**Author**: Jeon-Young Kang 1,2, Alexander Michels 1,3, Fanzheng Lyu1,2, Shaohua Wang1,2 , Nelson Agbodo4, Vincent Freeman5, Shaowen Wang1,2,3,*
1 CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at UrbanaChampaign, Urbana, Illinois
2 Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, Illinois
3 Illinois Informatics Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois
4 Division of Health Data and Policy, Illinois Department of Public Health, Springfield, Illinois
5 Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois
* Corresponding author: shaowen@illinois.edu
CyberGIS Center for Advanced Digital and Spatial Studies
CyberInfrastructure & Geospatial Information Laboratory
The accessibility measure calculated here is updated daily on https://wherecovid19.cigi.illinois.edu/¶
You can checkout the paper here: https://doi.org/10.1186/s12942-020-00229-x¶
Reference¶
Kang, J. Y., Michels, A. C., Lyu, F., Wang, S., Agbodo, N., Freeman, V. L., & Wang, S. (2020). Rapidly Measuring Spatial Accessibility of COVID-19 Healthcare Resources: A Case Study of Illinois, USA. medRxiv https://doi.org/10.1101/2020.05.06.20093534
Objectives¶
This aims to measure spatial access for people to hospitals in Illinois. The spatial accessibiilty is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). As a result, the map of spatial accessibility will be produced. It identifies which regions need more healthcare resources, such as the number of beds or physicians. This notebook would serve as a guideline of which regions need more beds for fighting against COVID-19.
Data¶
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, hosptial information is also publically available on the Homelanad Infrastructure Foundation-Level Data.
Method - An enhanced two-step floating catchment area (E2FCA)¶
The catchement area for each hospital will be delineated by travel times. People living in the overlappying regions by multiple hospitals' catchment area are more accessible to hospitals than people living in other places.
Codes¶
import necessary librareis to run this model.
import pandas as pd
import numpy as np
import geopandas as gpd
import networkx as nx
import osmnx as ox
from shapely.geometry import Point, LineString, Polygon
import matplotlib.pyplot as plt
from tqdm import tqdm
import multiprocessing as mp
import folium, itertools, os, time, warnings
from IPython.display import display, clear_output
warnings.filterwarnings("ignore")
pop_data = gpd.read_file('./Data/PopData/Chicago_Tract.shp')
pop_data.head()
Hospital Data¶
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.
hospitals = gpd.read_file('./Data/HospitalData/Chicago_Hospital_Info.shp')
hospitals.head()
Generate and Plot Map of Hospitals¶
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='grey',
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
Load and Plot Hexagon Grids (500-meter resolution)¶
grid_file = gpd.read_file('./Data/GridFile/Chicago_Grid.shp')
grid_file.plot(figsize=(8,8))
Load and Plot the Street Network¶
if not os.path.exists("Data/Chicago_Network.graphml"):
G = ox.graph_from_place('Chicago', network_type='drive') # pulling the drive network the first time will take a while
ox.save_graphml(G, 'Chicago_Network.graphml', folder="Data")
else:
G = ox.load_graphml('Chicago_Network.graphml', folder="Data", node_type=str)
ox.plot_graph(G, fig_height=10)