Number of Hospitals Accessible within 25 minutes in Turkana County
Data Source:
- Population grid 500meter by 500 meter -aggregated from WorldPop
- Hospital facilities - OSM
- Transport network - OSM pbf file, extracted using BBBIKE
For more about the methodology-contact admin@mtaawetu.com
In [ ]:
# Interactive map showing number of hospitals accessible within 25 minutes (access_Hospitals)
join.explore(column="access_Hospitals", scheme='natural_breaks',cmap="plasma")
/usr/local/lib/python3.10/dist-packages/mapclassify/classifiers.py:687: UserWarning: Not enough unique values in array to form 5 classes. Setting k to 3. self._classify()
Out[ ]:
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Hospitals in Turkana
In [ ]:
#Interactive map of Turkana
Hospitals.explore()
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Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]:
# Plot interactive map of population
origins.explore(column='Population', scheme='natural_breaks',cmap="plasma")
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook
Accessibility Modelling
Using the R5Py library to calculate the travel time matrix and analyze the accessibility of urban amenities The R5Py library, is a Python wrapper for the R5 routing engine. R5 is a multimodal routing engine that can be used to calculate travel times between locations in a city using different modes of transport, such as walking, cycling, and public transport. It is open-source, efficient and use real-time data and OpenStreetMap to provide accurate travel time estimates.
R5Py calculates the travel time matrix between origins and destinations.
Step 1: Install and import packages¶
In [ ]:
# Install the packages used in the first part of the tutorial
!pip install networkx
!pip install osmnx
!pip install rasterio
Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (3.3) Requirement already satisfied: osmnx in /usr/local/lib/python3.10/dist-packages (1.9.4) Requirement already satisfied: geopandas<0.15,>=0.12 in /usr/local/lib/python3.10/dist-packages (from osmnx) (0.14.4) Requirement already satisfied: networkx<3.4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from osmnx) (3.3) Requirement already satisfied: numpy<1.27,>=1.20 in /usr/local/lib/python3.10/dist-packages (from osmnx) (1.26.4) Requirement already satisfied: pandas<2.3,>=1.1 in /usr/local/lib/python3.10/dist-packages (from osmnx) (2.1.4) Requirement already satisfied: requests<2.33,>=2.27 in /usr/local/lib/python3.10/dist-packages (from osmnx) (2.32.3) Requirement already satisfied: shapely<2.1,>=2.0 in /usr/local/lib/python3.10/dist-packages (from osmnx) (2.0.6) Requirement already satisfied: fiona>=1.8.21 in /usr/local/lib/python3.10/dist-packages (from geopandas<0.15,>=0.12->osmnx) (1.9.6) Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from geopandas<0.15,>=0.12->osmnx) (24.1) Requirement already satisfied: pyproj>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from geopandas<0.15,>=0.12->osmnx) (3.6.1) Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas<2.3,>=1.1->osmnx) (2.8.2) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2.3,>=1.1->osmnx) (2024.1) Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2.3,>=1.1->osmnx) (2024.1) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<2.33,>=2.27->osmnx) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<2.33,>=2.27->osmnx) (3.8) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<2.33,>=2.27->osmnx) (2.0.7) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<2.33,>=2.27->osmnx) (2024.8.30) Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas<0.15,>=0.12->osmnx) (24.2.0) Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas<0.15,>=0.12->osmnx) (8.1.7) Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas<0.15,>=0.12->osmnx) (1.1.1) Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas<0.15,>=0.12->osmnx) (0.7.2) Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas<0.15,>=0.12->osmnx) (1.16.0) Requirement already satisfied: rasterio in /usr/local/lib/python3.10/dist-packages (1.3.11) Requirement already satisfied: affine in /usr/local/lib/python3.10/dist-packages (from rasterio) (2.4.0) Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio) (24.2.0) Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from rasterio) (2024.8.30) Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.10/dist-packages (from rasterio) (8.1.7) Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from rasterio) (0.7.2) Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.26.4) Requirement already satisfied: snuggs>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.4.7) Requirement already satisfied: click-plugins in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.1.1) Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from rasterio) (71.0.4) Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.10/dist-packages (from snuggs>=1.4.1->rasterio) (3.1.4)
In [ ]:
# Install the packages used in the third part of the tutorial
!pip install r5py
!pip install mapclassify
!pip install folium
Requirement already satisfied: r5py in /usr/local/lib/python3.10/dist-packages (0.1.2) Requirement already satisfied: ConfigArgParse in /usr/local/lib/python3.10/dist-packages (from r5py) (1.7) Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from r5py) (3.15.4) Requirement already satisfied: fiona in /usr/local/lib/python3.10/dist-packages (from r5py) (1.9.6) Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (from r5py) (0.14.4) Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from r5py) (6.4.4) Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from r5py) (1.4.2) Requirement already satisfied: jpype1 in /usr/local/lib/python3.10/dist-packages (from r5py) (1.5.0) Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from r5py) (1.26.4) Requirement already satisfied: pandas>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from r5py) (2.1.4) Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from r5py) (5.9.5) Requirement already satisfied: pyproj in /usr/local/lib/python3.10/dist-packages (from r5py) (3.6.1) Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from r5py) (2.32.3) Requirement already satisfied: shapely>=2.0 in /usr/local/lib/python3.10/dist-packages (from r5py) (2.0.6) Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.1.0->r5py) (2.8.2) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.1.0->r5py) (2024.1) Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.1.0->r5py) (2024.1) Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona->r5py) (24.2.0) Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from fiona->r5py) (2024.8.30) Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona->r5py) (8.1.7) Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.10/dist-packages (from fiona->r5py) (1.1.1) Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from fiona->r5py) (0.7.2) Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fiona->r5py) (1.16.0) Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from geopandas->r5py) (24.1) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->r5py) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->r5py) (3.8) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->r5py) (2.0.7) Requirement already satisfied: mapclassify in /usr/local/lib/python3.10/dist-packages (2.8.0) Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (3.3) Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.26.4) Requirement already satisfied: pandas!=1.5.0,>=1.4 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (2.1.4) Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.3.2) Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.13.1) Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2.8.2) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.1) Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.1) Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (1.4.2) Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (3.5.0) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas!=1.5.0,>=1.4->mapclassify) (1.16.0) Requirement already satisfied: folium in /usr/local/lib/python3.10/dist-packages (0.17.0) Requirement already satisfied: branca>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from folium) (0.7.2) Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from folium) (3.1.4) Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from folium) (1.26.4) Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from folium) (2.32.3) Requirement already satisfied: xyzservices in /usr/local/lib/python3.10/dist-packages (from folium) (2024.6.0) Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.9->folium) (2.1.5) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.8) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2.0.7) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2024.8.30)
Step 2: Install Java 21¶
In [ ]:
#Java 21
!java -version
!apt-get update
!apt-get install -y openjdk-21-jdk
import os
os.environ['JAVA_HOME'] = '/usr/lib/jvm/java-21-openjdk-amd64'
os.environ['PATH'] = f"{os.environ['JAVA_HOME']}/bin:{os.environ['PATH']}"
openjdk version "21.0.4" 2024-07-16 OpenJDK Runtime Environment (build 21.0.4+7-Ubuntu-1ubuntu222.04) OpenJDK 64-Bit Server VM (build 21.0.4+7-Ubuntu-1ubuntu222.04, mixed mode, sharing) Hit:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease Ign:4 https://r2u.stat.illinois.edu/ubuntu jammy InRelease Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] Hit:6 https://r2u.stat.illinois.edu/ubuntu jammy Release Hit:7 http://security.ubuntu.com/ubuntu jammy-security InRelease Hit:8 http://archive.ubuntu.com/ubuntu jammy-backports InRelease Hit:9 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease Hit:10 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease Hit:12 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease Fetched 128 kB in 3s (49.8 kB/s) Reading package lists... Done W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?) Reading package lists... Done Building dependency tree... Done Reading state information... Done openjdk-21-jdk is already the newest version (21.0.4+7-1ubuntu2~22.04). 0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.
Step 3: Import Libraries¶
In [ ]:
#### Import the necessary libraries ####
# R5Py for travel time matrix computation
from r5py import TransportNetwork, TravelTimeMatrixComputer
# GeoPandas for spatial data manipulation
import geopandas as gpd
import pandas as pd
# General library
from matplotlib import pyplot as plt
from datetime import datetime, timedelta
import networkx as nx
import osmnx as ox
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import geopandas as gpd
# from osgeo import gdal
Step 4: Import Warnings¶
In [ ]:
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning, module="pandas.core.frame")
Step 5: Get the Turkana County Boundary¶
In [ ]:
import osmnx as ox
# Download Turkana County boundary using OSM
Turkana_boundary = ox.geocode_to_gdf('Turkana County, Kenya')
# Save it to a shapefile
Turkana_boundary.to_file('Turkana_boundary.shp')
<ipython-input-10-a2511acc9dd3>:7: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.
Turkana_boundary.to_file('Turkana_boundary.shp')
WARNING:fiona._env:Normalized/laundered field name: 'addresstype' to 'addresstyp'
WARNING:fiona._env:Normalized/laundered field name: 'display_name' to 'display_na'
In [ ]:
import geopandas as gpd
# Load the shapefile
shapefile_path = "Turkana_boundary.shp"
gdf = gpd.read_file(shapefile_path)
# Convert the GeoDataFrame to GeoJSON
geojson_path = "Turkana_boundary.geojson"
gdf.to_file(geojson_path, driver='GeoJSON')
print(f"Shapefile successfully converted to GeoJSON and saved at: {geojson_path}")
Shapefile successfully converted to GeoJSON and saved at: Turkana_boundary.geojson
Step 6 Acquire Spatial Data¶
In [ ]:
# Download Hospitals from OSM
# Define the location of interest
place_name = "Turkana County"
# Define the tags for hospitals
#hospital_tags = {'amenity': 'hospital', 'geometry':'point'}
# Use the new `features_from_place` function instead of `geometries_from_place`
#hospitals = ox.features_from_place(place_name, tags=hospital_tags)
hospitals = ox.features_from_place(place_name, tags={'amenity':'hospital', 'geometry':'point'})
# Ensure the geometry column is properly set
if 'geometry' not in hospitals.columns or hospitals.geometry.is_empty.all():
raise ValueError("The 'geometry' column does not contain valid geometries.")
# Function to clean the GeoDataFrame
def clean_geodataframe(gdf):
# Drop columns with unsupported data types
for column in gdf.columns:
if gdf[column].apply(lambda x: isinstance(x, (list, dict, tuple))).any():
gdf = gdf.drop(columns=[column])
# Drop rows with invalid geometries
gdf = gdf.dropna(subset=['geometry'])
gdf = gdf[gdf.geometry.notna()]
return gdf
hospitals_cleaned = clean_geodataframe(hospitals)
# Save the cleaned hospitals data to a GeoJSON file
hospitals_cleaned.to_file("hospitalsN.geojson", driver="GeoJSON")
/usr/local/lib/python3.10/dist-packages/osmnx/_overpass.py:254: UserWarning: This area is 28 times your configured Overpass max query area size. It will automatically be divided up into multiple sub-queries accordingly. This may take a long time. multi_poly_proj = utils_geo._consolidate_subdivide_geometry(poly_proj)
In [ ]:
##Covert polygons to points
import json
# Load GeoJSON data from a file (replace with your actual GeoJSON file path)
geojson_file_path = "hospitalsN.geojson"
with open(geojson_file_path, "r") as infile:
geojson_data = json.load(infile)
# Create an empty list to store the points
points = []
# Iterate through each feature in the GeoJSON data
for feature in geojson_data["features"]:
if feature["geometry"]["type"] == "Polygon":
# Calculate the centroid of the polygon
coordinates = feature["geometry"]["coordinates"][0] # Assuming the first set of coordinates is the outer ring
lon = sum(coord[0] for coord in coordinates) / len(coordinates)
lat = sum(coord[1] for coord in coordinates) / len(coordinates)
# Create a new point feature with the same attributes as the original polygon
point_feature = {
"type": "Feature",
"geometry": {"type": "Point", "coordinates": [lon, lat]},
"properties": feature.get("properties", {}),
}
points.append(point_feature)
elif feature["geometry"]["type"] == "Point":
# Retain existing point features
points.append(feature)
# Create a new GeoJSON object with the points
points_geojson = {"type": "FeatureCollection", "features": points}
# Save the points GeoJSON to a file (optional)
with open("points2.geojson", "w") as outfile:
json.dump(points_geojson, outfile)
In [ ]:
Hospitals = gpd.read_file('points2.geojson')
In [ ]:
Hospitals.head()
<frozen importlib._bootstrap>:914: ImportWarning: _PyDrive2ImportHook.find_spec() not found; falling back to find_module() <frozen importlib._bootstrap>:914: ImportWarning: _PyDriveImportHook.find_spec() not found; falling back to find_module() <frozen importlib._bootstrap>:914: ImportWarning: _GenerativeAIImportHook.find_spec() not found; falling back to find_module() <frozen importlib._bootstrap>:914: ImportWarning: _OpenCVImportHook.find_spec() not found; falling back to find_module() <frozen importlib._bootstrap>:914: ImportWarning: APICoreClientInfoImportHook.find_spec() not found; falling back to find_module() <frozen importlib._bootstrap>:914: ImportWarning: _BokehImportHook.find_spec() not found; falling back to find_module() <frozen importlib._bootstrap>:914: ImportWarning: _AltairImportHook.find_spec() not found; falling back to find_module()
Out[ ]:
| element_type | osmid | addr:city | addr:postcode | amenity | healthcare | name | operator | operator:type | addr:street | website | phone | building | roof:material | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | node | 10876458964 | Lodwar | 30500 | hospital | hospital | Lodwar County Referral Hospital | Turkana County Government | government | None | None | None | None | None | POINT (35.60243 3.12234) |
| 1 | way | 668848311 | Lodwar | 30500 | hospital | hospital | Lodwar District Hospital | None | None | Lodwar-Lokichogio Road | None | None | None | None | POINT (35.60136 3.12294) |
| 2 | node | 10865401647 | Nakwamoru | None | hospital | hospital | Nakwamoru Health Centre | None | None | None | https://dioceseoflodwar.org/health | None | None | None | POINT (35.43140 2.05575) |
| 3 | node | 10865168176 | Lorugum | None | hospital | hospital | Saint Elizabeth Lorugum Health Centre | None | None | None | https://dioceseoflodwar.org/health | None | None | None | POINT (35.26300 2.89235) |
| 4 | node | 3278257060 | None | None | hospital | None | None | None | None | None | None | None | None | None | POINT (35.30791 3.95302) |
In [ ]:
##Change osmid to id
Hospitals = Hospitals.rename(columns={'osmid': 'id'})
In [ ]:
#Retain name, geometry and ID columns
columns_to_retain = ['id', 'geometry','name']
Hospitals = Hospitals[columns_to_retain]
In [ ]:
Hospitals.head()
Out[ ]:
| id | geometry | name | |
|---|---|---|---|
| 0 | 10876458964 | POINT (35.60243 3.12234) | Lodwar County Referral Hospital |
| 1 | 668848311 | POINT (35.60136 3.12294) | Lodwar District Hospital |
| 2 | 10865401647 | POINT (35.43140 2.05575) | Nakwamoru Health Centre |
| 3 | 10865168176 | POINT (35.26300 2.89235) | Saint Elizabeth Lorugum Health Centre |
| 4 | 3278257060 | POINT (35.30791 3.95302) | None |
In [ ]:
##Check for duplication of features when polygons were converted to points.
#In the case of hospitals in Turkana, there was no double representation
# You can verify through visual inspection in QGIS
#Remove duplicates
import json
# Load the GeoJSON file
with open('points2.geojson', 'r') as f:
geojson_data = json.load(f)
# Define the list of IDs to remove
ids_to_remove = []
# Get the features from the GeoJSON data
features = geojson_data['features']
# Remove the features with the matching IDs
features = [feature for feature in features if feature['properties']['osmid'] not in ids_to_remove]
# Update the GeoJSON data with the modified features
geojson_data['features'] = features
# Write the updated GeoJSON data to a new file
with open('points_cleaned.geojson', 'w') as f:
json.dump(geojson_data, f)
In [ ]:
##Get grid or other geography to represent origins
##hexagon grid of created in QGIS at 500meteres aggregating the WorldPop grid from 100meters
In [ ]:
origins = gpd.read_file('Turkana_Population_Grid.geojson')
In [ ]:
origins.head()
Out[ ]:
| id | VALUE_sum | geometry | |
|---|---|---|---|
| 0 | 15758 | 23.701702 | POLYGON ((34.06044 4.16533, 34.06493 4.16536, ... |
| 1 | 24008 | 33.979122 | POLYGON ((34.10560 4.12055, 34.11009 4.12058, ... |
| 2 | 24009 | 23.882393 | POLYGON ((34.10563 4.11604, 34.11012 4.11607, ... |
| 3 | 24832 | 33.938343 | POLYGON ((34.11009 4.12058, 34.11458 4.12060, ... |
| 4 | 24833 | 20.936283 | POLYGON ((34.11012 4.11607, 34.11460 4.11610, ... |
In [ ]:
import geopandas as gpd
# Load the origins dataset
origins = gpd.read_file('Turkana_Population_Grid.geojson')
# Rename the 'VALUE_sum' column to 'Population'
origins.rename(columns={'VALUE_sum': 'Population'}, inplace=True)
In [ ]:
origins.head()
Out[ ]:
| id | Population | geometry | |
|---|---|---|---|
| 0 | 15758 | 23.701702 | POLYGON ((34.06044 4.16533, 34.06493 4.16536, ... |
| 1 | 24008 | 33.979122 | POLYGON ((34.10560 4.12055, 34.11009 4.12058, ... |
| 2 | 24009 | 23.882393 | POLYGON ((34.10563 4.11604, 34.11012 4.11607, ... |
| 3 | 24832 | 33.938343 | POLYGON ((34.11009 4.12058, 34.11458 4.12060, ... |
| 4 | 24833 | 20.936283 | POLYGON ((34.11012 4.11607, 34.11460 4.11610, ... |
In [ ]:
# Import spatial boundaries of Siaya for context; they are obtained from ONS as open geo-boundary data
ox_bound = gpd.read_file('Turkana_boundary.geojson').to_crs(epsg=4326)
In [ ]:
import geopandas as gpd
# Load the origins dataset
origins = gpd.read_file('Turkana_Population_Grid.geojson')
# Ensure all geometries are Points by converting Polygons to their centroids
if origins.geom_type.iloc[0] == 'Polygon':
origins['geometry'] = origins['geometry'].centroid
# Rename the 'VALUE_sum' column to 'Population'
origins.rename(columns={'VALUE_sum': 'Population'}, inplace=True)
<ipython-input-27-1a07275e7dbc>:8: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation. origins['geometry'] = origins['geometry'].centroid
In [ ]:
origins.head()
Out[ ]:
| id | Population | geometry | |
|---|---|---|---|
| 0 | 15758 | 23.701702 | POINT (34.06270 4.16310) |
| 1 | 24008 | 33.979122 | POINT (34.10786 4.11831) |
| 2 | 24009 | 23.882393 | POINT (34.10789 4.11380) |
| 3 | 24832 | 33.938343 | POINT (34.11235 4.11834) |
| 4 | 24833 | 20.936283 | POINT (34.11237 4.11383) |
Time matrix and accessibility¶
To start with the accessibility analysis, we would like to make the simplist case: How long it would take from one certain place to travel to another?
In [ ]:
###### Create a TransportNetwork object in R5Pyusing the OSM data ######
# This provides a basis for analysis in the entire third section
#Get osm_pbf from 'extract.bbbike.org'
transport_network = TransportNetwork(
osm_pbf='Turkana OSM.pbf', # Path to the OSM PBF file
)
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# Select one point as origin, you can choose any point in the origins dataset
# Some good example could include:
# 1308 - The city center
# 2058 - Department of Stats Building
# 1319 - Lady Margaret Hall
Lodwar = Hospitals.loc[Hospitals['name'] == "Lodwar District Hospital"]
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
In [ ]:
# Create a TravelTimeMatrixComputer object using R5Py to calculate travel times
# This is a key step as it will determine the travel times between all pairs of origins and destinations in the network
travel_time_computer_point = TravelTimeMatrixComputer(
transport_network=transport_network,
origins=Lodwar,
destinations=origins,
snap_to_network=True,
# Below are some of the travel options that can be tweaked as needed
transport_modes=['WALK', 'TRANSIT', 'CAR'], # Modes of transport to consider, e.g., 'WALK', 'BICYCLE', 'CAR', 'TRANSIT'
# speed_walking=3.6,
)
# For more information on the available options, please refer to the R5Py documentation: https://r5py.readthedocs.io/en/latest/
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code) /usr/local/lib/python3.10/dist-packages/r5py/r5/regional_task.py:227: RuntimeWarning: Departure time 2024-09-07 10:18:36.445708 is outside of the time range covered by currently loaded GTFS data sets. warnings.warn(
In [ ]:
# Compute travel times between all origins and destinations;
od_matrix_point = travel_time_computer_point.compute_travel_times()
/usr/local/lib/python3.10/dist-packages/r5py/r5/base_travel_time_matrix_computer.py:150: RuntimeWarning: Some destination points could not be snapped to the street network warnings.warn(
In [ ]:
od_matrix_point.head()
# The calculated time_matrix is vey simple: you have the starting id, and its time of travel to any other location (id).
Out[ ]:
| from_id | to_id | travel_time | |
|---|---|---|---|
| 0 | 668848311 | 46330 | NaN |
| 1 | 668848311 | 47154 | NaN |
| 2 | 668848311 | 47155 | NaN |
| 3 | 668848311 | 47978 | NaN |
| 4 | 668848311 | 48802 | NaN |
In [ ]:
od_matrix_point['to_id'] = od_matrix_point['to_id'].astype('int64')
In [ ]:
join_point = origins.merge(od_matrix_point, left_on='id', right_on='to_id')
join_point.head()
# You can see how the aggregated data look like. A geometry is introduced to spatially plot the accessibility.
Out[ ]:
| id | Population | geometry | from_id | to_id | travel_time | |
|---|---|---|---|---|---|---|
| 0 | 46330 | 42.040239 | POINT (34.23097 3.78548) | 668848311 | 46330 | NaN |
| 1 | 47154 | 125.839985 | POINT (34.23546 3.78550) | 668848311 | 47154 | NaN |
| 2 | 47155 | 27.276790 | POINT (34.23548 3.78100) | 668848311 | 47155 | NaN |
| 3 | 47978 | 175.700847 | POINT (34.23994 3.78553) | 668848311 | 47978 | NaN |
| 4 | 48802 | 115.031870 | POINT (34.24443 3.78555) | 668848311 | 48802 | NaN |
Walking
In [ ]:
travel_time_computer_point_walk = TravelTimeMatrixComputer(
transport_network=transport_network,
origins=Lodwar,
destinations=origins,
snap_to_network=True,
# Below are some of the travel options that can be tweaked as needed
transport_modes=['WALK'], # Modes of transport to consider, e.g., 'WALK', 'BICYCLE', 'CAR', 'TRANSIT'
# speed_walking=3.6,
)
# Compute travel times between all origins and destinations
od_matrix_point_walk = travel_time_computer_point_walk.compute_travel_times()
# Create the combined matrix
join_point = origins.merge(od_matrix_point_walk, left_on="id", right_on="to_id")
join_point.head()
/usr/local/lib/python3.10/dist-packages/r5py/r5/base_travel_time_matrix_computer.py:150: RuntimeWarning: Some destination points could not be snapped to the street network warnings.warn(
Out[ ]:
| id | Population | geometry | from_id | to_id | travel_time | |
|---|---|---|---|---|---|---|
| 0 | 46330 | 42.040239 | POINT (34.23097 3.78548) | 668848311 | 46330 | NaN |
| 1 | 47154 | 125.839985 | POINT (34.23546 3.78550) | 668848311 | 47154 | NaN |
| 2 | 47155 | 27.276790 | POINT (34.23548 3.78100) | 668848311 | 47155 | NaN |
| 3 | 47978 | 175.700847 | POINT (34.23994 3.78553) | 668848311 | 47978 | NaN |
| 4 | 48802 | 115.031870 | POINT (34.24443 3.78555) | 668848311 | 48802 | NaN |
Opportunities accessible within a certain travel time¶
It is important to understand the accessibility of urban amenities, such as food outlets, schools, and healthcare facilities, to different parts of the city. This information can help urban planners and policymakers identify areas with limited access to essential services and improve the overall accessibility of the city.
Similar case can be made for the accessibility of opportunities, such as jobs and schools.
In this case, we will calculate the number of hopsitals accessible within 25 minutes of sustainable travel (walking, cycling and public transport) from each population grid in the city.
In [ ]:
travel_time_computer = TravelTimeMatrixComputer(
transport_network=transport_network,
origins=origins,
destinations=Hospitals, #
#snap_to_network=True,
transport_modes=['WALK','BICYCLE', 'TRANSIT'],
)
/usr/local/lib/python3.10/dist-packages/r5py/r5/regional_task.py:227: RuntimeWarning: Departure time 2024-09-07 10:18:36.445708 is outside of the time range covered by currently loaded GTFS data sets. warnings.warn(
In [ ]:
od_matrix = travel_time_computer.compute_travel_times()
# Normally we should compute the calculation using colab, but in the beta stage,
# the time it took is too long (40minutes). So we have referred to a pre-calculated matrix
# for the travel time data. They are the same though!
In [ ]:
# If you are finding the previous block taking more than 3mins to run, you can
# uncomment the following block to load the pre-calculated matrix.
# od_matrix = pd.read_csv(wd+'results/od_matrix.csv')
In [ ]:
time_threshold = 25
# Filter the OD matrix to include only travel times less than or equal to 25 minutes
filtered_matrix = od_matrix[od_matrix['travel_time'] <= time_threshold]
# Count the number of accessible POIs for each population grid
accessible_pois = filtered_matrix.groupby('from_id').size().reset_index(name='access_Hospitals')
# Merge the accessibility information with the origins GeoDataFrame for plotting
join = origins.merge(accessible_pois, left_on="id", right_on="from_id")