Network constrained and classified spatial pattern analysis of healthcare facilities and their relationship with the road structure: a case study of Thiruvananthapuram city
Authors
Cynthia Daniel
Department of Civil Engneering, National Institute of Technology Trichy, Tiruchirappalli, India
Samson Mathew
Department of Civil Engneering, National Institute of Technology Trichy, Tiruchirappalli, India
Subbarayan Saravanan
Department of Civil Engneering, National Institute of Technology Trichy, Tiruchirappalli, India
Equity of the utilization of medical facilities is extremely dependant on the spatial distribution of healthcare amenities. In this study, an attempt is made to study the geographic distribution of the various categories of healthcare facilities in the city of Thiruvananthapuram, South India using the centrographic analysis that includes the Mean Center, Standard Deviational Ellipse and Average Nearest Neighbour and it was found that there is an uneven distribution. Network-based point pattern analysis is also performed and cross k-function is used to determine the distribution of medical shops relative to the healthcare services. A weighted node approach is used to calculate the indices of centrality by weighing all nodes based on their degree using Geographical Information System (GIS). Multi centrality assessment model consisting of betweenness, closeness and straightness centrality is used to compute the weighted road centrality on a local and global scale. Kernel Density Estimation technique is applied to modify the centrality values and the vector points to a basic raster framework. Correlation analysis was performed to find the role of the network centrality on the location of healthcare facilities in the study area. Correlation values are greatest among category 2 hospitals; straightness centrality and betweenness centrality favours the location of category 1 hospitals and homeopathy hospitals respectively. The results confirm that network topology influences the location of medical facilities in the study area.
Keywords: Healthcare facilities, Spatial pattern analysis, Network K-function, Road centrality, Kernel Density Estimation (KDE), Correlation analysis
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