Automatic delineation of rational service areas and health professional shortage areas in GIS based on human movements and health resources

Abstract How people travel to receive health services is essential for understanding healthcare shortages. The rational service areas (RSAs) are defined to represent local healthcare markets and used as the basic units to evaluate whether people have access to health resources. Therefore, finding an...

Full description

Saved in:
Bibliographic Details
Published inTransactions in GIS Vol. 28; no. 6; pp. 1639 - 1661
Main Authors Liang, Yunlei, Gao, Song
Format Journal Article
LanguageEnglish
Published 01.09.2024
Online AccessGet full text

Cover

Loading…
More Information
Summary:Abstract How people travel to receive health services is essential for understanding healthcare shortages. The rational service areas (RSAs) are defined to represent local healthcare markets and used as the basic units to evaluate whether people have access to health resources. Therefore, finding an appropriate way to develop RSAs is important for understanding the utilization of health resources and supporting accurate resource allocation to the health professional shortage areas (HPSAs). Existing RSAs are usually developed based on the local knowledge of public health needs and are created through time‐intensive manual work by health service officials. In this research, a travel data‐driven and spatially constrained community detection method based on human mobility flow is proposed to automate the process of establishing the statewide RSAs and further identifying HPSAs based on healthcare criteria in a geographic information system (GIS) software. The proposed method considers the difference between rural and urban populations by assigning different parameters and delineates RSAs with the goal of reducing health resource inequalities faced by rural areas. Using the data in the State of Wisconsin, our experiment shows that the proposed RSA delineation method outperforms other baselines including the traditional Dartmouth method in the aspects of RSA compactness, region size balances, and health shortage scores. Furthermore, the whole process of delineating RSAs and identifying HPSAs is automated using Python toolboxes in ArcGIS to support future analyses and practices in a timely and repeatable manner.
ISSN:1361-1682
1467-9671
DOI:10.1111/tgis.13207