwHfy8X5dUZ1fqSdi2nPtzKw6PUthIsvu1QjvnlYiI6h+gawaG6rIE/UW8YKrUC+kMsPumh7T8wpeu0uLRA5HDgicsbFF9oAp2ZKRTMUoxvtm+Z59qHjdsNo4I3cz1OBKblN9wvLtP09zDn6zFzMX2dEFsPmhdFETC6+0AY4NdML27y+g8qKnxwM5BdPQPVjVD9e8K7qNNqLbv4ttzd3PpU5S68KbI8tpOze6V3Vb1AdjtWLH06+Ln0GM47FiW81ZJdtyReBFBkAHAjshzkyb1Xk+r0RmYs5WI8u0eYSWO90Ue71xiWuy/MOtjAzLPd6BWw74Z5OV4psiAl0GWyvcXXM6ftMVCeVeU5T4pHszYKtKvYClqfQDayd3wDnUyp8x+L8FsWyla0AjKF4oqTJ2KLNmpjbWD5s6XVgZ1TfL2h3FUyoxRhc1CumBfBhZ7Ngq4rTsX23v+TOjsDKRud5taTwrI33UH0192ooVg2oGI9ivexPMOGNA47DSnW9X+T6JbChcbHo9OMQ2QORLbtMcy8yAJGjEHkAkXMRyWSW6g6EDqvwI4EDllD4WuEyhcsjYTzXKKxURTuiMEjh9wqfRcKTzlb4T+71DQqrlmmnh8IaBbYUO+7OXbeQQpvCYgpbK1yvMLvg2ic1N3LL6uHDziwjsibwBqrzEdkC+BCrOpQf6o0E7gROzt3xFbAM7ft91TxrNSxcaAjmC3oQ8PPc87Xg2m5Y3b81gY+xEmQzUJ2LyIpYrfW1Cp4wOXesjI3IZmD+on27sGpHVLPrqhZa/X7UcECfXG+kCpfmzv1I4f6C634c6aHyx4AK2l9C4SSFaxUeUvh57vwwhVUVxiqsX+LeNoW/R573RYfAWetNHynS680q0ysWOx4N/reo4whugB81HLBS5AM4JzesO0HhFwXX7VzwYZ2jsHCZttdVeEo7RqY/qdA7936bwnZd3P+bIiL5WeT9jWsQWVfHwOB/jxoPX3DJJvtEfv4UK5H8v8ARCxYtRFalfcsgz9V0teBi+25HYY7XD2FbEpdiG+nmjmYLOw+XuH9r4Iwi73wW+bl/yefXxjWIZHLLLJNGtzTmRbISFprzJpZG/WtEdkZ1QuTK4+gY5/ctJqRyrIM5Xc/HPF36An+mXJkw25q4m/Z0ElFeify8VAU2VMOOmBfO8TG3mzguvqxhPdfhRc5PKDizTMHrH6P6JiIbAxNR/ahIG4rIKGz74DNse2B54F0sF0tXbET7fl+UKXQs27xXmXZq4ThEvgHOpJbFpEC4+JqXxQpe5yML/g18i8ifgIs7DUNVf4NtxpfHcoJ+H9XnsDrp02iPlM9zNqpTIq8/qajt6jkNG4JfnlD7seNzvmZEpD+2ZJ9nPvBibm60KLA08AfgrDqesSzmW5qPZtiGzsKbQueo9ST9OY8of0l6cPE1J1vRMb/K3ahOzg3JHomcLxyaVobI8tge4jzgOkROBO4tcuXPgCNziz/kxL9fTc+sjGLD3tTi4mtOfoANL/OuZW9F3juY9jlYt6patXSFW2MC7g78Nybyk2iv0zcZq1y7O/ARtnq6a+69FbCeNymmJth27Picr5mwwNf3MFFtRfuXa/s8S/VDRE4H/gQ8UaSNhbEecQusJ+kNLIuJZhg2nJ2O1QScm2sj34MqsCuqz+Tayqcp3Dz3vNlYtrW4VzzzTCl/SXpw8WUZi82bg+3DgS16PIytPEZHNSMK7rwUczW7s6C9hXLndinz5CcwIRWmDxTyZcJE9qU9/cRuuR5zWeA8LAPaXlSfCqMcmSor5r6dWUVkOPB3bDFlIPANnRc8AM5C9bcVtnkP8KMKrjwWC1saWeS9iZjv5mEku7gS5Sms15uIaudtmJTic74sILIcIichsk7u9ZrYKuKyWBp3objwwHqaSinsIUvRi9LzxWUxb5tGRqkPwVZwdy13YZrwYWeasQDZzbHI8L7A7xBZC3MnewpbWGkDvsbmdZ9jw8lFgSVzP88obLbIc3bF3MJ+ha1aluv9VifZhZNKmAW8mzuWxOaivRHph2qpsmSpwoedaUbkNjr6cYLl0jwX1Ytzc7QeWLhO8bLNXbcvOa+WMVhS3UnAKsD7dB3KE5KxWILe0VjwsGF+qW3AfDLyofaeL92cjQ3j5mErjW3Y4sqfEFkG1VOwhY/qsTybSyFyHnAz5hEzCBvCzSB94puFzTWvKSouO5epBRcXX5qxXCo/whZV/oAFr+bpi0gfLOtYddhwdigm7s2wxEaXYtWDii2iJM0HWKXaNmz4uGKRa45H9eoi5zOLDzuzgsjKWK6U3rkzHwEHoPp4le1sAFyA7eNFF02mY1/GxXNv1s9UTGQfY/XhuwMvAI+j2tHf0/J87ohFb3wFPEI+Qr+JcPFlCZFh2BARLLPYYah+XuG93bDl/yuSMW4B32KFV17BYgLHA1No5drrJXDxZQ0bMi4MCKqVJMu1rQoLjF253KU1Mg1zZ7sKWwiZmdBzmgoXXzMi0gtYDNUvENkDS5Qb59bARKxc9HjgdmAcqpla7EgDvuDSLNhS+8ZYJME+QBsij1HeVawS3sHKhj0APIrqB2WudyrAe75mQeQSbCk+Dt7HNvFHAI9VPK90qsJ7vqxj87m9sOzRtfIN5tkyCuvhXqtp096pChdfFrEN8t0xwW1HdXF572GOz29haRfGAJ9VvHjjxIaLLy1YeNDCwOSiHhwi22AOy/2xuV01CyjfYUPIvwMPe6+WDlx8obE07ycDO+fOfIrIk9hm9OK5ox+W0q+anJffYdsL/wBu9X229OELLqGwnu6PWG8WJ58DlwFXofplzG07MeI9XwhENsX23laJqcX5mE/mVcCILOWubGVcfI3EQoB+A/ySeAKZR2LzuJHey2UPF1+jEFkDuAnYIIbWRgAno/pmDG05gfA0Ekkj0obI0dj+Wb3CGwtsjupuLrzs4z1fkogsA/wNi5erh1HAxcADvk3QPLj4ksD8LA/ABNOvxlbmAbcCF6D6ckyWOSnCxRc3IoOwVcdae7tZwHXAH1F9Nza7nNTh4osLC1Y9EjiX2sJ3ZgNXA+cXLd/lNB0uvjiwPJrXYkUlq+Vb4C9Yua7Pyl3sNA8uvnqwfbtTsdpwPaq8+wPg98At7vrVmrj4opiY5nRwbLao8PlYTYR1MZHNyv38a6xgSDW8l7vvDo/+bm1cfHlEdgduAL5DZCTwIRY9sDXwOJbTcsU6nvAh1kPejmptuTadpsIdq4FcnpO7iL++wItYkOo8bE7nMXPOApqz5xNZuFOt8a4ZRnzCmw7cD/wZ1c717xwnR3OJz+ZnOwB7I9ITOLDLjM52zYVY9dR6mQZcDlwEfJWVegFOOJpn2CmyOXALVjIrzwvARajemrumG9AL1W9zjs63YQsn9TASS0T7OKWi0B2nCNkWn6UV3wzYD/gpxR3FFRPH18BuWLDpRCzpUD2p0e8EzkD1tTracFqY7IjPMjUfg9WOG4TN0QZixT0axWSs7PIFqL7UwOc6TUg25nwii2AeJPsFsuB5rErQaFQnBbLBaTLSLz6RlbDVw2o3s+vlHSwV+t+ACT6Xc+Im3eKzclb3Y/W2G8E3tC+gjEZ1ToOe67Qg6RKfxcFtiM3pBgMnkGzt78nY7+BfmOhuIENlhZ1skx7xWdT3HcAPE3rC88D1wNNAH6AXVtaqm5e0ckKQjtVOkX5Y1dXlyl1aA/cA5wAveo/mpIm09HwnEL/w/g2c6S5eTlpJi/j+O8a2HgV+66Jz0k5ahp0zqc/bBOBJ4DRUR8dgkeMkTlryds6o495XgL2BYS48J0ukZdj5OdC3ynvGYV4nd3ptAieLpEV81djxGHAe8JCvXjpZJi3iq6Sy6kNYFMGzSRvjOI0gLeLr28V744FTUB3RIFscpyGEX+0U6Y5lBivkZSwB7f95li+nGUlDz7dEweu7sXQMj/iczmlm0iC+JXP/jgUORfXFgLY4TsNIg/jagD2Af/qWgdNKpGHOtziqX4c1wnEaT3jxOU6Lkhb3MsdpOVx8jhMIF5/jBMLF5ziBcPE5TiBcfI4TCBef4wTCxec4gXDxOU4gXHyOEwgXn+MEwsXnOIFw8TlOIFx8jhMIF5/jBMLF5ziBcPE5TiBcfI4TiP8PQd3irhKJY48AAAAASUVORK5CYII=\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.axis('off')\n",
    "queens = neighborhoods_df[neighborhoods_df['BoroName'] == 'Queens']\n",
    "queens.plot(color='red', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6d005f",
   "metadata": {},
   "source": [
    "## Point-in-polygon\n",
    "We look for points that lie within Queens neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74703b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(905464.6245788574, 1075092.8838439942, 112484.83531799316, 280480.45832214353)"
      ]
     },
     "execution_count": 11,
     "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",
    "neighborhoods_df.plot(ax=ax, facecolor='green')\n",
    "queens.plot(ax=ax, color='red')\n",
    "pois_df.plot(ax=ax, color='blue');\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29dab496",
   "metadata": {},
   "source": [
    "The Queens neighborhood is at index=1 so we use the within() function to determine the points that lie within the geometry at index 1 in the geo dataframe  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "328a5a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi_in_queens_mask = pois_df.within(neighborhoods_df.loc[1, 'geometry'])\n",
    "poi_in_queens_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3084e4fc",
   "metadata": {},
   "source": [
    "We have found out that only the POI with index=5 lies within the Queens neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c523411a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POINT (1009550.000 215571.000)</td>\n",
       "      <td>1225121</td>\n",
       "      <td>793979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         geometry   value1  value2\n",
       "5  POINT (1009550.000 215571.000)  1225121  793979"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi_in_queens = pois_df.loc[poi_in_queens_mask]\n",
    "poi_in_queens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b8b8ba",
   "metadata": {},
   "source": [
    "Now we can plot the POI that lies within the Queens neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b22ba45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(905464.7390380859, 1075092.8783935546, 112485.76063504723, 280480.4142594267)"
      ]
     },
     "execution_count": 14,
     "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",
    "neighborhoods_df.plot(ax=ax, facecolor='green')\n",
    "queens.plot(ax=ax, color='red')\n",
    "poi_in_queens.plot(ax=ax, color='blue');\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f347d5e",
   "metadata": {},
   "source": [
    "## Spatial joins\n",
    "One more thing we can do is to add to the neighborhoods data the information about the POIs that lie withing them. We can achieve the result by performing a spatial join between the two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef5a16f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "      <th>index_right</th>\n",
       "      <th>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (932450.000 139211.000)</td>\n",
       "      <td>1071661</td>\n",
       "      <td>793239</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>330470.010332</td>\n",
       "      <td>1.623820e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (951725.000 158301.000)</td>\n",
       "      <td>1110026</td>\n",
       "      <td>793424</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>330470.010332</td>\n",
       "      <td>1.623820e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POINT (1009550.000 215571.000)</td>\n",
       "      <td>1225121</td>\n",
       "      <td>793979</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>896344.047763</td>\n",
       "      <td>3.045213e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POINT (1028825.000 234661.000)</td>\n",
       "      <td>1263486</td>\n",
       "      <td>794164</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>464392.991824</td>\n",
       "      <td>1.186925e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         geometry   value1  value2  index_right  BoroCode  \\\n",
       "1   POINT (932450.000 139211.000)  1071661  793239            0         5   \n",
       "2   POINT (951725.000 158301.000)  1110026  793424            0         5   \n",
       "5  POINT (1009550.000 215571.000)  1225121  793979            1         4   \n",
       "6  POINT (1028825.000 234661.000)  1263486  794164            4         2   \n",
       "\n",
       "        BoroName     Shape_Leng    Shape_Area  \n",
       "1  Staten Island  330470.010332  1.623820e+09  \n",
       "2  Staten Island  330470.010332  1.623820e+09  \n",
       "5         Queens  896344.047763  3.045213e+09  \n",
       "6          Bronx  464392.991824  1.186925e+09  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_inner_df = pois_df.sjoin(neighborhoods_df, how=\"inner\")\n",
    "join_inner_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2e9b8f",
   "metadata": {},
   "source": [
    "Finally we can plot the POIs that lie within one of the neighborhoods in NYC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "192457da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(905464.7390380859, 1075092.8783935546, 112485.76063504723, 280480.4142594267)"
      ]
     },
     "execution_count": 16,
     "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",
    "neighborhoods_df.plot(ax=ax, facecolor='green')\n",
    "join_inner_df.plot(ax=ax, color='blue');\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a84f7ed",
   "metadata": {},
   "source": [
    "## Other spatial relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "efc8adc8",
   "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=\"24.95079456 60.16905356 0.0028198800000041047 0.0011548800000014126\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,120.339262)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"5.639760000008209e-05\" opacity=\"0.6\" d=\"M 24.950899,60.169158 L 24.953492,60.169158 L 24.95351,60.170104 L 24.950958,60.16999 L 24.950899,60.169158 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x2f05c1313d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "\n",
    "# Create Point objects\n",
    "p1 = Point(24.952242, 60.1696017)\n",
    "p2 = Point(24.976567, 60.1612500)\n",
    "\n",
    "# Create a Polygon\n",
    "coords = [(24.950899, 60.169158), (24.953492, 60.169158), (24.953510, 60.170104), (24.950958, 60.169990)]\n",
    "poly = Polygon(coords)\n",
    "poly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e473834a",
   "metadata": {},
   "source": [
    "We check whether the points are within the  polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a7e30c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.within(poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b789330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2.within(poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606afcfa",
   "metadata": {},
   "source": [
    "## Intersect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b99e04",
   "metadata": {},
   "source": [
    "We create two lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0fad9081",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import LineString, MultiLineString\n",
    "# Create two lines\n",
    "line_a = LineString([(0, 0), (1, 1)])\n",
    "line_b = LineString([(1, 1), (0, 2)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f652809",
   "metadata": {},
   "source": [
    "and we check whether they intersect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ab1f0663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_a.intersects(line_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49439832",
   "metadata": {},
   "source": [
    "whether they touch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4db2e671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_a.touches(line_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "88ed2431",
   "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=\"-0.08 -0.08 1.1600000000000001 2.16\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,2.0)\"><g><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.0432\" points=\"0.0,0.0 1.0,1.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.0432\" points=\"1.0,1.0 0.0,2.0\" opacity=\"0.8\" /></g></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.multilinestring.MultiLineString at 0x2f05d601fd0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_line = MultiLineString([line_a, line_b])\n",
    "multi_line"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa893a9",
   "metadata": {},
   "source": [
    "### References\n",
    "* [Intro to Python GIS](https://automating-gis-processes.github.io/CSC18/lessons/L4/point-in-polygon.html)\n",
    "* [Shapely: A Python package for manipulation and analysis of planar geometric objects](https://shapely.readthedocs.io/en/stable/index.html)\n",
    "* [ISPRA - IdroGEO - Mosaicatura Nazionale e Regionale Pericolosita' Frane e Alluvioni](https://idrogeo.isprambiente.it/app/page/open-data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08e6bf4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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