Developing an epidemic-adjusted walkability index to analyze pandemic-resilient urban areas: a data driven GIS-based multi-criteria approach

The infectious disease pandemic highlighted the critical role of the spatial structure of cities in shaping infectious-disease transmission dynamics. This study develops a health-oriented framework to investigate the spatiotemporal association between urban walkability and the incidence of the recent global infectious disease pandemic (COVID-19) in New York City. First, a baseline walkability index is constructed using 24 spatial indicators representing accessibility, land use, sidewalk facilities, street connectivity, and safety; utilizing a Spatial Multi-Criteria Analysis (SMCA) framework. Next, global (Ordinary Least Squares) and local (Geographically Weighted Regression) spatial regression models are implemented to investigate the relationship between walkability indicators and weekly COVID-19 case rates. Finally, an epidemic-adjusted walkability index is derived by re-weighting indicators according to their observed spatial associations with COVID-19 incidence. Results reveal a statistically significant inverse association between the synthesized walkability Index and COVID-19 incidence (correlation coefficients ranging from − 0.50 to − 0.81). Areas characterized by high street connectivity, diversified mobility options, and greater access to urban facilities such as green spaces, healthcare, education, and public services were generally associated with lower reported COVID-19 incidence rates. The proposed framework demonstrates how urban-form metrics and spatial regression models can be integrated to examine built-environment associations with epidemic outcomes and support exploratory planning for pandemic-resilient cities.