Customized decision-making support per location’s characteristics of spatial accessibility: a case study of healthcare resources in New York City

Dynamic spatial accessibility, incorporating temporal dimension into spatial accessibility measurements, is in the spotlight because of significant temporal variations in urban phenomena (e.g., traffic congestion or floating population). Despite its importance, there are two significant gaps, particularly in existing healthcare access literature: 1) lack of consideration of the dynamic nature of spatial accessibility, and 2) limited support of dynamic spatial accessibility for practical policymaking. Our study aims to improve the measurements of spatial accessibility with time-dependent data and summarize their temporal sequences for effective decision-making support. Specifically, we measure hourly changes in spatial accessibility to healthcare resources in New York City with a Gaussian two-step floating catchment area (G2SFCA) method. We then employ sequence analysis to detect and cluster unique temporal changes observed in the measures of accessibility. Our results reveal that each location provides distinct temporal changes in their healthcare accessibility owing to the dynamic urban phenomena. In addition, we find that places can be grouped into 14 clusters based on the combination of temporal changes (i.e., stationary and non-stationary) and the different levels (e.g., high, moderate, or low) of accessibility. Therefore, our findings can support flexible policy implications for the various needs of locations. For example, to have better accessibility, certain areas could extend the operating hours of their existing healthcare resources, or other locations should be provided with additional hospitals in place. Given that decision-making is often based on limited resources, our study sheds light on how to make optimal allocation of healthcare resources to effectively reduce inequality of access.