Measuring Distance and Time and Analyzing Distance Decay Behavior - Liu, Lingbo; Wang, Fahui (Computational Methods and GIS Applications in Social Science - Lab Manual, 2024)
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 measure 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 fitting distance decay function by the spatial interaction model or the complementary cumulative distribution curve. This chapter uses few 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. The first study illustrates how to estimate various travel time matrices between census block groups and hospitals in East Baton Rouge Parish, simply referred to as Baton Rouge, Louisiana. The second study uses hospitalization data in Florida to demonstrate how to derive the best fitting distance decay function by the spatial interaction model or the complementary cumulative distribution curve. This study is based on a project reported in Wang and Wang, 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 in Florida in 2011 from the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality.