Case04B2-Delineating Hospital Service Areas by Spatialized Network Community Detection Methods

Chapter 4 Delineating Functional Regions and Application in Health Geography

Case study 4A illustrates how to use the proximal area method and the Huff model to estimate the service areas of acute hospitals in Baton Rouge. Case study 4B implements the Dartmouth method that pioneered the delineation of hospital service areas (HSAs) by a simple plurality rule, and the network community detection approach to define HSAs with maximal patient flows within HSAs and minimal flows between HSAs. It relies on data of observed hospitalization service volumes between residents and hospitals in Florida.

Case Study 4B: Automated Delineation of Hospital Service Areas in Florida

This case study uses toolkits developed by Python Script node to delineate the hospital service areas in Florida. It uses three methods: Huff model, Dartmouth method, and Network Community Detection methods. The folder Florida contains all data for case study 4B listed as follows:

1) subfolder FL _ HSA contains a polygon feature class ZIP _ Code _Area.zip for 983 ZIP code areas in Florida, and a table for hospitalization volumes between these ZIP code areas OD _ All _ Flows.csv with 37,180 non-zero flows and a total service volume of 2,392,066 (refer to Case Study 2B on the hospitalization table details),

2) a CSV file FLplgnAdjAppend.csv is a spatial adjacency matrix, and another CSV file FLAdjUpdate.csv is the updated spatial adjacency matrix generated in Step 3 of Section 4.2.1, and

3) Python scripts in Network Community Detection Method for Python Script.py and Dartmouth Method for Python Script.py

Part 2: Delineating HSAs by Spatialized Network Community Detection Methods

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual

Lingbo Liu, Fahui Wang

External resources

• Github for Workbook Issue Report
• Github for Geospatial Analytics