Enhancing Walkability in Shanghai, China: Integrating the Spatial Analysis of Built Environment Design Factors at the Macro- and Street Levels

Abstract

The streetscape features related to pedestrian activity have not been studied as extensively as the macrolevel built environment characteristics measured by the D variables. In addition, existing studies have not adequately taken spatial autocorrelation into account. This study incorporates the built environment design factors at both macro- and street levels in Shanghai, China, to assess their spatially varying relationships with pedestrian activity. We extracted information from Baidu Street View images to create variables that capture characteristics of streetscape and macrolevel built environment design and examined their links with pedestrian activity using geographically weighted Poisson regression (GWPR) models. We found that two conventionally used D variables—design and diversity—are not significantly associated with pedestrian counts in Shanghai. The improved explanation power in the GWPR models highlighted the significance of controlling for spatial autocorrelation. The presence of the first-floor windows and a higher density of pedestrian generators are associated with the high–high clusters of pedestrian counts. Gated communities, a common urban typology in China, are prevalent in areas characterized by the low–low clusters of pedestrian counts. Our results confirmed the significance of integrating streetscape features when examining the built environment characteristics in explaining pedestrian activity and suggested re-evaluations of recommendations solely derived from studies conducted in American cities within the context of China.

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