# Or instead, you can use explore for an interactive plot.NOTE: this cell has been placed at the top for quick reference of the final output.
join.explore(column="access_schools", scheme='natural_breaks',cmap="plasma")

# Install the packages used in the analysis
!pip install networkx
!pip install osmnx
!pip install r5py
!pip install mapclassify
!pip install folium

#Java 21: Update Java version to execute updated RFpy
!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']}"

#### 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

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

# Download Schools from OSM
# Define the location of interest
place_name = "Nairobi County"

# Define the tags for schools
#school_tags = {'amenity': 'school', 'geometry':'point'}

# Use the new `features_from_place` function instead of `geometries_from_place`
#schools = ox.features_from_place(place_name, tags=school_tags)
schools = ox.features_from_place(place_name, tags={'amenity':'school', 'geometry':'point'})

# Ensure the geometry column is properly set
if 'geometry' not in schools.columns or schools.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

schools_cleaned = clean_geodataframe(schools)

# Save the cleaned schools data to a GeoJSON file
schools_cleaned.to_file("schoolsN.geojson", driver="GeoJSON")

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##Covert polygons to points
import json

# Load GeoJSON data from a file (replace with your actual GeoJSON file path)
geojson_file_path = "schoolsN.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)

##Check for duplication of features when polygons were converted to points.
#In the case of schools in Nairobi, some facilities had double represntation e.g Calvary Education Centre school 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 = [4457763959, 5926218250, 1201173154, 244391943, 5926387116, 4838514224, 260967392, 260967410, 261016370, 17101235, 17101234, 1244387410, 583157837, 17131693, 11902999269, 878232422, 878232423, 878232421, 720900823, 879524501, 973017504, 364993385, 36499663, 364982859, 6445081210, 5933622804, 5933721703, 5933721704, 5933721713, 6445081205, 1110227334, 5926355908, 949598078, 9137254518, 2951976091, 6445111405, 9167078118, 9164228727, 9169505385, 949597326, 949598174, 2935256787, 4711263743, 878232421, 5926090741, 5924835189, 1437347777, 890550597, 5924832980, 4461494343, 11801760327, 8128866092, 5926218136, 8157780961, 5933721708, 7174839950, 1432507879, 9108401529, 9167057817, 1483304295, 612007447, 2923513484, 6249035802, 5925863197, 9167037538, 551901275, 9164063621, 2932284095, 3082332223, 9119356135, 5926218141, 4464546559, 2405836319, 6445081208, 8129001741, 655592513, 5924835188, 6249035798, 6445081197, 9157462997, 6445081206, 6445081191, 6445132391, 1999910117, 5924832982, 5933721709, 5926218143, 5933622801, 3073260240, 592207512, 5948282117, 5933573020, 9163232288, 2952187220, 9114039451, 4472329843, 9167220117, 5933721712, 4464506028, 9167097883, 5948170933, 9163860973, 9169179517, 5926355912, 2405854914, 2405798440, 5926355913, 6249035795, 9164093563, 9165314982, 5953268876, 261016416, 260967410, 5948376939, 2968577925, 5926387120, 5948282126, 5948282127, 2405829025, 4711308866, 254653583, 7937755856, 1419004987, 5933714619, 5924832973, 9163408296, 9134741817, 9167044817, 293228409, 5933652696, 5948282118, 5933721705, 3070186504, 9166926919, 8764466562, 93668640, 3082332228, 9166914517, 6445077911, 364996664, 364996663, 364993385, 5948282121, 5933714621, 9075883318, 5134869728, 748826131, 9216102018, 9058309550, 9125919261, 11801760315, 9160876505, 2923513490, 9169178418, 1235786842, 973017505, 6830686439, 9162410712, 261016729, 9139621563, 2923089741, 9166932882, 3070274195, 9122789938, 5926218139, 5933714626, 160913634, 9137263417, 659301385, 5926090747, 144188716, 2953922777, 10780953107, 11801760314, 4464601890, 9165513244, 1764153756, 6445077901, 8374583635, 6249035799, 9173935115, 4464655293, 2105394325, 9163228068, 4464622787, 2923513494, 6249035791, 5926229107, 5926387128, 5926387129, 2968578135, 6445111413, 5915900141, 561265479, 6583228985, 409162258, 9164251364, 9167259317, 5933721655, 5933721656, 1493484848, 6445132385, 569284196, 5948282123, 973017504, 879524501, 97076364, 9160478755, 11801760328, 5926195779, 5170840100, 9122984596, 162826365, 6249035794, 6021550872, 6445111407, 5926387131, 6445129687, 4464546560, 6817508245, 752317805, 171869601, 171869598, 5948282114, 5933721653, 612007554, 9165286734, 5948220719, 3174474065, 5926195782, 5926195783, 6445111392, 5232810721, 1580152996, 2405823296, 9164495804, 9166883307, 5933721660, 7321575796, 5925863204, 890550666, 4711263746, 9161134707, 5933721658, 5112486230, 11681732671, 1764155302, 5926355907, 7098406185, 97066384, 9164129614, 9170788222, 712907767, 10780953108, 5948220720, 2935256797, 2923436819, 2923815702, 5400815313, 5924832976, 1493484865, 9722373317, 2959968892, 9122892721, 6445111419, 11801760330, 3533006870, 2923815708, 5119191997, 6445077890, 3082332231, 9162822140, 3070240345, 2968578147, 11801760316, 9164147011, 9158114917, 9116097926, 5119239745, 828155762, 17131693, 5926218264, 5926229115, 5925863206, 5948144504, 6249035793, 1419005003, 6445111422, 11801760311, 949597387, 5134869730, 775535480, 7089472243, 9134893317, 5933622807, 9163361080, 5948376938, 883145970, 5953317911, 2964954568, 5926387124, 9165411582, 8374564304, 5925863211, 5134869731, 5170815428, 7325103880, 5926218261, 11801760334, 2405836316, 5933652807, 3004383269, 9151330717, 9157787749, 3454366438, 9169137017, 9137438617, 5933721715, 9157599989, 5926218253, 5926218262, 5948376942, 106088779, 9116299929, 9157610203, 5933573023, 5953218907, 9129136345, 5933652693, 10788139060, 5933721719, 6249035800]

# 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)

Schools = gpd.read_file('points_cleaned.geojson')

Schools.head()

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

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

Schools.head()

##Get grid or other geography to represent origins
##Hexagon grid of the study area in this examople created in QGIS at 500meters aggregating the WorldPop grid centroids from 100meters

origins = gpd.read_file('NbiCentroids_500mHex_2020.geojson')

origins.head()

# Import spatial boundaries of for context of the study area; you can get boundaries from GADM:  https://gadm.org/ or your database
ox_bound = gpd.read_file('NairobiBoundary.geojson').to_crs(epsg=4326)

# Plot the features for better understanding
origins.explore(column='Population', scheme='natural_breaks',cmap="plasma")

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# You can use .explore() to interactively explore the data or .plot() to plot the data
# destinations_university.plot()
Schools.explore()

###### 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.jpeg'
#GTFS from Digital Matatus
transport_network = TransportNetwork(
    osm_pbf='NairobiOSM.osm.pbf', # Path to the OSM PBF file
    gtfs=['GTFS_FEED_2019.zip'] # Path to the GTFS file
)

# Select one point as origin, you can choose any point in the origins dataset

# Some good example could include:
# You can select by ID

Banda = Schools.loc[Schools['name'] == "The Banda School"]

# 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= Banda,
    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 2024-08-02 19:25:17.157338 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()

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).

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.

# 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')
Banda.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()

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travel_time_computer_point_walk = TravelTimeMatrixComputer(
    transport_network=transport_network,
    origins=Banda,
    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()

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

# 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)

Banda.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()

travel_time_computer = TravelTimeMatrixComputer(
    transport_network=transport_network,
    origins=origins,
    destinations=Schools, #
    #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 2024-08-02 19:25:17.157338 is outside of the time range covered by currently loaded GTFS data sets.
  warnings.warn(

#Origin Destination (O-D) Matrix
od_matrix = travel_time_computer.compute_travel_times()

#WARNING!- it takes some time to calculate the OD matrix (about 40 minutes)
# If takes too much time to calculate the OD matrix on Colab or Jupyter, you can try creating it in QGIS using QNEAT3 algorithim.
# The load it # od_matrix = pd.read_csv('your_matrix.csv')

# Set the travel time threshold : in this example 25 used based on National Policy
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_schools')

# 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_schools", scheme='natural_breaks',cmap="plasma",markersize=2,linewidth=1, legend=True)

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

	element_type	osmid	addr:city	addr:postcode	addr:street	amenity	name	phone	religion	website	...	wikipedia	noaddress	roof:colour	roof:levels	roof:shape	addr:housename	description	layer	service_times	geometry
0	node	30092225	Nairobi	00100	Ole Shapara Avenue	school	Kinderworld International School	+254798139954	muslim	https://kis.sc.ke/	...	None	None	None	None	None	None	None	None	None	POINT (36.82355 -1.31379)
1	node	30092294	None	None	None	school	Nairobi Academy Secondary	None	None	None	...	None	None	None	None	None	None	None	None	None	POINT (36.75327 -1.34018)
2	node	30312225	None	None	None	school	Consolata School	None	None	None	...	None	None	None	None	None	None	None	None	None	POINT (36.80406 -1.26749)
3	node	30402763	Nairobi	None	Kipande Road	school	Aga Khan Nursery	None	None	None	...	None	None	None	None	None	None	None	None	None	POINT (36.81492 -1.27217)
4	node	295848487	Nairobi	None	Juja Road	school	Pangani Girls Secondary School	None	None	None	...	None	None	None	None	None	None	None	None	None	POINT (36.83739 -1.26894)

	id	geometry	name
0	30092225	POINT (36.82355 -1.31379)	Kinderworld International School
1	30092294	POINT (36.75327 -1.34018)	Nairobi Academy Secondary
2	30312225	POINT (36.80406 -1.26749)	Consolata School
3	30402763	POINT (36.81492 -1.27217)	Aga Khan Nursery
4	295848487	POINT (36.83739 -1.26894)	Pangani Girls Secondary School

	id	subcounty	ward	Population	geometry
0	1382.0	Langata Sub County	Karen Ward	213.695	POINT (36.74868 -1.33218)
1	5271.0	Kasarani Sub County	Ruai Ward	339.260	POINT (36.98600 -1.26232)
2	3794.0	Ruaraka Sub County	Korogocho Ward	13673.037	POINT (36.89655 -1.24190)
3	4018.0	Langata Sub County	Mugumo-ini Ward	0.000	POINT (36.90810 -1.38431)
4	2158.0	Langata Sub County	Mugumo-ini Ward	0.000	POINT (36.79532 -1.36839)

	from_id	to_id	travel_time
0	1203369516	1382.0	10.0
1	1203369516	5271.0	NaN
2	1203369516	3794.0	NaN
3	1203369516	4018.0	33.0
4	1203369516	2158.0	7.0

	id	subcounty	ward	Population	geometry	from_id	to_id	travel_time
0	1382.0	Langata Sub County	Karen Ward	213.695	POINT (36.74868 -1.33218)	1203369516	1382	10.0
1	5271.0	Kasarani Sub County	Ruai Ward	339.260	POINT (36.98600 -1.26232)	1203369516	5271	NaN
2	3794.0	Ruaraka Sub County	Korogocho Ward	13673.037	POINT (36.89655 -1.24190)	1203369516	3794	NaN
3	4018.0	Langata Sub County	Mugumo-ini Ward	0.000	POINT (36.90810 -1.38431)	1203369516	4018	33.0
4	2158.0	Langata Sub County	Mugumo-ini Ward	0.000	POINT (36.79532 -1.36839)	1203369516	2158	7.0

School Accessibility- Nairobi : Number of Schools Accessible within 25 minutes

Step 1 install and import packages¶

Step 2 Acquire Spatial Data¶

Time matrix and accessibility¶

Opportunities accessible within a certain travel time¶