geopandas_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9727ad5d",
   "metadata": {},
   "source": [
    "# Basic Geopandas Examples\n",
    "https://geopandas.org/en/stable/index.html\n",
    "\n",
    "Frank Donnelly\n",
    "Head of GIS & Data Services, Brown University Library\n",
    "\n",
    "Dec 11, 2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfca1918",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import LineString\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82734c04",
   "metadata": {},
   "source": [
    "# Read Input and Create Geometry From Points\n",
    "Must know and plot the CRS of points as is; CRS is referenced using EPSG codes: https://epsg.io/. 4269 is NAD 83, often used by US federal govt. The most common long / lat CRS is 4326, WGS 84."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ccc9735a",
   "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>statefp</th>\n",
       "      <th>countyfp</th>\n",
       "      <th>countyns</th>\n",
       "      <th>geoid</th>\n",
       "      <th>name</th>\n",
       "      <th>namelsad</th>\n",
       "      <th>lsad</th>\n",
       "      <th>classfp</th>\n",
       "      <th>mtfcc</th>\n",
       "      <th>csafp</th>\n",
       "      <th>cbsafp</th>\n",
       "      <th>funcstat</th>\n",
       "      <th>aland</th>\n",
       "      <th>awater</th>\n",
       "      <th>intptlat</th>\n",
       "      <th>intptlon</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>009</td>\n",
       "      <td>01219782</td>\n",
       "      <td>44009</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>06</td>\n",
       "      <td>H4</td>\n",
       "      <td>G4020</td>\n",
       "      <td>148</td>\n",
       "      <td>39300</td>\n",
       "      <td>N</td>\n",
       "      <td>852827571</td>\n",
       "      <td>604769755</td>\n",
       "      <td>+41.3967920</td>\n",
       "      <td>-071.6202820</td>\n",
       "      <td>MULTIPOLYGON (((307427.031 86583.659, 307412.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>007</td>\n",
       "      <td>01219781</td>\n",
       "      <td>44007</td>\n",
       "      <td>Providence</td>\n",
       "      <td>Providence County</td>\n",
       "      <td>06</td>\n",
       "      <td>H4</td>\n",
       "      <td>G4020</td>\n",
       "      <td>148</td>\n",
       "      <td>39300</td>\n",
       "      <td>N</td>\n",
       "      <td>1060552463</td>\n",
       "      <td>67870199</td>\n",
       "      <td>+41.8697678</td>\n",
       "      <td>-071.5786246</td>\n",
       "      <td>POLYGON ((315157.916 236069.733, 315138.271 23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>001</td>\n",
       "      <td>01219777</td>\n",
       "      <td>44001</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>Bristol County</td>\n",
       "      <td>06</td>\n",
       "      <td>H4</td>\n",
       "      <td>G4020</td>\n",
       "      <td>148</td>\n",
       "      <td>39300</td>\n",
       "      <td>N</td>\n",
       "      <td>62500772</td>\n",
       "      <td>53359134</td>\n",
       "      <td>+41.7068397</td>\n",
       "      <td>-071.2866874</td>\n",
       "      <td>POLYGON ((407177.244 220459.551, 407060.698 22...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>005</td>\n",
       "      <td>01219779</td>\n",
       "      <td>44005</td>\n",
       "      <td>Newport</td>\n",
       "      <td>Newport County</td>\n",
       "      <td>06</td>\n",
       "      <td>H4</td>\n",
       "      <td>G4020</td>\n",
       "      <td>148</td>\n",
       "      <td>39300</td>\n",
       "      <td>N</td>\n",
       "      <td>265293780</td>\n",
       "      <td>547001789</td>\n",
       "      <td>+41.5010449</td>\n",
       "      <td>-071.2830626</td>\n",
       "      <td>POLYGON ((433188.859 146428.981, 433458.478 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>003</td>\n",
       "      <td>01219778</td>\n",
       "      <td>44003</td>\n",
       "      <td>Kent</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>06</td>\n",
       "      <td>H4</td>\n",
       "      <td>G4020</td>\n",
       "      <td>148</td>\n",
       "      <td>39300</td>\n",
       "      <td>N</td>\n",
       "      <td>436588773</td>\n",
       "      <td>50686111</td>\n",
       "      <td>+41.6751180</td>\n",
       "      <td>-071.5802819</td>\n",
       "      <td>POLYGON ((315828.392 188986.602, 315772.035 18...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  statefp countyfp  countyns  geoid        name           namelsad lsad  \\\n",
       "0      44      009  01219782  44009  Washington  Washington County   06   \n",
       "1      44      007  01219781  44007  Providence  Providence County   06   \n",
       "2      44      001  01219777  44001     Bristol     Bristol County   06   \n",
       "3      44      005  01219779  44005     Newport     Newport County   06   \n",
       "4      44      003  01219778  44003        Kent        Kent County   06   \n",
       "\n",
       "  classfp  mtfcc csafp cbsafp funcstat       aland     awater     intptlat  \\\n",
       "0      H4  G4020   148  39300        N   852827571  604769755  +41.3967920   \n",
       "1      H4  G4020   148  39300        N  1060552463   67870199  +41.8697678   \n",
       "2      H4  G4020   148  39300        N    62500772   53359134  +41.7068397   \n",
       "3      H4  G4020   148  39300        N   265293780  547001789  +41.5010449   \n",
       "4      H4  G4020   148  39300        N   436588773   50686111  +41.6751180   \n",
       "\n",
       "       intptlon                                           geometry  \n",
       "0  -071.6202820  MULTIPOLYGON (((307427.031 86583.659, 307412.2...  \n",
       "1  -071.5786246  POLYGON ((315157.916 236069.733, 315138.271 23...  \n",
       "2  -071.2866874  POLYGON ((407177.244 220459.551, 407060.698 22...  \n",
       "3  -071.2830626  POLYGON ((433188.859 146428.981, 433458.478 14...  \n",
       "4  -071.5802819  POLYGON ((315828.392 188986.602, 315772.035 18...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read shapefile\n",
    "county_file=os.path.join('input','ri_county_bndy.shp')\n",
    "gdf_cnty=gpd.read_file(county_file)\n",
    "gdf_cnty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "521e1e5d",
   "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>OBS_NAME</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>OBS_DATE</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>USPS</th>\n",
       "      <th>ALAND_SQMI</th>\n",
       "      <th>AWATER_SQMI</th>\n",
       "      <th>INTPTLAT</th>\n",
       "      <th>INTPTLONG</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_NUM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa</td>\n",
       "      <td>a</td>\n",
       "      <td>1/5/2020</td>\n",
       "      <td>4414140</td>\n",
       "      <td>Central Falls city</td>\n",
       "      <td>RI</td>\n",
       "      <td>1.194</td>\n",
       "      <td>0.080</td>\n",
       "      <td>41.890064</td>\n",
       "      <td>-71.393481</td>\n",
       "      <td>POINT (-71.39348 41.89006)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bravo</td>\n",
       "      <td>a</td>\n",
       "      <td>1/31/2020</td>\n",
       "      <td>4419180</td>\n",
       "      <td>Cranston city</td>\n",
       "      <td>RI</td>\n",
       "      <td>28.345</td>\n",
       "      <td>1.672</td>\n",
       "      <td>41.769734</td>\n",
       "      <td>-71.485049</td>\n",
       "      <td>POINT (-71.48505 41.76973)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>charlie</td>\n",
       "      <td>a</td>\n",
       "      <td>1/15/2020</td>\n",
       "      <td>4422960</td>\n",
       "      <td>East Providence city</td>\n",
       "      <td>RI</td>\n",
       "      <td>13.284</td>\n",
       "      <td>3.322</td>\n",
       "      <td>41.800433</td>\n",
       "      <td>-71.359258</td>\n",
       "      <td>POINT (-71.35926 41.80043)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>delta</td>\n",
       "      <td>a</td>\n",
       "      <td>2/10/2020</td>\n",
       "      <td>4449960</td>\n",
       "      <td>Newport city</td>\n",
       "      <td>RI</td>\n",
       "      <td>7.656</td>\n",
       "      <td>3.718</td>\n",
       "      <td>41.476681</td>\n",
       "      <td>-71.319627</td>\n",
       "      <td>POINT (-71.31963 41.47668)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>echo</td>\n",
       "      <td>a</td>\n",
       "      <td>2/5/2020</td>\n",
       "      <td>4454640</td>\n",
       "      <td>Pawtucket city</td>\n",
       "      <td>RI</td>\n",
       "      <td>8.667</td>\n",
       "      <td>0.297</td>\n",
       "      <td>41.874824</td>\n",
       "      <td>-71.373323</td>\n",
       "      <td>POINT (-71.37332 41.87482)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>foxtrot</td>\n",
       "      <td>a</td>\n",
       "      <td>1/15/2020</td>\n",
       "      <td>4459000</td>\n",
       "      <td>Providence city</td>\n",
       "      <td>RI</td>\n",
       "      <td>18.406</td>\n",
       "      <td>2.179</td>\n",
       "      <td>41.823056</td>\n",
       "      <td>-71.418784</td>\n",
       "      <td>POINT (-71.41878 41.82306)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>golf</td>\n",
       "      <td>b</td>\n",
       "      <td>1/16/2020</td>\n",
       "      <td>4459000</td>\n",
       "      <td>Providence city</td>\n",
       "      <td>RI</td>\n",
       "      <td>18.406</td>\n",
       "      <td>2.179</td>\n",
       "      <td>41.823056</td>\n",
       "      <td>-71.418784</td>\n",
       "      <td>POINT (-71.41878 41.82306)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hotel</td>\n",
       "      <td>b</td>\n",
       "      <td>1/31/2020</td>\n",
       "      <td>4474300</td>\n",
       "      <td>Warwick city</td>\n",
       "      <td>RI</td>\n",
       "      <td>35.001</td>\n",
       "      <td>14.750</td>\n",
       "      <td>41.703085</td>\n",
       "      <td>-71.420310</td>\n",
       "      <td>POINT (-71.42031 41.70309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>india</td>\n",
       "      <td>b</td>\n",
       "      <td>2/1/2020</td>\n",
       "      <td>4480780</td>\n",
       "      <td>Woonsocket city</td>\n",
       "      <td>RI</td>\n",
       "      <td>7.746</td>\n",
       "      <td>0.194</td>\n",
       "      <td>42.001708</td>\n",
       "      <td>-71.499947</td>\n",
       "      <td>POINT (-71.49995 42.00171)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>julliett</td>\n",
       "      <td>c</td>\n",
       "      <td>2/20/2020</td>\n",
       "      <td>4480780</td>\n",
       "      <td>Woonsocket city</td>\n",
       "      <td>RI</td>\n",
       "      <td>7.746</td>\n",
       "      <td>0.194</td>\n",
       "      <td>42.001708</td>\n",
       "      <td>-71.499947</td>\n",
       "      <td>POINT (-71.49995 42.00171)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>kilo</td>\n",
       "      <td>c</td>\n",
       "      <td>2/16/2020</td>\n",
       "      <td>0952280</td>\n",
       "      <td>New London city</td>\n",
       "      <td>CT</td>\n",
       "      <td>5.607</td>\n",
       "      <td>4.984</td>\n",
       "      <td>41.332957</td>\n",
       "      <td>-72.096176</td>\n",
       "      <td>POINT (-72.09618 41.33296)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         OBS_NAME GROUP   OBS_DATE    GEOID                  NAME USPS  \\\n",
       "OBS_NUM                                                                  \n",
       "0            alfa     a   1/5/2020  4414140    Central Falls city   RI   \n",
       "1           bravo     a  1/31/2020  4419180         Cranston city   RI   \n",
       "2         charlie     a  1/15/2020  4422960  East Providence city   RI   \n",
       "3           delta     a  2/10/2020  4449960          Newport city   RI   \n",
       "4            echo     a   2/5/2020  4454640        Pawtucket city   RI   \n",
       "5         foxtrot     a  1/15/2020  4459000       Providence city   RI   \n",
       "6            golf     b  1/16/2020  4459000       Providence city   RI   \n",
       "7           hotel     b  1/31/2020  4474300          Warwick city   RI   \n",
       "8           india     b   2/1/2020  4480780       Woonsocket city   RI   \n",
       "9        julliett     c  2/20/2020  4480780       Woonsocket city   RI   \n",
       "10           kilo     c  2/16/2020  0952280       New London city   CT   \n",
       "\n",
       "         ALAND_SQMI  AWATER_SQMI   INTPTLAT  INTPTLONG  \\\n",
       "OBS_NUM                                                  \n",
       "0             1.194        0.080  41.890064 -71.393481   \n",
       "1            28.345        1.672  41.769734 -71.485049   \n",
       "2            13.284        3.322  41.800433 -71.359258   \n",
       "3             7.656        3.718  41.476681 -71.319627   \n",
       "4             8.667        0.297  41.874824 -71.373323   \n",
       "5            18.406        2.179  41.823056 -71.418784   \n",
       "6            18.406        2.179  41.823056 -71.418784   \n",
       "7            35.001       14.750  41.703085 -71.420310   \n",
       "8             7.746        0.194  42.001708 -71.499947   \n",
       "9             7.746        0.194  42.001708 -71.499947   \n",
       "10            5.607        4.984  41.332957 -72.096176   \n",
       "\n",
       "                           geometry  \n",
       "OBS_NUM                              \n",
       "0        POINT (-71.39348 41.89006)  \n",
       "1        POINT (-71.48505 41.76973)  \n",
       "2        POINT (-71.35926 41.80043)  \n",
       "3        POINT (-71.31963 41.47668)  \n",
       "4        POINT (-71.37332 41.87482)  \n",
       "5        POINT (-71.41878 41.82306)  \n",
       "6        POINT (-71.41878 41.82306)  \n",
       "7        POINT (-71.42031 41.70309)  \n",
       "8        POINT (-71.49995 42.00171)  \n",
       "9        POINT (-71.49995 42.00171)  \n",
       "10       POINT (-72.09618 41.33296)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read CSV\n",
    "point_file=os.path.join('input','test_points.csv')\n",
    "df_pnts=pd.read_csv(point_file, index_col='OBS_NUM', delimiter=',',dtype={'GEOID':str})\n",
    "\n",
    "gdf_pnts = gpd.GeoDataFrame(df_pnts,geometry=gpd.points_from_xy(\n",
    "    df_pnts['INTPTLONG'],df_pnts['INTPTLAT']),crs = 'EPSG:4269')\n",
    "gdf_pnts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c97e42a",
   "metadata": {},
   "source": [
    "# Get CRS Info and Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82663b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:3438>\n",
       "Name: NAD83 / Rhode 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) - Rhode Island - counties of Bristol; Kent; Newport; Providence; Washington.\n",
       "- bounds: (-71.85, 41.13, -71.08, 42.02)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 Rhode Island zone (US survey foot)\n",
       "- method: Transverse Mercator\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CRS Counties\n",
    "gdf_cnty.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7a8838b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([216185.97680585,   4568.47853844, 440907.38505165, 340888.89765888])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bounds Counties\n",
    "gdf_cnty.total_bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4aaa6eef",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CRS Points\n",
    "gdf_pnts.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7965363c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-72.096176,  41.332957, -71.319627,  42.001708])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bounds Points\n",
    "gdf_pnts.total_bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7715fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:3438>\n",
       "Name: NAD83 / Rhode 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) - Rhode Island - counties of Bristol; Kent; Newport; Providence; Washington.\n",
       "- bounds: (-71.85, 41.13, -71.08, 42.02)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 Rhode Island zone (US survey foot)\n",
       "- method: Transverse Mercator\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform Point Geometry to Match County CRS\n",
    "# 3438 is the EPSG for for the RI State Plane Zone\n",
    "gdf_pnts.to_crs(3438,inplace=True)\n",
    "gdf_pnts.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55495ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([164350.33557909,  91515.54713867, 377511.59607262, 334638.78150084])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_pnts.total_bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861fbbba",
   "metadata": {},
   "source": [
    "# Generate Lines from Points Grouped by Categories and Calculate Length\n",
    "Assumes points are in proper sequence; if not, sort by sequence column first. Output measurement units are based on the CRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "779b3561",
   "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>GROUP</th>\n",
       "      <th>length_mi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>70.718183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>29.295344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>55.516051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GROUP  length_mi\n",
       "0     a  70.718183\n",
       "1     b  29.295344\n",
       "2     c  55.516051"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines = gdf_pnts.groupby('GROUP')['geometry'].apply(lambda x: LineString(x.tolist()))\n",
    "gdf_lines = gpd.GeoDataFrame(lines, geometry='geometry',crs = 'EPSG:3438').reset_index()\n",
    "gdf_lines['length_mi']=(gdf_lines.length)/5280\n",
    "gdf_lines.loc[:,['GROUP','length_mi']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2655fde",
   "metadata": {},
   "source": [
    "# Spatial Join Points and Polygons\n",
    "Keep all points on left, null values for non-matching counties on right. Take subset of columns from right, must always include geom for spatial joins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88aef431",
   "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>OBS_NAME</th>\n",
       "      <th>OBS_DATE</th>\n",
       "      <th>COUNTY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_NUM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa</td>\n",
       "      <td>1/5/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bravo</td>\n",
       "      <td>1/31/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>charlie</td>\n",
       "      <td>1/15/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>delta</td>\n",
       "      <td>2/10/2020</td>\n",
       "      <td>Newport County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>echo</td>\n",
       "      <td>2/5/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>foxtrot</td>\n",
       "      <td>1/15/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>golf</td>\n",
       "      <td>1/16/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hotel</td>\n",
       "      <td>1/31/2020</td>\n",
       "      <td>Kent County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>india</td>\n",
       "      <td>2/1/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>julliett</td>\n",
       "      <td>2/20/2020</td>\n",
       "      <td>Providence County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>kilo</td>\n",
       "      <td>2/16/2020</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         OBS_NAME   OBS_DATE             COUNTY\n",
       "OBS_NUM                                        \n",
       "0            alfa   1/5/2020  Providence County\n",
       "1           bravo  1/31/2020  Providence County\n",
       "2         charlie  1/15/2020  Providence County\n",
       "3           delta  2/10/2020     Newport County\n",
       "4            echo   2/5/2020  Providence County\n",
       "5         foxtrot  1/15/2020  Providence County\n",
       "6            golf  1/16/2020  Providence County\n",
       "7           hotel  1/31/2020        Kent County\n",
       "8           india   2/1/2020  Providence County\n",
       "9        julliett  2/20/2020  Providence County\n",
       "10           kilo  2/16/2020                NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_pnts_wcnty=gpd.sjoin(gdf_pnts, gdf_cnty[['geoid','namelsad','geometry']],\n",
    "                         how='left', predicate='intersects')\n",
    "gdf_pnts_wcnty.rename(columns={'geoid': 'COUNTY_ID', 'namelsad': 'COUNTY'}, inplace=True)\n",
    "gdf_pnts_wcnty.loc[:,['OBS_NAME','OBS_DATE','COUNTY']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc24835",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "292f89fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xticks(rotation=315)\n",
    "gdf_cnty.plot(ax=ax, color='yellow', edgecolor='grey')\n",
    "gdf_pnts.plot(ax=ax,color='black', markersize=5)\n",
    "gdf_lines.plot(ax=ax, column=\"GROUP\", legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fde4d5d",
   "metadata": {},
   "source": [
    "# WRITE OUTPUT\n",
    "Shapefile format cannot handle column names > 10 characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "663f13a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_25967/1840073267.py:4: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  gdf_pnts_wcnty.to_file(out_points)\n"
     ]
    }
   ],
   "source": [
    "out_points=os.path.join('output','test_points_counties.shp')\n",
    "out_lines=os.path.join('output','test_lines.shp')\n",
    "\n",
    "gdf_pnts_wcnty.to_file(out_points)\n",
    "gdf_lines.to_file(out_lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0ea488d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}