Repository: DataKind-DC/rcp2

Issue:
Calculate absolute and driving distance from GEOIDs to closest one or more fire stations
Data Sources - FD Locations, GEOID geographies

- [ ] Assign GEOID (Full FIPS) to FD Locations File - Use FD Location file's Lat Longs to identify Census Block Group Info (GEOID) for all Fire Stations, update master file.

- [ ] Determine distance between FD and Tracts - Determine as the crow flies distance to nearest fire station from census tract centroid, Document distance in output. Denote if paid or volunteer, if located in same county, and FDID

- [ ] Determine distance between FD and Block Group - Determine as the crow flies distance to nearest fire station from census block group centroid, Document distance in output. Denote if paid or volunteer, if located in same county, and FDID

- [ ] Determine Avg Drive time from FD to Census Tract Boundary - Use FD location data to determine closest possible drive distance to ingress at census tract level. Denote if paid or volunteer, if located in same county, and FDID

- [ ] Determine Avg Drive time from FD to Census Block Group Boundary - Use FD location data to determine closest possible drive distance to ingress at census block group level. Denote if paid or volunteer, if located in same county, and FDID



Discussion:

Patch:
diff --git a/notebooks/2020-06-28_fire_station_geoids.ipynb b/notebooks/2020-06-28_fire_station_geoids.ipynb
new file mode 100644
index 0000000..aed5c7a
--- /dev/null
+++ b/notebooks/2020-06-28_fire_station_geoids.ipynb
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring Fire Station GEOIDs.\n",
+    "\n",
+    "This notebook confirms that fire station *GEOID10* values look reasonable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from src.data import raw\n",
+    "from src.data import fire_stations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read and prep source data.\n",
+    "\n",
+    "First, we read the fire station data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map FIPS codes to state abbreviations (e.g. {01: AL, ...})\n",
+    "fips_map = {v: k for k, v in raw.STATES.items()}\n",
+    "\n",
+    "# Read fire station data.\n",
+    "stations = fire_stations.read()\n",
+    "\n",
+    "# Extract FIPS code and state name from GEOID10.\n",
+    "stations[\"geoid10_fips\"] = stations.GEOID10.str[:2]\n",
+    "stations[\"geoid10_state\"] = stations.geoid10_fips.map(fips_map)\n",
+    "\n",
+    "# Subset to just the stations with DC GEOIDs.\n",
+    "dc_stations = stations.loc[stations.geoid10_state == \"DC\", :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we read some DC Census tract polygons for comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def has_station(x, y):\n",
+    "    \"\"\"Identify census tracts with fire stations.\"\"\"\n",
+    "    mapping = {True: \"Station\", False: \"No Station\"}\n",
+    "    return x.GEOID10.isin(y.GEOID10).map(mapping)\n",
+    "\n",
+    "# Read DC census tracts.\n",
+    "dc_tracts = raw.read_shapefiles([\"DC\"])\n",
+    "\n",
+    "# Identify census tracts that supposedly have stations.\n",
+    "dc_tracts[\"has_station\"] = has_station(dc_tracts, stations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get a feel for *GEOID10* values.\n",
+    "\n",
+    "Looks like the GEOID values are reasonable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# All stations have a GEOID10 value.\n",
+    "stations.GEOID10.notna().all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CA    3209\n",
+       "NY    2851\n",
+       "TX    2714\n",
+       "PA    2708\n",
+       "NC    1947\n",
+       "Name: geoid10_state, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# States with the most fire stations. Seems plausible.\n",
+    "(stations.geoid10_state.value_counts().head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize DC as a spot check.\n",
+    "\n",
+    "And the fire stations seem to match up with the right Census tracts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(104.64080411403188, 0.5, 'Latitude')"
+      ]
+     },
+     "execution_count": 7,
+     "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": [
+    "# Point locations of fire stations.\n",
+    "points = (fire_stations.calculate_points(dc_stations.Longitude, \n",
+    "                                         dc_stations.Latitude,\n",
+    "                                         crs=dc_tracts.crs))\n",
+    "\n",
+    "# Set up the plot.\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set_aspect(\"equal\")\n",
+    "\n",
+    "# Plot the census tracts.\n",
+    "dc_tracts.plot(ax=ax, column=\"has_station\", categorical=True, legend=True)\n",
+    "\n",
+    "# Plot fire station locations.\n",
+    "points.plot(ax=ax, color=\"red\", markersize=20)\n",
+    "\n",
+    "# Adjust plot title and labels.\n",
+    "ax.set_title(\"DC Fire Stations and Census Tracts\")\n",
+    "ax.set_xlabel(\"Longitude\")\n",
+    "ax.set_ylabel(\"Latitude\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/setup.py b/setup.py
index e311731..f57b0db 100644
--- a/setup.py
+++ b/setup.py
@@ -7,6 +7,11 @@ setup(
     description='Project objective',
     author='DataKindDC',
     license='MIT',
+    install_requires=[
+        "geopandas>=0.8.0",
+        "pandas>=1.0.5",
+        "Shapely>=1.7.0",
+    ],
     extras_require={
         "tests": "pytest>=5.4.3",
     },
diff --git a/src/data/fire_stations.py b/src/data/fire_stations.py
new file mode 100644
index 0000000..62214f9
--- /dev/null
+++ b/src/data/fire_stations.py
@@ -0,0 +1,109 @@
+"""Data processing for the fire stations data.
+
+The processed fire station data may be useful for analyzing the locations and
+high-level attributes for fire stations across the US.
+
+This module includes several top-level functions:
+
+- :func:`src.data.fire_stations.process` processes raw fire station data.
+- :func:`src.data.fire_stations.read` reads processed fire station data.
+
+Run this module as a script to process fire station data from raw inputs and
+save the processed data to the default data directory. ::
+
+  $ python -m src.data.fire_stations
+
+"""
+import geopandas
+import pandas as pd
+import shapely
+from src import utils
+from src.data import raw
+
+
+# Path to processed fire station data.
+PATH = utils.DATA["processed"] / "fire-stations.csv"
+
+
+def process():
+    """Process the raw fire stations data.
+
+    This function reads raw fire station data and appends column *GEOID10*,
+    which identifies the 2010 Census tract that each station belongs to. The
+    calculation occurs in three steps.
+
+    1. Identify fire station locations from latitude and longitude.
+    2. Identify 2010 Census tract boundaries from raw shape files.
+    3. Perform a spatial left-join, matching stations to surrounding tracts.
+
+    The resulting pandas.DataFrame has the same row count as the raw data.
+
+    Returns:
+        pandas.DataFrame: Processed fire station data.
+    """
+    stations = raw.read_fire_stations()
+    tracts = raw.read_shapefiles()[["GEOID10", "geometry"]]
+    
+    # Create points for the lon-lat pair at each fire station.
+    geometry = calculate_points(stations.Longitude,
+                                stations.Latitude,
+                                crs=tracts.crs)
+    
+    # Append the points to the fire station data.
+    stations = geopandas.GeoDataFrame(stations, geometry=geometry)
+
+    # Find the Tract geoid for each station.
+    result = geopandas.sjoin(stations, tracts, op="within", how="left")
+
+    # Return a pandas.DataFrame without geometries.
+    return pd.DataFrame(result).drop("geometry", axis=1)
+
+
+def read(process_if_needed=False):
+    """Read processed fire stations data.
+
+    If `process_if_needed` is True and the processed fire station data are not
+    found on disk, then this function will process the fire stations data from
+    raw data and return the result.
+
+    Args:
+        process_if_needed (bool): Process the data if data not found.
+
+    Returns:
+        pandas.DataFrame: Processed fire station data.
+    """
+    dtype = {"GEOID10": str}
+    if process_if_needed:
+        try:
+            result = pd.read_csv(PATH, dtype=dtype)
+        except FileNotFoundError:
+            result = process()
+    else:
+        result = pd.read_csv(PATH, dtype=dtype)
+    return result
+
+
+def calculate_points(long, lat, *args, **kwargs):
+    """Calculate points in space from longitude and latitude.
+
+    Args:
+        long (array-like): Longitude values.
+        lat (array-like): Latitude values.
+        args: Positial arguments passed to geopandas.GeoSeries.
+        kwargs: Keyword arguments passed to geopandas.GeoSeries.
+
+    Returns:
+        geopandas.GeoSeries: Points for the coordinates given.
+    """
+    geometry = []
+    for coord in zip(long, lat):
+        geometry.append(shapely.geometry.Point(coord))
+    return geopandas.GeoSeries(geometry, *args, **kwargs)
+
+
+if __name__ == "__main__":
+    # Process the fire stations data.
+    stations = process()
+    
+    # Write processed data to disk.
+    stations.to_csv(PATH)
\ No newline at end of file
diff --git a/src/data/raw.py b/src/data/raw.py
new file mode 100644
index 0000000..f6cb2b7
--- /dev/null
+++ b/src/data/raw.py
@@ -0,0 +1,151 @@
+"""Utilities for working with raw data.
+
+The following top-level functions can be useful for reading raw data.
+
+- :func:`src.data.raw.read_shapefiles` reads 2010 Census tract shapefiles.
+- :func:`src.data.raw.read_fire_stations` reads fire station data.
+
+"""
+import geopandas
+import pandas as pd
+from src import utils
+
+
+# Map state names to state FIPS codes.
+STATES = {
+    'AL': '01',
+    'AK': '02',
+    'AZ': '04',
+    'AR': '05',
+    'CA': '06',
+    'CO': '08',
+    'CT': '09',
+    'DE': '10',
+    'FL': '12',
+    'GA': '13',
+    'HI': '15',
+    'ID': '16',
+    'IL': '17',
+    'IN': '18',
+    'IA': '19',
+    'KS': '20',
+    'KY': '21',
+    'LA': '22',
+    'ME': '23',
+    'MD': '24',
+    'MA': '25',
+    'MI': '26',
+    'MN': '27',
+    'MS': '28',
+    'MO': '29',
+    'MT': '30',
+    'NE': '31',
+    'NV': '32',
+    'NH': '33',
+    'NJ': '34',
+    'NM': '35',
+    'NY': '36',
+    'NC': '37',
+    'ND': '38',
+    'OH': '39',
+    'OK': '40',
+    'OR': '41',
+    'PA': '42',
+    'RI': '44',
+    'SC': '45',
+    'SD': '46',
+    'TN': '47',
+    'TX': '48',
+    'UT': '49',
+    'VT': '50',
+    'VA': '51',
+    'WA': '53',
+    'WV': '54',
+    'WI': '55',
+    'WY': '56',
+    'AS': '60',
+    'GU': '66',
+    'MP': '69',
+    'PR': '72',
+    'VI': '78',
+    'DC': '11',
+}
+
+
+class BadPathError(Exception):
+    """An error for invalid paths."""
+    pass
+
+
+def read_shapefiles(states=None, fips=None, glob=None):
+    """Read raw 2010 Census tract shapefiles.
+
+    The project raw data includes 56 shapefiles with 2010 Census tract
+    polygons. Each file corresponds to a US state, territory, etc.
+
+    This function facilitates reading and stacking any number of these
+    shapefiles into a single geopandas.GeoDataFrame.
+
+    The caller can specify the shapefiles to use with a list of two-letter
+    state abbreviations, a list of two-digit FIPS codes, or a glob for the file
+    names of interest. The caller can only use one of these filters in a call.
+
+    Args:
+        states (list): Two-letter state abbreviation strings.
+        fips (list): Two-digit state FIPS code strings.
+        glob (str): A glob expression (e.g., "*.shp").
+        
+    Returns:
+        geopandas.GeoDataFrame: Shapes for the states of interest.
+    """
+    # Directory with raw shapefiles.
+    datadir = utils.DATA["shapefiles-census"]
+    
+    # Template for shapefile names.
+    fname = "tl_2010_{code}_tract10.shp"
+    
+    # Count the nmber of filter arguments.
+    n_filters = sum([arg != None for arg in (states, fips, glob)])
+
+    # Handle calls with too many arguments.
+    if n_filters > 1:
+        raise ValueError("Only one of [states/fips/glob] may be selected.")
+    
+    # Identify the paths to read from, defaulting to all shapefiles.
+    elif n_filters == 0:
+        paths = sorted(datadir.glob("*.shp"))
+        
+    elif fips:
+        paths = [datadir / fname.format(code=code) for code in fips]
+        
+    elif states:
+        paths = []
+        for state in states:
+            code = STATES[state]
+            paths.append(datadir / fname.format(code=code))
+            
+    elif glob:
+        paths = sorted(datadir.glob(glob))
+
+    # Handle bad path names and non-shapefiles. 
+    for path in paths:
+        if not path.exists():
+            raise BadPathError(f"File {path} not found")
+        if not path.suffix == ".shp":
+            raise BadPathError(f"File {path} doesn't have extension '.shp'")
+        
+    # Read the data.
+    chunks = []
+    for path in paths:
+        chunks.append(geopandas.read_file(path))
+    return pd.concat(chunks)    
+
+
+def read_fire_stations():
+    """Read raw fire station data.
+
+    Returns:
+        pandas.DataFrame: The fire station data.
+    """
+    path = utils.DATA["master"] / "Fire Station Location Data.csv"
+    return pd.read_csv(path)


Test patch:
diff --git a/src/tests/test_fire_stations.py b/src/tests/test_fire_stations.py
new file mode 100644
index 0000000..e18b8d4
--- /dev/null
+++ b/src/tests/test_fire_stations.py
@@ -0,0 +1,13 @@
+import geopandas
+import numpy as np
+import shapely
+from src.data import fire_stations
+
+
+def test_calculate_points():
+    lon = np.random.uniform(0, 90, size=2)
+    lat = np.random.uniform(-180, 180, size=2)
+    result = fire_stations.calculate_points(lon, lat)
+    assert type(result) == geopandas.GeoSeries
+    assert result.shape == (2,)
+    assert type(result.iloc[0]) == shapely.geometry.point.Point
\ No newline at end of file
diff --git a/src/tests/test_raw.py b/src/tests/test_raw.py
new file mode 100644
index 0000000..d88082a
--- /dev/null
+++ b/src/tests/test_raw.py
@@ -0,0 +1,33 @@
+import geopandas
+import pytest
+from src.data import raw
+
+
+def test_read_shapefiles_states():
+    df = raw.read_shapefiles(states=["AL", "AK"])
+    assert type(df) == geopandas.GeoDataFrame
+    assert df.shape == (1348, 13)
+    assert df.STATEFP10.isin(["01", "02"]).all()
+
+    
+def test_read_shapefiles_fips():
+    df = raw.read_shapefiles(fips=["01", "02"])
+    assert type(df) == geopandas.GeoDataFrame
+    assert df.shape == (1348, 13)
+    assert df.STATEFP10.isin(["01", "02"]).all()
+
+    
+def test_read_shapefiles_glob():
+    df = raw.read_shapefiles(glob="tl_2010_0[12]*.shp")
+    assert type(df) == geopandas.GeoDataFrame
+    assert df.shape == (1348, 13)
+    assert df.STATEFP10.isin(["01", "02"]).all()
+
+    
+def test_read_shapefiles_errors():
+    with pytest.raises(ValueError):
+        raw.read_shapefiles(states=["AL"], fips=["01"])
+    with pytest.raises(raw.BadPathError):
+        raw.read_shapefiles(fips=["ZZ"])
+    with pytest.raises(raw.BadPathError):
+        raw.read_shapefiles(glob="*")
\ No newline at end of file