Measuring and optimizing spatial accessibility to emergency medical services with a focus on age-based spatial...

Emergency Medical Services (EMS) play a crucial role in safeguarding public health, yet their efficiency is highly dependent on spatial accessibility and optimal facility location. Existing research often evaluates EMS spatial accessibility using total population counts, overlooking the heterogeneity of EMS risk across space and age groups. This study aims to improve EMS accessibility measurement and spatial optimization by integrating spatial and demographic variations in EMS risk.

An integrated framework combining a modified two-step floating catchment area (2SFCA) model and two spatial optimization models-the Maximum Covering Location Problem (MCLP) and the P-median model-were developed. The improved models incorporate spatial and age-based EMS risk derived from 2024 EMS records in Wuhan, China. Comparative analyses were conducted between traditional and risk-integrated models to examine differences in accessibility and optimal station layouts.

Findings reveal significant age-related and spatial variations in EMS risk, with older adults populations showing markedly higher risks, and high-risk areas concentrated in central and near-suburban districts. Compared to traditional 2SFCA results, the improved model produced significantly different accessibility distributions, particularly in peri-urban areas. In optimization analyses, risk-integrated MCLP and P-median models located new EMS stations closer to near-urban areas, whereas traditional models favored peripheral suburbs.

Incorporating spatial and demographic variations in EMS risk substantially enhances the accuracy of accessibility measurement and optimization outcomes. The results underscore the importance of prioritizing high-risk older adults populations, strengthening EMS capacity in urban cores, and improving coverage in underserved suburbs. This framework offers a transferable tool for developing more efficient and equitable EMS systems in rapidly urbanizing and aging regions.