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Evaluating Spatial Allocation of Resilient Medical Facilities in Megacities: A Case Study of Shanghai, China.
- Source :
- Systems; Feb2025, Vol. 13 Issue 2, p132, 20p
- Publication Year :
- 2025
-
Abstract
- In response to public health emergencies, the importance of resilient medical facilities—such as large exhibition centers and sports venues that can be rapidly converted into temporary emergency hospitals—has become increasingly evident. This study focuses on these potential medical resources within megacities, using Shanghai as a case study. Employing advanced analytical tools including the kernel density two-step floating catchment area method, Lorenz curves, and Gini coefficient, we systematically evaluated the spatial allocation of 36 resilient medical facilities in Shanghai. The findings indicate that Shanghai's resilient medical facilities ensure 4.5 emergency beds per thousand residents, with large exhibition centers and sports venues demonstrating exceptional conversion capabilities far surpassing those of ordinary public venues. However, the study also uncovers significant disparities in spatial allocation: these facilities are predominantly concentrated in the city center, leading to notable deficiencies in accessibility and equity for suburban areas. The opportunity for residents to access emergency medical services decreases progressively from the city center outward, a trend consistent across various travel time scenarios. The calculated Gini coefficient underscores an extremely uneven spatial configuration of resilient medical facilities, well beyond the warning threshold, suggesting substantial service disparities between different regions during emergencies, which poses potential risks to public health. Our research provides valuable insights for megacities aiming to more effectively address future public health challenges and enhance societal resilience. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20798954
- Volume :
- 13
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Systems
- Publication Type :
- Academic Journal
- Accession number :
- 183335208
- Full Text :
- https://doi.org/10.3390/systems13020132