Chapter 2 Measuring Distance and Time and Analyzing Distance Decay Behavior
This chapter uses two case studies to illustrate how to implement two common tasks encountered most often in spatial analysis: estimating a travel distance or time matrix and modeling distance decay behaviors. Case study 2A shows how to mea?sure distances and travel times between residents at the census block group level and acute hospitals in Baton Rouge, Louisiana. Case study 2B uses hospitalization data in Florida to demonstrate how to derive the best ? tting distance decay function by the spatial interaction model or the complementary cumulative distribution curve.
Case Study 2B:Analyzing Distance Decay Behavior for Hospitalization in Florida
This case study is based on a project reported in Wang and Wang (2022), and examines the distance decay rule via two approaches, namely the spatial interaction model and the complementary cumulative distribution curve. The main data source is the State Inpatient Database (SID) in Florida in 2011 from the Healthcare Cost and Utilization Project (HCUP) sponsored by the Agency for Healthcare Research and Quality (AHRQ, 2011). The SID dataset includes individual inpatient discharge records from hospitals, which are aggregated to OD inpatient volumes between resi?dential ZIP code areas and hospitals.
The case study uses an OD flow table OD _ All _ Flows.csv with 209,379 flows in the folder Florida , with the following fields (columns):
(1) Hosp _ ZoneID , Hosp _ X _ MC , and Hosp _ Y _ MC for each hospi?tal’s ID and XY-coordinates, and NUMBEDS for its staffed bed size,
(2) PatientZipZoneID , Xcoord , and Ycoord for each ZIP code area’s ID and XY-coordinates, and POPU for its population, and
(3) AllFlows and Total _ Time _ Min for patient service flow volume and drive time (minutes) on each OD pair.
Computational Methods and GIS Applications in Social Science - KNIME Lab Manual
Lingbo Liu, Fahui Wang
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Case02B-Analyzing Distance Decay Behavior for Hospitalization in Florida
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