Mtaa wetu is a neighbourhood planning and level of staisfaction app. Mtaa wetu page helps you to visualise maps of how easy it is to access schools, jobs and hospitals across the city. Select the layer of interest e.g schools to view the map of school accessibility. For more information about how accessibility is measured. Go to our methodology section(link)

Mtaa Wetu

• Mtaas (current)
• Maps
  • Destination Accessibility
  • Diversity of Land Use
  • Design of Road Network
  • Zoning Policy
  • Opportunity
  • Population
• Research
  • Datasets
    • Links to open data sites and portals
    • Mtaa Wetu generated information
  • Online Research
    • Research papers
    • Data validation examples
    • Notebooks/ tutorials
• About
• CyberMart
• Contact Us
• Dashboard

1. Notebooks
2. Access to Hospitals in Siaya County

Access to Hospitals in Siaya County

Access to Hospitals in Siaya County- Number of Hospitals Accessible within 25 Minutes from population point in Siaya

NOTE: Points represent population counts in 500m hexagons covering Siaya County

#Map of accessiblity to hospitals
join.explore(column="access_Hospitals", scheme='natural_breaks',cmap="plasma")

Make this Notebook Trusted to load map: File -> Trust Notebook

Hospitals in Siaya Source: Open Streets Maps

# Map of hospitals in Siaya
Hospitals.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

***Population distribution in Siaya County ***

500 meter hexagon cnetroids ; Population count in 500 meter hexagon

# Population distribution in Siaya
origins.explore(column='Population', scheme='natural_breaks',cmap="plasma")

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Make this Notebook Trusted to load map: File -> Trust Notebook

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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

# Install the packages used in the first part of the tutorial
!pip install networkx
!pip install osmnx
!pip install rasterio

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# Install the packages used in the third part of the tutorial
!pip install r5py
!pip install mapclassify
!pip install folium

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Step 2: Install Java 21

#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 "11.0.24" 2024-07-16
OpenJDK Runtime Environment (build 11.0.24+8-post-Ubuntu-1ubuntu322.04)
OpenJDK 64-Bit Server VM (build 11.0.24+8-post-Ubuntu-1ubuntu322.04, mixed mode, sharing)
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Step 3: Import Libraries

#### 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

import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning, module="pandas.core.frame")

Step 5: Get the Siaya County Boundary

import osmnx as ox
import geopandas as gpd
import matplotlib.pyplot as plt

# Define the place name
place_name = "Siaya County, Kenya"

# Get the boundary polygon of Siaya County
gdf = ox.geocode_to_gdf(place_name)

# Display the boundary GeoDataFrame
print(gdf)

# Plot the boundary polygon
gdf.plot()
plt.title("Boundary of Siaya County")
plt.show()

                                            geometry  bbox_north  bbox_south  \
0  POLYGON ((33.94912 -0.33974, 34.21235 -0.34072...    0.311022   -0.431292   

   bbox_east  bbox_west   place_id  osm_type   osm_id       lat        lon  \
0  34.559942  33.949123  368231981  relation  3486291 -0.060401  34.200135   

      class            type  place_rank  importance addresstype          name  \
0  boundary  administrative           8    0.406682       state  Siaya County   

                  display_name  
0  Siaya County, Nyanza, Kenya  

import osmnx as ox

# Download Siaya County boundary using OSM
siaya_boundary = ox.geocode_to_gdf('Siaya County, Kenya')

# Save it to a shapefile (optional)
siaya_boundary.to_file('siaya_boundary.shp')

<ipython-input-7-97186e1fa29b>:7: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.
  siaya_boundary.to_file('siaya_boundary.shp')
WARNING:fiona._env:Normalized/laundered field name: 'addresstype' to 'addresstyp'
WARNING:fiona._env:Normalized/laundered field name: 'display_name' to 'display_na'

import geopandas as gpd

# Load the shapefile
shapefile_path = "siaya_boundary.shp"
gdf = gpd.read_file(shapefile_path)

# Convert the GeoDataFrame to GeoJSON
geojson_path = "siaya_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: siaya_boundary.geojson

Step 6 Acquire Spatial Data

# Download Hospitals from OSM
# Define the location of interest
place_name = "Siaya 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")

##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)

Hospitals = gpd.read_file('points2.geojson')

Hospitals.head()

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	element_type	osmid	amenity	name	operator	emergency	healthcare	healthcare:speciality	building	addr:city	addr:postcode	operator:type	leisure	geometry
0	node	1419020606	hospital	St Paul's Health Centre	None	yes	None	None	None	None	None	None	None	POINT (34.29362 0.16300)
1	node	1850405064	hospital	None	None	None	hospital	None	None	None	None	None	None	POINT (34.36263 0.15645)
2	node	11693537898	hospital	Yala Level 4 Hospital	None	None	hospital	None	None	None	None	None	None	POINT (34.53782 0.10127)
3	way	129746965	hospital	St Paul PSC Section	None	None	None	None	None	None	None	None	None	POINT (34.29335 0.16304)
4	way	129856950	hospital	Ambira Sub District Hospital	None	yes	None	None	None	None	None	None	None	POINT (34.28540 0.18108)

##Change osmid to id
Hospitals = Hospitals.rename(columns={'osmid': 'id'})

#Retain name, geometry and ID columns
columns_to_retain = ['id', 'geometry','name']
Hospitals = Hospitals[columns_to_retain]

Hospitals.head()

	id	geometry	name
0	1419020606	POINT (34.29362 0.16300)	St Paul's Health Centre
1	1850405064	POINT (34.36263 0.15645)	None
2	11693537898	POINT (34.53782 0.10127)	Yala Level 4 Hospital
3	129746965	POINT (34.29335 0.16304)	St Paul PSC Section
4	129856950	POINT (34.28540 0.18108)	Ambira Sub District Hospital

##Check for duplication of features when polygons were converted to points.
#In the case of hospitals in Siaya, some facilities had double represntation e.g Ambira Sub District Hospital was represented as two points
# 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 = [772387134, 774523118, 180331572]
# 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)

##Get grid or other geography to represent origins
##hexagon grid of created in QGIS at 500meteres aggregating the WorldPop grid centroids from 100meters

origins = gpd.read_file('siaya_centroids_population.geojson')

origins.head()

	Population	geometry
0	9.754832	POINT (34.29512 -0.40250)
1	8.343940	POINT (34.30346 -0.40250)
2	5.247587	POINT (34.28678 -0.40250)
3	6.275999	POINT (34.29095 -0.40250)
4	5.481225	POINT (34.28262 -0.39833)

import geopandas as gpd

# Load the origins dataset
origins = gpd.read_file('siaya_centroids_population.geojson')

# Add the 'id' column with unique identifiers
origins['id'] = range(1, len(origins) + 1)

# Add the 'Location' column with random names
origins['Location'] = ['Location' + str(i) for i in range(1, len(origins) + 1)]

origins.head()

	Population	geometry	id	Location
0	9.754832	POINT (34.29512 -0.40250)	1	Location1
1	8.343940	POINT (34.30346 -0.40250)	2	Location2
2	5.247587	POINT (34.28678 -0.40250)	3	Location3
3	6.275999	POINT (34.29095 -0.40250)	4	Location4
4	5.481225	POINT (34.28262 -0.39833)	5	Location5

# Import spatial boundaries of Siaya for context; they are obtained from ONS as open geo-boundary data
ox_bound = gpd.read_file('siaya_boundary.geojson').to_crs(epsg=4326)

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?

###### 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='Siaya.osm.pbf', # Path to the OSM PBF file
)

# 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

Ambira = Hospitals.loc[Hospitals['name'] == "Ambira Sub District Hospital"]

# 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=Ambira,
    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/r5py/r5/regional_task.py:227: RuntimeWarning: Departure time 2024-09-02 18:30:42.120076 is outside of the time range covered by currently loaded GTFS data sets.
  warnings.warn(

# 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(

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).

	from_id	to_id	travel_time
0	129856950	1	78.0
1	129856950	2	76.0
2	129856950	3	77.0
3	129856950	4	78.0
4	129856950	5	74.0

od_matrix_point['to_id'] = od_matrix_point['to_id'].astype('int64')

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.

	Population	geometry	id	Location	from_id	to_id	travel_time
0	9.754832	POINT (34.29512 -0.40250)	1	Location1	129856950	1	78.0
1	8.343940	POINT (34.30346 -0.40250)	2	Location2	129856950	2	76.0
2	5.247587	POINT (34.28678 -0.40250)	3	Location3	129856950	3	77.0
3	6.275999	POINT (34.29095 -0.40250)	4	Location4	129856950	4	78.0
4	5.481225	POINT (34.28262 -0.39833)	5	Location5	129856950	5	74.0

# Build a plot to show the travel times
fig, ax = plt.subplots(figsize=(10,8))  # Adjust size as needed

# Plot the boundary for context
ox_bound.plot(ax=ax, color='none', scheme='natural_breaks', linewidth=1)
join_point.plot(ax=ax,column="travel_time", cmap="cividis_r",scheme = 'natural_breaks', markersize=2,linewidth=1,legend=True,aspect='equal')
Ambira.plot(ax=ax, color='red', markersize=10,legend=True)

ax.set_title("Travel time from selected point to all destinations combining public transport and active travel")
ax.set_axis_off()

/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)

Walking

travel_time_computer_point_walk = TravelTimeMatrixComputer(
    transport_network=transport_network,
    origins=Ambira,
    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(

	Population	geometry	id	Location	from_id	to_id	travel_time
0	9.754832	POINT (34.29512 -0.40250)	1	Location1	129856950	1	NaN
1	8.343940	POINT (34.30346 -0.40250)	2	Location2	129856950	2	NaN
2	5.247587	POINT (34.28678 -0.40250)	3	Location3	129856950	3	NaN
3	6.275999	POINT (34.29095 -0.40250)	4	Location4	129856950	4	NaN
4	5.481225	POINT (34.28262 -0.39833)	5	Location5	129856950	5	NaN

join_point.explore(column="travel_time", scheme='natural_breaks',cmap="cividis_r")

Make this Notebook Trusted to load map: File -> Trust Notebook

# Or, You can use this part to plot the result
fig, ax = plt.subplots(figsize=(10,8))  # Adjust size as needed

# Change the following line to your own variable.
join_point.plot(ax=ax,column="travel_time", cmap="cividis_r",markersize=2,linewidth=1,legend=True)

Ambira.plot(ax=ax, color='red', markersize=10, scheme='natural_breaks', legend=True)
ox_bound.plot(ax=ax, color='none',linewidth=1)

ax.set_title("Travel time from selected point to all destinations with only walking")
ax.set_axis_off()

/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)

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.

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/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 2024-09-02 18:30:42.120076 is outside of the time range covered by currently loaded GTFS data sets.
  warnings.warn(

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!

# 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')

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")

# Build a plot to show the accessible opportunities
fig, ax = plt.subplots(figsize=(10,8))  # Adjust size as needed

# Plot the Oxford County boundary for context
ox_bound.plot(ax=ax, color='none', scheme='natural_breaks', linewidth=1)

# Plot the number of accessible food outlets aggregated in the above block
join.plot(ax=ax,column="access_Hospitals", scheme='natural_breaks',cmap="plasma",markersize=2,linewidth=1, legend=True)

ax.set_title(f"Number of Hospitals Accessible Under {time_threshold} Minutes")
ax.set_axis_off()

import geopandas
import shapely

population_grid = geopandas.read_file("SiayaGPKG.gpkg")

print(population_grid.columns)

Index(['VALUE', 'geometry'], dtype='object')

# Install the packages used in the third part of the tutorial
!pip install r5py
!pip install mapclassify
!pip install folium

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import matplotlib.pyplot as plt

overview_map = population_grid.explore("VALUE", cmap="Reds")

display(overview_map)

print(type(overview_map))

<class 'folium.folium.Map'>

population_grid.plot(column='VALUE', cmap='Reds', legend=True)

<Axes: >

# Allow 12 GB of memory
import sys
sys.argv.append(["--max-memory", "12G"])

import geopandas as gpd

# Assuming population_grid is already defined and is a GeoDataFrame
# Adding a sequential ID column
population_grid['id'] = range(1, len(population_grid) + 1)

# Verify the addition of the ID column
print(population_grid.head())

       VALUE                                           geometry  id
0  10.120865  MULTIPOLYGON (((34.24958 0.31711, 34.24958 0.3...   1
1   8.948438  MULTIPOLYGON (((34.25126 0.31759, 34.25208 0.3...   2
2   5.794432  MULTIPOLYGON (((34.24759 0.31628, 34.24792 0.3...   3
3   9.713193  MULTIPOLYGON (((34.24875 0.31674, 34.24875 0.3...   4
4  16.873987  MULTIPOLYGON (((34.25708 0.31631, 34.25708 0.3...   5

import folium
from IPython.display import display

# Define latitude and longitude for map center
latitude = 0.062568  # Replace with your desired latitude
longitude = 34.283092  # Replace with your desired longitude

# Create a base map
m = folium.Map(location=[latitude, longitude], zoom_start=12)

# Add your GeoDataFrame to the map using the new ID column
folium.Choropleth(
    geo_data=population_grid,
    data=population_grid,
    columns=['id', 'VALUE'],  # Use 'id' and the column for values
    key_on='feature.properties.id',  # Adjust to match the ID column
    fill_color='Reds',
    fill_opacity=0.7,
    line_opacity=0.2
).add_to(m)

# Display the map
display(m)

# Display the map
display(m)

import folium
from IPython.display import display

latitude = 0.062568
longitude = 34.283092

# Create a simple map
m = folium.Map(location=[latitude, longitude], zoom_start=12)
display(m)

Make this Notebook Trusted to load map: File -> Trust Notebook

import folium
import geopandas as gpd

# Assuming population_grid is your GeoDataFrame
# Sample latitude and longitude for the map center
latitude = 0.062568
longitude = 34.283092

# Create a base map
m = folium.Map(location=[latitude, longitude], zoom_start=12)

# Convert the GeoDataFrame to GeoJSON format and add to the map
folium.GeoJson(
    population_grid,
    style_function=lambda x: {
        'fillColor': 'red',
        'color': 'black',
        'weight': 1,
        'fillOpacity': 0.5,
    }
).add_to(m)

# Display the map
m