Merge pull request #13 from olincollege/SV-22-traffic-volume

Sv 22 traffic volume

docs/source/explanations/datasets.rst

@@ -15,6 +15,8 @@ Traffic Dataset
 
 Since our project is focused on pedestrian safety at nighttime on crosswalks, we need a dataset that contains information about the volume of traffic. MassDOT provides a convenient dataset that includes average annual daily traffic (AADT) counts for most roads in Massachusetts. The counts will be used to inform the risk of a pedestrian being hit by a car at a given crosswalk. The dataset can be viewed at the `MassDOT Traffic Dataset <https://www.arcgis.com/apps/mapviewer/index.html?url=https://gis.massdot.state.ma.us/arcgis/rest/services/Roads/VMT/FeatureServer/10&source=sd>`_.
 
+To estimate the danger of a road, we also need a dataset with information about speed limits per road. MassDOT provides a database (updated yearly) with speed limits for most roads in Massachusetts. This can be viewed and downloaded at `this link <https://geodot-massdot.hub.arcgis.com/search?groupIds=362562c527bb404884dd1608b4bfdb62>`
+
 Population Density Dataset
 --------------------------
 
@@ -62,4 +64,4 @@ We have chosen to use the pedestrian accidents dataset from the Vision Zero init
 
     Vision Zero Network Community Map (February 2024)
 
-Vision Zero initiatives are a nationwide effort to eliminate traffic fatalities and severe injuries. Growing number of cities have contributed to this effort and collected data, which will help this project be applicable outside of Boston as well.
+Vision Zero initiatives are a nationwide effort to eliminate traffic fatalities and severe injuries. Growing number of cities have contributed to this effort and collected data, which will help this project be applicable outside of Boston as well.

pyproject.toml

@@ -18,4 +18,3 @@ line-length = 88
 [build-system]
 requires = ["setuptools>=70.0.0"]
 build-backend = "setuptools.build_meta"
-

src/night_light/utils/__init__.py

@@ -5,7 +5,7 @@
     LAYER_HIGHLIGHT_STYLE_DICT,
     open_html_file,
 )
-from .query_geojson import fetch_geojson_data, save_geojson
+from .query_geojson import fetch_geojson_data, save_geojson, save_geodatabase_to_geojson
 
 __all__ = [
     create_folium_map,
@@ -15,4 +15,5 @@
     open_html_file,
     fetch_geojson_data,
     save_geojson,
+    save_geodatabase_to_geojson,
 ]

src/night_light/utils/query_geojson.py

@@ -25,9 +25,7 @@ def fetch_geojson_data(url: str, params: dict) -> gpd.GeoDataFrame:
     response = requests.get(url, params=params)
     response.raise_for_status()
     try:
-        gdf = gpd.GeoDataFrame.from_features(
-            response.json()["features"], crs="EPSG:4326"
-        )
+        gdf = gpd.GeoDataFrame.from_features(response.json()["features"], crs="EPSG:4326")
         return gdf
     except KeyError:
         raise ValueError("Invalid GeoJSON data")
@@ -45,3 +43,25 @@ def save_geojson(gdf: gpd.GeoDataFrame, filename: str):
         None
     """
     gdf.to_file(filename, driver="GeoJSON")
+
+
+def save_geodatabase_to_geojson(
+    path_gdf: str, filename: str, params: dict = {}
+) -> gpd.GeoDataFrame:
+    """
+    Given the path to a GeoDataFrame, filter it and save it as a GeoJSON file
+
+    Args:
+        path_gdf (str): The file path to the GeoDataFrame location
+        filename (str): The file path where the GeoJSON will be saved
+        params (dict): Dictionary representing parameters to filter geodataframe by
+
+    Returns:
+        gdf: A GeoDataFrame containing the geometries and properties from
+        the fetched GeoJSON data
+    """
+    gdf = gpd.read_file(filename=path_gdf)
+    for key, value in params.items():
+        gdf_filtered = gdf[gdf[key] == value]
+    gdf_filtered.to_file(filename, driver="GeoJSON")
+    return gdf_filtered

tests/conftest.py

@@ -1,12 +1,15 @@
 from night_light.utils import query_geojson
+from night_light.road_characteristics import traffic_speed_data
 
 import pytest
 import asyncio
 import aiohttp
 import geopandas as gpd
 
 MA_CROSSWALK_URL = "https://gis.massdot.state.ma.us/arcgis/rest/services/Assets/Crosswalk_Poly/FeatureServer/0/query"
-MA_TRAFFIC_URL = "https://gis.massdot.state.ma.us/arcgis/rest/services/Roads/VMT/FeatureServer/10/query"
+MA_TRAFFIC_URL = (
+    "https://gis.massdot.state.ma.us/arcgis/rest/services/Roads/VMT/FeatureServer/10/query"
+)
 MA_MUNICIPALITIES_URL = "https://arcgisserver.digital.mass.gov/arcgisserver/rest/services/AGOL/Towns_survey_polym/FeatureServer/0/query"
 BOSTON_STREETLIGHTS_URL = "https://services.evari.io/evari/rest/services/Boston/Boston_Read_Only/MapServer/0/query"
 
@@ -20,9 +23,7 @@
 MA_MUNICIPALITIES = query_geojson.fetch_geojson_data(
     url=MA_MUNICIPALITIES_URL, params=MUNICIPALITIES_MA_PARAMS
 )
-BOSTON_TOWN_ID = MA_MUNICIPALITIES[MA_MUNICIPALITIES["TOWN"] == "BOSTON"][
-    "TOWN_ID"
-].values[0]
+BOSTON_TOWN_ID = MA_MUNICIPALITIES[MA_MUNICIPALITIES["TOWN"] == "BOSTON"]["TOWN_ID"].values[0]
 
 CROSSWALK_BOSTON_PARAMS = {
     "where": "TOWN='BOSTON'",
@@ -46,18 +47,25 @@
 }
 BOSTON_CENTER_COORD = [42.3601, -71.0589]
 
+ROAD_INVENTORY_DATA = "src/night_light/road_characteristics/RoadInventory2023.gdb"
+
 
 @pytest.fixture
 def boston_crosswalk():
-    return query_geojson.fetch_geojson_data(
-        url=MA_CROSSWALK_URL, params=CROSSWALK_BOSTON_PARAMS
-    )
+    return query_geojson.fetch_geojson_data(url=MA_CROSSWALK_URL, params=CROSSWALK_BOSTON_PARAMS)
 
 
 @pytest.fixture
 def boston_traffic():
-    return query_geojson.fetch_geojson_data(
-        url=MA_TRAFFIC_URL, params=TRAFFIC_BOSTON_PARAMS
+    return query_geojson.fetch_geojson_data(url=MA_TRAFFIC_URL, params=TRAFFIC_BOSTON_PARAMS)
+
+
+@pytest.fixture
+def boston_traffic_speed():
+    return query_geojson.save_geodatabase_to_geojson(
+        path_gdf=ROAD_INVENTORY_DATA,
+        filename="tests/test_road_speeds.geojson",
+        params={"City": BOSTON_TOWN_ID},
     )
 
 

tests/test_traffic_speed_data.py

@@ -0,0 +1,18 @@
+import pytest
+import os
+from night_light.utils import query_geojson
+
+
+def test_save_boston_traffic_speed_geojson(boston_traffic_speed):
+    """
+    Test saving Boston traffic speed data to a geojson
+    """
+    geojson_name = "tests/test_road_speeds.geojson"
+    saved_gdf = query_geojson.gpd.read_file(geojson_name)
+
+    assert boston_traffic_speed.crs == saved_gdf.crs
+    assert list(boston_traffic_speed.columns) == list(saved_gdf.columns)
+    assert boston_traffic_speed.shape == saved_gdf.shape
+
+    os.remove(geojson_name)
+    assert not os.path.exists(geojson_name)