Revealing the unequal availability and socioeconomic factors of health facilities based on taxi trajectory data and geospatial analysis
DOI:
https://doi.org/10.59490/ejtir.2026.26.3.7816Keywords:
healthcare resource accessibility, two-step floating catchment area, random forest geographically weighted regression, distance decay, urban inequalityAbstract
As China’s economy grows, healthcare equity concerns intensify. Historically, healthcare resource allocation often mismatches cities’ economic status, and their spatial distribution remains uneven. This study examines healthcare disparities at a grid level using a two-step floating catchment area (2SFCA) that accounts for distance decay. It utilizes big data and API technology to develop a more accurate distance decay function based on travel data, obtaining results that reflect the healthcare fairness. Focusing on Shenzhen, China’s largest city with a high number of immigrants and mobile population, it highlights a significant disparity between healthcare and other essential services despite rapid economic development. The approach initially fits a distance decay function with taxi GPS data, correlating driving behavior with hospital proximity. Subsequently, the 2SFCA is enhanced to assess healthcare facility accessibility accurately. Then, random forest geographically weighted regression models relationships among economic, physical environment, social dimensions, and healthcare accessibility in Shenzhen. Results indicate that aging and road network density exhibit the most extensive spatial influence on healthcare accessibility, both positively. These findings deepen understanding of healthcare resource inequality in metropolitan areas and support improved healthcare resource management and policy.
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Copyright (c) 2026 Yunying Liu, Huayang Liu, Zhaoqi Xu, Yiming Song, Yixin Wan, Xinyue Gu
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