In [8]:
# Read in and plot grid file for Chicago
grid_file = gpd.read_file('./data/raw/public/GridFile/Chicago_Grid.shp')
grid_file.plot(figsize=(8,8))
Out[8]:
<AxesSubplot:>
Reproduction of: Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA
Original study by Kang, J. Y., A. Michels, F. Lyu, Shaohua Wang, N. Agbodo, V. L. Freeman, and Shaowen Wang. 2020. Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. International Journal of Health Geographics 19 (1):1–17. DOI:10.1186/s12942-020-00229-x.
Reproduction Authors: Joe Holler, Derrick Burt, and Kufre Udoh With contributions from Peter Kedron, Drew An-Pham, and the Spring 2021 Open Source GIScience class at Middlebury
With modifications from Elise Chan, Alana Lutz
Reproduction Materials Available at: github.com/HEGSRR/RPr-Kang-2020
Created: 2021-06-01
Revised: 2023-11-17
The original study uses network analysis and the ratio of potential patients to hospital services to calculate the accessibility of hospitals during the Covid-19 pandemic for Chicago and the state of Illinois as a whole. The four datasets used as inputs are: OpenStreetMap road geometries and speed limits, hospital locations with the number of ICU beds and ventilators, the number of Covid-19 cases in each zip code (where data is assigned to the centroid of the zip code), and the population over the age of 50 in each census tract (where data is assigned to the centroid of the tract). Each hospital gets a catchment area with distances 10, 20, and 30 minutes away from the hospital based on speed limits. The access ratio for hexagonal subunits was calculated by weighting the service ratio (number of available services to population wanting to use that service) by the travel distance and finally normalizing these scores on a range from 0-1 where 0 is not accessible and 1 is highly accessible.
To perform the ESFCA method, three types of data are required, as follows: (1) road network, (2) population, and (3) hospital information. The road network can be obtained from the OpenStreetMap Python Library, called OSMNX. The population data is available on the American Community Survey. Lastly, hospital information is also publically available on the Homelanad Infrastructure Foundation-Level Data.
Import necessary libraries to run this model.
See environment.yml for the library versions used for this analysis.
# Import modules
import numpy as np
import pandas as pd
import geopandas as gpd
import networkx as nx
import osmnx as ox
import re
from shapely.geometry import Point, LineString, Polygon
import matplotlib.pyplot as plt
from tqdm import tqdm
import multiprocessing as mp
import folium
import itertools
import os
import time
import warnings
import IPython
import requests
from IPython.display import display, clear_output
warnings.filterwarnings("ignore")
print('\n'.join(f'{m.__name__}=={m.__version__}' for m in globals().values() if getattr(m, '__version__', None)))
numpy==1.22.0 pandas==1.3.5 geopandas==0.10.2 networkx==2.6.3 osmnx==1.1.2 re==2.2.1 folium==0.12.1.post1 IPython==8.3.0 requests==2.27.1
Because we have restructured the repository for replication, we need to check our working directory and make necessary adjustments.
# Check working directory
os.getcwd()
'/home/jovyan/work/RPr-Kang-2020/procedure/code'
# Use to set work directory properly
if os.path.basename(os.getcwd()) == 'code':
os.chdir('../../')
os.getcwd()
'/home/jovyan/work/RPr-Kang-2020'
If you would like to use the data generated from the pre-processing scripts, use the following code:
covid_data = gpd.read_file('./data/raw/public/Pre-Processing/covid_pre-processed.shp')
atrisk_data = gpd.read_file('./data/raw/public/Pre-Processing/atrisk_pre-processed.shp')
# Read in at risk population data
atrisk_data = gpd.read_file('./data/raw/public/PopData/Illinois_Tract.shp')
atrisk_data.head()
| GEOID | STATEFP | COUNTYFP | TRACTCE | NAMELSAD | Pop | Unnamed_ 0 | NAME | OverFifty | TotalPop | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 17091011700 | 17 | 091 | 011700 | Census Tract 117 | 3688 | 588 | Census Tract 117, Kankakee County, Illinois | 1135 | 3688 | POLYGON ((-87.88768 41.13594, -87.88764 41.136... |
| 1 | 17091011800 | 17 | 091 | 011800 | Census Tract 118 | 2623 | 220 | Census Tract 118, Kankakee County, Illinois | 950 | 2623 | POLYGON ((-87.89410 41.14388, -87.89400 41.143... |
| 2 | 17119400951 | 17 | 119 | 400951 | Census Tract 4009.51 | 5005 | 2285 | Census Tract 4009.51, Madison County, Illinois | 2481 | 5005 | POLYGON ((-90.11192 38.70281, -90.11128 38.703... |
| 3 | 17119400952 | 17 | 119 | 400952 | Census Tract 4009.52 | 3014 | 2299 | Census Tract 4009.52, Madison County, Illinois | 1221 | 3014 | POLYGON ((-90.09442 38.72031, -90.09360 38.720... |
| 4 | 17135957500 | 17 | 135 | 957500 | Census Tract 9575 | 2869 | 1026 | Census Tract 9575, Montgomery County, Illinois | 1171 | 2869 | POLYGON ((-89.70369 39.34803, -89.69928 39.348... |
# Read in covid case data
covid_data = gpd.read_file('./data/raw/public/PopData/Chicago_ZIPCODE.shp')
covid_data['cases'] = covid_data['cases']
covid_data.head()
| ZCTA5CE10 | County | State | Join | ZONE | ZONENAME | FIPS | pop | cases | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 60660 | Cook County | IL | Cook County IL | IL_E | Illinois East | 1201 | 43242 | 78 | POLYGON ((-87.65049 41.99735, -87.65029 41.996... |
| 1 | 60640 | Cook County | IL | Cook County IL | IL_E | Illinois East | 1201 | 69715 | 117 | POLYGON ((-87.64645 41.97965, -87.64565 41.978... |
| 2 | 60614 | Cook County | IL | Cook County IL | IL_E | Illinois East | 1201 | 71308 | 134 | MULTIPOLYGON (((-87.67703 41.91845, -87.67705 ... |
| 3 | 60712 | Cook County | IL | Cook County IL | IL_E | Illinois East | 1201 | 12539 | 42 | MULTIPOLYGON (((-87.76181 42.00465, -87.76156 ... |
| 4 | 60076 | Cook County | IL | Cook County IL | IL_E | Illinois East | 1201 | 31867 | 114 | MULTIPOLYGON (((-87.74782 42.01540, -87.74526 ... |
Note that 999 is treated as a "NULL"/"NA" so these hospitals are filtered out. This data contains the number of ICU beds and ventilators at each hospital.
# Read in hospital data
hospitals = gpd.read_file('./data/raw/public/HospitalData/Chicago_Hospital_Info.shp')
hospitals.head()
| FID | Hospital | City | ZIP_Code | X | Y | Total_Bed | Adult ICU | Total Vent | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | Methodist Hospital of Chicago | Chicago | 60640 | -87.671079 | 41.972800 | 145 | 36 | 12 | MULTIPOINT (-87.67108 41.97280) |
| 1 | 4 | Advocate Christ Medical Center | Oak Lawn | 60453 | -87.732483 | 41.720281 | 785 | 196 | 64 | MULTIPOINT (-87.73248 41.72028) |
| 2 | 13 | Evanston Hospital | Evanston | 60201 | -87.683288 | 42.065393 | 354 | 89 | 29 | MULTIPOINT (-87.68329 42.06539) |
| 3 | 24 | AMITA Health Adventist Medical Center Hinsdale | Hinsdale | 60521 | -87.920116 | 41.805613 | 261 | 65 | 21 | MULTIPOINT (-87.92012 41.80561) |
| 4 | 25 | Holy Cross Hospital | Chicago | 60629 | -87.690841 | 41.770001 | 264 | 66 | 21 | MULTIPOINT (-87.69084 41.77000) |
# Plot hospital data
m = folium.Map(location=[41.85, -87.65], tiles='cartodbpositron', zoom_start=10)
for i in range(0, len(hospitals)):
folium.CircleMarker(
location=[hospitals.iloc[i]['Y'], hospitals.iloc[i]['X']],
popup="{}{}\n{}{}\n{}{}".format('Hospital Name: ',hospitals.iloc[i]['Hospital'],
'ICU Beds: ',hospitals.iloc[i]['Adult ICU'],
'Ventilators: ', hospitals.iloc[i]['Total Vent']),
radius=5,
color='blue',
fill=True,
fill_opacity=0.6,
legend_name = 'Hospitals'
).add_to(m)
legend_html = '''<div style="position: fixed; width: 20%; heigh: auto;
bottom: 10px; left: 10px;
solid grey; z-index:9999; font-size:14px;
"> Legend<br>'''
m
# Read in and plot grid file for Chicago
grid_file = gpd.read_file('./data/raw/public/GridFile/Chicago_Grid.shp')
grid_file.plot(figsize=(8,8))
<AxesSubplot:>
If Chicago_Network_Buffer.graphml does not already exist, this cell will query the road network from OpenStreetMap.
Each of the road network code blocks may take a few mintues to run.
%%time
# To create a new graph from OpenStreetMap, delete or rename data/raw/private/Chicago_Network_Buffer.graphml
# (if it exists), and set OSM to True
OSM = False
# if buffered street network is not saved, and OSM is preferred, # generate a new graph from OpenStreetMap and save it
if not os.path.exists("./data/raw/private/Chicago_Network_Buffer.graphml") and OSM:
print("Loading buffered Chicago road network from OpenStreetMap. Please wait... runtime may exceed 9min...", flush=True)
G = ox.graph_from_place('Chicago', network_type='drive', buffer_dist=24140.2)
print("Saving Chicago road network to raw/private/Chicago_Network_Buffer.graphml. Please wait...", flush=True)
ox.save_graphml(G, './data/raw/private/Chicago_Network_Buffer.graphml')
print("Data saved.")
# otherwise, if buffered street network is not saved, download graph from the OSF project
elif not os.path.exists("./data/raw/private/Chicago_Network_Buffer.graphml"):
print("Downloading buffered Chicago road network from OSF...", flush=True)
url = 'https://osf.io/download/z8ery/'
r = requests.get(url, allow_redirects=True)
print("Saving buffered Chicago road network to file...", flush=True)
open('./data/raw/private/Chicago_Network_Buffer.graphml', 'wb').write(r.content)
# if the buffered street network is already saved, load it
if os.path.exists("./data/raw/private/Chicago_Network_Buffer.graphml"):
print("Loading buffered Chicago road network from raw/private/Chicago_Network_Buffer.graphml. Please wait...", flush=True)
G = ox.load_graphml('./data/raw/private/Chicago_Network_Buffer.graphml')
print("Data loaded.")
else:
print("Error: could not load the road network from file.")
Loading buffered Chicago road network from raw/private/Chicago_Network_Buffer.graphml. Please wait... Data loaded. CPU times: user 36.6 s, sys: 1.52 s, total: 38.2 s Wall time: 38.1 s
%%time
ox.plot_graph(G, node_size = 1, bgcolor = 'white', node_color = 'black', edge_color = "#333333", node_alpha = 0.5, edge_linewidth = 0.5)
CPU times: user 59.7 s, sys: 285 ms, total: 59.9 s Wall time: 59.7 s
(<Figure size 576x576 with 1 Axes>, <AxesSubplot:>)
Display all the unique speed limit values and count how many network edges (road segments) have each value. We will compare this to our cleaned network later.
%%time
# Turn nodes and edges into geodataframes
nodes, edges = ox.graph_to_gdfs(G, nodes=True, edges=True)
# Get unique counts of road segments for each speed limit
print(edges['maxspeed'].value_counts())
print(str(len(edges)) + " edges in graph")
# can we also visualize highways / roads with higher speed limits to check accuracy?
# the code above converts the graph into an edges geodataframe, which could theoretically be filtered
# by fast road segments and mapped, e.g. in folium
25 mph 4793 30 mph 3555 35 mph 3364 40 mph 2093 45 mph 1418 20 mph 1155 55 mph 614 60 mph 279 50 mph 191 40 79 15 mph 76 70 mph 71 65 mph 54 10 mph 38 [40 mph, 45 mph] 27 [30 mph, 35 mph] 26 45,30 24 [40 mph, 35 mph] 22 70 21 25 20 [55 mph, 45 mph] 16 25, east 14 [45 mph, 35 mph] 13 [30 mph, 25 mph] 10 [45 mph, 50 mph] 8 50 8 [40 mph, 30 mph] 7 [35 mph, 25 mph] 6 [55 mph, 60 mph] 5 20 4 [70 mph, 60 mph] 3 [65 mph, 60 mph] 3 [40 mph, 45 mph, 35 mph] 3 [70 mph, 65 mph] 2 [70 mph, 45 mph, 5 mph] 2 [40, 45 mph] 2 [35 mph, 50 mph] 2 35 2 [55 mph, 65 mph] 2 [40 mph, 50 mph] 2 [15 mph, 25 mph] 2 [40 mph, 35 mph, 25 mph] 2 [15 mph, 40 mph, 30 mph] 2 [20 mph, 25 mph] 2 [30 mph, 25, east] 2 [65 mph, 55 mph] 2 [20 mph, 35 mph] 2 [55 mph, 55] 2 55 2 [15 mph, 30 mph] 2 [45 mph, 30 mph] 2 [15 mph, 45 mph] 2 [55 mph, 45, east, 50 mph] 2 [20 mph, 30 mph] 1 [5 mph, 45 mph, 35 mph] 1 [55 mph, 35 mph] 1 [5 mph, 35 mph] 1 [55 mph, 50 mph] 1 Name: maxspeed, dtype: int64 384240 edges in graph CPU times: user 35.5 s, sys: 46.4 ms, total: 35.6 s Wall time: 35.5 s
edges.head()
| osmid | highway | oneway | length | name | geometry | lanes | ref | bridge | maxspeed | access | service | tunnel | junction | width | area | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| u | v | key | ||||||||||||||||
| 261095436 | 261095437 | 0 | 24067717 | residential | False | 46.873 | NaN | LINESTRING (-87.90237 42.10571, -87.90198 42.1... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 261095437 | 261095439 | 0 | 24067717 | residential | False | 46.317 | NaN | LINESTRING (-87.90198 42.10540, -87.90159 42.1... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 261095436 | 0 | 24067717 | residential | False | 46.873 | NaN | LINESTRING (-87.90198 42.10540, -87.90237 42.1... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |
| 261109275 | 0 | 24069424 | residential | False | 34.892 | NaN | LINESTRING (-87.90198 42.10540, -87.90227 42.1... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |
| 261109274 | 0 | 24069424 | residential | False | 47.866 | NaN | LINESTRING (-87.90198 42.10540, -87.90156 42.1... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Cleans the OSMNX network to work better with drive-time analysis.
First, we remove all nodes with 0 outdegree because any hospital assigned to such a node would be unreachable from everywhere. Next, we remove small (under 10 node) strongly connected components to reduce erroneously small ego-centric networks. Lastly, we ensure that the max speed is set and in the correct units before calculating time.
Args:
Returns:
# view all highway types
print(edges['highway'].value_counts())
residential 296481 secondary 30909 tertiary 29216 primary 19277 motorway_link 2322 unclassified 1840 motorway 1449 trunk 843 primary_link 833 secondary_link 356 living_street 238 trunk_link 157 tertiary_link 121 [residential, unclassified] 69 [tertiary, residential] 66 [secondary, primary] 15 [secondary, tertiary] 10 [motorway, motorway_link] 6 [tertiary, unclassified] 6 [motorway, trunk] 4 [residential, living_street] 4 [secondary, secondary_link] 3 busway 2 [motorway, primary] 2 [tertiary, motorway_link] 2 emergency_bay 2 [trunk, primary] 2 [tertiary, tertiary_link] 1 [trunk, motorway] 1 [primary, motorway_link] 1 [secondary, motorway_link] 1 [primary_link, residential] 1 Name: highway, dtype: int64
# two things about this function:
# 1) the work to remove nodes is hardly worth it now that OSMnx cleans graphs by default
# the function is now only pruning < 300 nodes
# 2) try using the OSMnx speed module for setting speeds, travel times
# https://osmnx.readthedocs.io/en/stable/user-reference.html#module-osmnx.speed
# just be careful about units of speed and time!
# the remainder of this code expects 'time' to be measured in minutes
def network_setting(network):
_nodes_removed = len([n for (n, deg) in network.out_degree() if deg ==0])
network.remove_nodes_from([n for (n, deg) in network.out_degree() if deg ==0])
for component in list(nx.strongly_connected_components(network)):
if len(component)<10:
for node in component:
_nodes_removed+=1
network.remove_node(node)
ox.speed.add_edge_speeds(network)
ox.speed.add_edge_travel_times(network)
print("Removed {} nodes ({:2.4f}%) from the OSMNX network".format(_nodes_removed, _nodes_removed/float(network.number_of_nodes())))
print("Number of nodes: {}".format(network.number_of_nodes()))
print("Number of edges: {}".format(network.number_of_edges()))
return(network)
%%time
# G, hospitals, grid_file, pop_data = file_import (population_dropdown.value)
G = network_setting(G)
# Create point geometries for each node in the graph, to make constructing catchment area polygons easier
for node, data in G.nodes(data=True):
data['geometry']=Point(data['x'], data['y'])
# Modify code to react to processor dropdown (got rid of file_import function)
Removed 274 nodes (0.0019%) from the OSMNX network Number of nodes: 142044 Number of edges: 383911 CPU times: user 45.2 s, sys: 184 ms, total: 45.4 s Wall time: 45.3 s
Display all the unique speed limit values and count how many network edges (road segments) have each value. Compare to the previous results.
%%time
## Get unique counts for each road network
# Turn nodes and edges in geodataframes
nodes, edges = ox.graph_to_gdfs(G, nodes=True, edges=True)
# Check that osmnx added speeds and travel times to graph
print(edges['speed_kph'].value_counts())
print(str(len(edges)) + " edges in graph")
print(edges['travel_time'].value_counts())
39.2 291413
48.3 29822
56.7 26353
60.1 14985
40.2 5604
56.3 3364
86.3 2200
64.4 2093
32.2 1872
42.9 1793
72.4 1418
69.8 654
88.5 606
90.1 565
96.6 277
80.5 191
51.0 118
40.0 80
24.1 76
112.7 61
104.6 42
16.1 38
25.0 34
68.0 29
52.0 26
45.3 24
60.0 24
70.0 21
64.0 18
80.0 16
44.0 12
56.0 9
76.0 8
50.0 8
48.0 8
36.0 6
92.0 5
96.0 4
71.0 4
20.0 4
104.0 3
32.0 3
72.0 3
45.0 3
100.0 3
52.4 2
55.0 2
108.0 2
35.0 2
53.0 2
84.0 1
Name: speed_kph, dtype: int64
383911 edges in graph
9.3 14185
9.2 11922
18.6 8012
9.4 7209
18.5 6608
...
199.5 1
115.5 1
145.7 1
122.3 1
136.9 1
Name: travel_time, Length: 1183, dtype: int64
CPU times: user 34.6 s, sys: 64.1 ms, total: 34.7 s
Wall time: 34.7 s
def hospital_setting(hospitals, G):
# Create an empty column
hospitals['nearest_osm']=None
# Append the neaerest osm column with each hospitals neaerest osm node
for i in tqdm(hospitals.index, desc="Find the nearest network node from hospitals", position=0):
hospitals['nearest_osm'][i] = ox.get_nearest_node(G, [hospitals['Y'][i], hospitals['X'][i]], method='euclidean') # find the nearest node from hospital location
print ('hospital setting is done')
return(hospitals)
Converts geodata to centroids
Args:
Returns:
def pop_centroid (pop_data, pop_type):
pop_data = pop_data.to_crs({'init': 'epsg:4326'})
# If pop is selected in dropdown, select at risk pop where population is greater than 0
if pop_type =="pop":
pop_data=pop_data[pop_data['OverFifty']>=0]
# If covid is selected in dropdown, select where covid cases are greater than 0
if pop_type =="covid":
pop_data=pop_data[pop_data['cases']>=0]
pop_cent = pop_data.centroid # it make the polygon to the point without any other information
# Convert to gdf
pop_centroid = gpd.GeoDataFrame()
i = 0
for point in tqdm(pop_cent, desc='Pop Centroid File Setting', position=0):
if pop_type== "pop":
pop = pop_data.iloc[i]['OverFifty']
code = pop_data.iloc[i]['GEOID']
if pop_type =="covid":
pop = pop_data.iloc[i]['cases']
code = pop_data.iloc[i].ZCTA5CE10
pop_centroid = pop_centroid.append({'code':code,'pop': pop,'geometry': point}, ignore_index=True)
i = i+1
return(pop_centroid)
Function written by Joe Holler + Derrick Burt. It is a more efficient way to calculate distance-weighted catchment areas for each hospital. The algorithm runs quicker than the original one ("calculate_catchment_area"). It first creates a dictionary (with a node and its corresponding drive time from the hospital) of all nodes within a 30 minute drive time (using single_cource_dijkstra_path_length function). From here, two more dictionaries are constructed by querying the original one. From this dictionaries, single part convex hulls are created for each drive time interval and appended into a single list (one list with 3 polygon geometries). Within the list, the polygons are differenced from each other to produce three catchment areas.
Args:
Returns:
def dijkstra_cca_polygons(G, nearest_osm, distances, distance_unit = "travel_time"):
'''
Before running: must assign point geometries to street nodes
# create point geometries for the entire graph
for node, data in G.nodes(data=True):
data['geometry']=Point(data['x'], data['y'])
'''
## CREATE DICTIONARIES
# create dictionary of nearest nodes
nearest_nodes_30 = nx.single_source_dijkstra_path_length(G, nearest_osm, distances[2], distance_unit) # creating the largest graph from which 10 and 20 minute drive times can be extracted from
# extract values within 20 and 10 (respectively) minutes drive times
nearest_nodes_20 = dict()
nearest_nodes_10 = dict()
for key, value in nearest_nodes_30.items():
if value <= distances[1]:
nearest_nodes_20[key] = value
if value <= distances[0]:
nearest_nodes_10[key] = value
## CREATE POLYGONS FOR 3 DISTANCE CATEGORIES (10 min, 20 min, 30 min)
# 30 MIN
# If the graph already has a geometry attribute with point data,
# this line will create a GeoPandas GeoDataFrame from the nearest_nodes_30 dictionary
points_30 = gpd.GeoDataFrame(gpd.GeoSeries(nx.get_node_attributes(G.subgraph(nearest_nodes_30), 'geometry')))
# This line converts the nearest_nodes_30 dictionary into a Pandas data frame and joins it to points
# left_index=True and right_index=True are options for merge() to join on the index values
points_30 = points_30.merge(pd.Series(nearest_nodes_30).to_frame(), left_index=True, right_index=True)
# Re-name the columns and set the geodataframe geometry to the geometry column
points_30 = points_30.rename(columns={'0_x':'geometry','0_y':'z'}).set_geometry('geometry')
# Create a convex hull polygon from the points
polygon_30 = gpd.GeoDataFrame(gpd.GeoSeries(points_30.unary_union.convex_hull))
polygon_30 = polygon_30.rename(columns={0:'geometry'}).set_geometry('geometry')
# 20 MIN
# Select nodes less than or equal to 20
points_20 = points_30.query("z <= 1200")
# Create a convex hull polygon from the points
polygon_20 = gpd.GeoDataFrame(gpd.GeoSeries(points_20.unary_union.convex_hull))
polygon_20 = polygon_20.rename(columns={0:'geometry'}).set_geometry('geometry')
# 10 MIN
# Select nodes less than or equal to 10
points_10 = points_30.query("z <= 600")
# Create a convex hull polygon from the points
polygon_10 = gpd.GeoDataFrame(gpd.GeoSeries(points_10.unary_union.convex_hull))
polygon_10 = polygon_10.rename(columns={0:'geometry'}).set_geometry('geometry')
# Create empty list and append polygons
polygons = []
# Append
polygons.append(polygon_10)
polygons.append(polygon_20)
polygons.append(polygon_30)
# Clip the overlapping distance ploygons (create two donuts + hole)
for i in reversed(range(1, len(distances))):
polygons[i] = gpd.overlay(polygons[i], polygons[i-1], how="difference")
return polygons
Measures the effect of a single hospital on the surrounding area. (Uses dijkstra_cca_polygons)
Args:
Returns:
def hospital_measure_acc (_thread_id, hospital, pop_data, distances, weights):
# Create polygons
polygons = dijkstra_cca_polygons(G, hospital['nearest_osm'], distances)
# Calculate accessibility measurements
num_pops = []
for j in pop_data.index:
point = pop_data['geometry'][j]
# Multiply polygons by weights
for k in range(len(polygons)):
if len(polygons[k]) > 0: # To exclude the weirdo (convex hull is not polygon)
if (point.within(polygons[k].iloc[0]["geometry"])):
num_pops.append(pop_data['pop'][j]*weights[k])
total_pop = sum(num_pops)
for i in range(len(distances)):
polygons[i]['time']=distances[i]
polygons[i]['total_pop']=total_pop
polygons[i]['hospital_icu_beds'] = float(hospital['Adult ICU'])/polygons[i]['total_pop'] # proportion of # of beds over pops in 10 mins
polygons[i]['hospital_vents'] = float(hospital['Total Vent'])/polygons[i]['total_pop'] # proportion of # of beds over pops in 10 mins
polygons[i].crs = { 'init' : 'epsg:4326'}
polygons[i] = polygons[i].to_crs({'init':'epsg:32616'})
print('{:.0f}'.format(_thread_id), end=" ", flush=True)
return(_thread_id, [ polygon.copy(deep=True) for polygon in polygons ])
Parallel implementation of accessibility measurement.
Args:
Returns:
def hospital_acc_unpacker(args):
return hospital_measure_acc(*args)
# WHERE THE RESULTS ARE POOLED AND THEN REAGGREGATED
def measure_acc_par (hospitals, pop_data, network, distances, weights, num_proc = 4):
catchments = []
for distance in distances:
catchments.append(gpd.GeoDataFrame())
pool = mp.Pool(processes = num_proc)
hospital_list = [ hospitals.iloc[i] for i in range(len(hospitals)) ]
print("Calculating", len(hospital_list), "hospital catchments...\ncompleted number:", end=" ")
results = pool.map(hospital_acc_unpacker, zip(range(len(hospital_list)), hospital_list, itertools.repeat(pop_data), itertools.repeat(distances), itertools.repeat(weights)))
pool.close()
results.sort()
results = [ r[1] for r in results ]
for i in range(len(results)):
for j in range(len(distances)):
catchments[j] = catchments[j].append(results[i][j], sort=False)
return catchments
Calculates and aggregates accessibility statistics for one catchment on our grid file.
Args:
Returns:
from collections import Counter
def overlap_calc(_id, poly, grid_file, weight, service_type):
value_dict = Counter()
if type(poly.iloc[0][service_type])!=type(None):
value = float(poly[service_type])*weight
intersect = gpd.overlay(grid_file, poly, how='intersection')
intersect['overlapped']= intersect.area
intersect['percent'] = intersect['overlapped']/intersect['area']
intersect=intersect[intersect['percent']>=0.5]
intersect_region = intersect['id']
for intersect_id in intersect_region:
try:
value_dict[intersect_id] +=value
except:
value_dict[intersect_id] = value
return(_id, value_dict)
def overlap_calc_unpacker(args):
return overlap_calc(*args)
Calculates how all catchment areas overlap with and affect the accessibility of each grid in our grid file.
Args:
Returns:
def overlapping_function (grid_file, catchments, service_type, weights, num_proc = 4):
grid_file[service_type]=0
pool = mp.Pool(processes = num_proc)
acc_list = []
for i in range(len(catchments)):
acc_list.extend([ catchments[i][j:j+1] for j in range(len(catchments[i])) ])
acc_weights = []
for i in range(len(catchments)):
acc_weights.extend( [weights[i]]*len(catchments[i]) )
results = pool.map(overlap_calc_unpacker, zip(range(len(acc_list)), acc_list, itertools.repeat(grid_file), acc_weights, itertools.repeat(service_type)))
pool.close()
results.sort()
results = [ r[1] for r in results ]
service_values = results[0]
for result in results[1:]:
service_values+=result
for intersect_id, value in service_values.items():
grid_file.loc[grid_file['id']==intersect_id, service_type] += value
return(grid_file)
Normalizes our result (Geodataframe) for a given resource (res).
def normalization (result, res):
result[res]=(result[res]-min(result[res]))/(max(result[res])-min(result[res]))
return result
Imports all files we need to run our code and pulls the Illinois network from OSMNX if it is not present (will take a while).
NOTE: even if we calculate accessibility for just Chicago, we want to use the Illinois network (or at least we should not use the Chicago network) because using the Chicago network will result in hospitals near but outside of Chicago having an infinite distance (unreachable because roads do not extend past Chicago).
Args:
Returns:
def output_map(output_grid, base_map, hospitals, resource):
ax=output_grid.plot(column=resource, cmap='PuBuGn',figsize=(18,12), legend=True, zorder=1)
# Next two lines set bounds for our x- and y-axes because it looks like there's a weird
# Point at the bottom left of the map that's messing up our frame (Maja)
ax.set_xlim([314000, 370000])
ax.set_ylim([540000, 616000])
base_map.plot(ax=ax, facecolor="none", edgecolor='gray', lw=0.1)
hospitals.plot(ax=ax, markersize=10, zorder=1, c='blue')
Below you can customize the input of the model:
import ipywidgets
from IPython.display import display
processor_dropdown = ipywidgets.Dropdown( options=[("1", 1), ("2", 2), ("3", 3), ("4", 4)],
value = 4, description = "Processor: ")
population_dropdown = ipywidgets.Dropdown( options=[("Population at Risk", "pop"), ("COVID-19 Patients", "covid") ],
value = "pop", description = "Population: ")
resource_dropdown = ipywidgets.Dropdown( options=[("ICU Beds", "hospital_icu_beds"), ("Ventilators", "hospital_vents") ],
value = "hospital_icu_beds", description = "Resource: ")
hospital_dropdown = ipywidgets.Dropdown( options=[("All hospitals", "hospitals"), ("Subset", "hospital_subset") ],
value = "hospitals", description = "Hospital:")
display(processor_dropdown,population_dropdown,resource_dropdown,hospital_dropdown)
Dropdown(description='Processor: ', index=3, options=(('1', 1), ('2', 2), ('3', 3), ('4', 4)), value=4)
Dropdown(description='Population: ', options=(('Population at Risk', 'pop'), ('COVID-19 Patients', 'covid')), …
Dropdown(description='Resource: ', options=(('ICU Beds', 'hospital_icu_beds'), ('Ventilators', 'hospital_vents…
Dropdown(description='Hospital:', options=(('All hospitals', 'hospitals'), ('Subset', 'hospital_subset')), val…
if population_dropdown.value == "pop":
pop_data = pop_centroid(atrisk_data, population_dropdown.value)
elif population_dropdown.value == "covid":
pop_data = pop_centroid(covid_data, population_dropdown.value)
distances=[600, 1200, 1800] # Distances in travel time
weights=[1.0, 0.68, 0.22] # Weights where weights[0] is applied to distances[0]
# Other weighting options representing different distance decays
# weights1, weights2, weights3 = [1.0, 0.42, 0.09], [1.0, 0.75, 0.5], [1.0, 0.5, 0.1]
# it is surprising how long this function takes just to calculate centroids.
# why not do it with the geopandas/pandas functions rather than iterating through every item?
Pop Centroid File Setting: 100%|██████████| 86/86 [00:00<00:00, 147.42it/s]
If you have already run this code and changed the Hospital selection, rerun the Load Hospital Data block.
# Set hospitals according to hospital dropdown
if hospital_dropdown.value == "hospital_subset":
hospitals = hospital_setting(hospitals[:1], G)
else:
hospitals = hospital_setting(hospitals, G)
resources = ["hospital_icu_beds", "hospital_vents"] # resources
# this is also slower than it needs to be; if network nodes and hospitals are both
# geopandas data frames, it should be possible to do a much faster spatial join rather than iterating through every hospital
Find the nearest network node from hospitals: 100%|██████████| 66/66 [01:27<00:00, 1.32s/it]
hospital setting is done
# Create point geometries for entire graph
# what is the pupose of the following two lines? Can this be deleted?
# for node, data in G.nodes(data=True):
# data['geometry']=Point(data['x'], data['y'])
# which hospital to visualize?
fighosp = 7
# Create catchment for hospital 0
poly = dijkstra_cca_polygons(G, hospitals['nearest_osm'][fighosp], distances)
# Reproject polygons
for i in range(len(poly)):
poly[i].crs = { 'init' : 'epsg:4326'}
poly[i] = poly[i].to_crs({'init':'epsg:32616'})
# Reproject hospitals
# Possible to map from the hospitals data rather than creating hospital_subset?
hospital_subset = hospitals.iloc[[fighosp]].to_crs(epsg=32616)
fig, ax = plt.subplots(figsize=(12,8))
min_10 = poly[0].plot(ax=ax, color="royalblue", label="10 min drive")
min_20 = poly[1].plot(ax=ax, color="cornflowerblue", label="20 min drive")
min_30 = poly[2].plot(ax=ax, color="lightsteelblue", label="30 min drive")
hospital_subset.plot(ax=ax, color="red", legend=True, label = "hospital")
# Add legend
ax.legend()
<matplotlib.legend.Legend at 0x7ff94c5fef10>
poly
[ geometry
0 POLYGON ((443456.283 4609874.589, 441585.172 4...,
geometry
0 POLYGON ((433443.581 4600237.316, 427780.923 4...,
geometry
0 POLYGON ((438932.445 4588484.312, 431706.358 4...]
%%time
catchments = measure_acc_par(hospitals, pop_data, G, distances, weights, num_proc=processor_dropdown.value)
Calculating 66 hospital catchments... completed number: 5 15 0 10 6 1 16 11 7 2 17 12 3 8 18 13 4 9 19 14 20 25 30 21 35 26 31 36 22 27 32 37 28 23 33 38 29 24 34 39 40 45 55 50 41 46 56 42 51 47 57 43 48 52 58 44 49 53 59 60 65 54 61 62 63 64 CPU times: user 2.37 s, sys: 517 ms, total: 2.89 s Wall time: 56 s
%%time
for j in range(len(catchments)):
catchments[j] = catchments[j][catchments[j][resource_dropdown.value]!=float('inf')]
result=overlapping_function(grid_file, catchments, resource_dropdown.value, weights, num_proc=processor_dropdown.value)
CPU times: user 6.61 s, sys: 435 ms, total: 7.05 s Wall time: 18.4 s
%%time
result = normalization (result, resource_dropdown.value)
CPU times: user 2.47 ms, sys: 0 ns, total: 2.47 ms Wall time: 2.24 ms
result.head()
| left | top | right | bottom | id | area | geometry | hospital_icu_beds | hospital_vents | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 440843.416087 | 4.638515e+06 | 441420.766356 | 4.638015e+06 | 4158 | 216661.173 | POLYGON ((440843.416 4638265.403, 440987.754 4... | 0.891964 | 0.902812 |
| 1 | 440843.416087 | 4.638015e+06 | 441420.766356 | 4.637515e+06 | 4159 | 216661.168 | POLYGON ((440843.416 4637765.403, 440987.754 4... | 0.925806 | 0.929226 |
| 2 | 440843.416087 | 4.639515e+06 | 441420.766356 | 4.639015e+06 | 4156 | 216661.169 | POLYGON ((440843.416 4639265.403, 440987.754 4... | 0.919677 | 0.928904 |
| 3 | 440843.416087 | 4.639015e+06 | 441420.766356 | 4.638515e+06 | 4157 | 216661.171 | POLYGON ((440843.416 4638765.403, 440987.754 4... | 0.904288 | 0.910442 |
| 4 | 440843.416087 | 4.640515e+06 | 441420.766356 | 4.640015e+06 | 4154 | 216661.171 | POLYGON ((440843.416 4640265.403, 440987.754 4... | 0.942553 | 0.953108 |
To assess the results of our changes to the speed limits, we compare the final accessibility score maps for the four combinations of input variables to the maps from the first reproduction. This comparison isolates the impact of modifying the speed limits due to the inclusion of a buffer in the reproduction that would also change results from the original study.
The speed limit modification changes all four of the maps. The maps for both Covid-19 cases only change by a few pixels from the original reproduction, whereas the population at risk cases change much more from the first reproduction. For the population at risk/ICU bed map, the modified speed limit map has a smaller zone of highest accessibility and the zones don’t extend as far to the north(west). For the modified maps for both population types, there minute changes between ICU beds and ventilators, which confirms our hypothesis that these amounts are correlated (i.e. approximately one ventilator per one hospital bed) and won’t change the analysis very much.
There are several geographic threats to validity (as established by Schmitt 1978) in the original study, including some that were resolved through changes implemented in the reproductions and some that may not be resolvable with the available data.
Boundary distortions are present on both the city and state level scales in the original study. Ignoring hospitals or roads outside of these boundaries ignores the reality that there may be closer hospitals outside of these boundaries that a person would more likely go to than a hospital within this boundary. This distortion is rectified on the city-scale by including a buffer around the city limits to include hospitals and roads whose catchment areas overlap with the city. Boundary distortion for the state of Illinois may not be resolvable due to the inconsistency of Covid data collection between states, so introducing a buffer only introduces more uncertainty. It may be possible to look at accessibility for the population at risk for Illinois using a buffer because this value comes from Census data, which should be more consistent across state borders.
Partition distortion is another major geographic threat to the validity of this study. Using centroids for the population at risk and the Covid-19 population is an oversimplification of the spatial distribution of these populations. Furthermore, a hospital catchment area has to overlap by more than 50% a population polygon to consider that polygon in the catchment area. Although this threat is not resolved in this reproduction, other students implemented an area weighted reaggregation to include more spatial complexity in this calculation.
Modifying the speed limits was an effort to decrease the impact of space-time interaction threats to the validity of the study. However, this study still only considers personal vehicles in the model and not other forms of transportation (public transportation, bike, and walking). Including multimodal networks would significantly increase the computing time of the study and may not be feasible for many researchers. Furthermore, traffic congestion, which changes spatial accessibility throughout the day, is not factored into the model. Even though the accessibility is normalized on a 0 to 1 scale, congestion may change spatial distributions. During the beginning stages of the pandemic, traffic congestion was minimal, so this was not a priority to address in this specific reproduction.
For the population at risk and ICU bed map, the modified speed limit map has a smaller zone of highest accessibility and the zones don’t extend as far to the north(west). When modifying the speed limits, the average speed limit generally decreased. We hypothesize that because the majority of roads are residential, the majority of roads should have speed limits under 25mph. However, due to the default speed limit of 30mph (in the reproduction, 35mph in the original), the original reproduction study has the most common speed limit of 30-35mph. Overall, modifying the speed limits lowered the catchment areas from the hospitals, so the accessibility zones contracted as well.
The connection between the population type and the speed limits remains unclear. The population at risk is higher than the population with Covid-19, so the service to population ratio will be smaller. Perhaps this is why the accessibility zones on the modified speed limit map got smaller, but we would expect the ratio for Covid-19 to still change as well.
%%time
hospitals = hospitals.to_crs({'init': 'epsg:26971'})
result = result.to_crs({'init': 'epsg:26971'})
output_map(result, pop_data, hospitals, resource_dropdown.value)
CPU times: user 1.61 s, sys: 291 ms, total: 1.91 s Wall time: 1.51 s
def output_map_classified(output_grid, hospitals, resource):
ax=output_grid.plot(column=resource,
scheme='Equal_Interval',
k=5,
linewidth=0,
cmap='Blues',
figsize=(18,12),
legend=True,
label="Acc Measure",
zorder=1)
# Next two lines set bounds for our x- and y-axes because it looks like there's a weird
# Point at the bottom left of the map that's messing up our frame (Maja)
ax.set_xlim([325000, 370000])
ax.set_ylim([550000, 600000])
hospitals.plot(ax=ax,
markersize=10,
zorder=2,
c='black',
legend=True,
label="Hospital"
)
# ax.legend(loc="upper right") # add hospital legend
output_map_classified(result, hospitals, resource_dropdown.value)
# save as image with file name including the resource value, population value, and buffered / not buffered
plt.savefig('./results/figures/reproduction/{}_{}_buff_classified_spdLimit.png'.format(population_dropdown.value, resource_dropdown.value))
Comparing the reproduced accessibility maps to the original maps, the accessibility zones are in the same general areas (north central Chicago being the most accessible and south Chicago being the least). However, buffering the city and correcting the speed limits changes the scores of individual census tracts. The original study results can be interpreted for general regional trends but not for specific cores at the tract level.
Luo, W., & Qi, Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & place, 15(4), 1100-1107.