Spatial Drivers of Urban Industrial Agglomeration Using Street View Imagery and Remote Sensing: A Case Study of Shanghai

Jiaqi Zhang, Zhen He, Weijing Wang () and Ziwen Sun ()
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Jiaqi Zhang: Edinburgh School of Architecture and Landscape Architecture, Edinburgh College of Art, University of Edinburgh, 74 Lauriston Place, Edinburgh EH3 9DF, UK
Zhen He: Independent Researcher, Shanghai 200093, China
Weijing Wang: Future Cities Laboratory Global, Singapore-ETH Centre, 1 Create Way, CREATE Tower, Singapore 138602, Singapore
Ziwen Sun: School of Design and Arts, Beijing Institute of Technology, Beijing 102488, China

Land, 2025, vol. 14, issue 8, 1-26

Abstract: The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development.

Keywords: street view image (SVI); spatial data analysis; geographic information systems (GISs); XGBoost; SHAP interpretability analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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