From Street Form to Spatial Justice: Explaining Urban Exercise Inequality via a Triadic SHAP-Informed Framework

TL;DR

This paper introduces an explainable, theory-informed framework combining urban data and SHAP analysis to diagnose street-level exercise deprivation, aiming to promote spatial justice in physical activity infrastructure.

Contribution

It develops a novel triadic SHAP-informed framework integrating Lefebvre's spatial theory with multi-source data for diagnosing urban exercise inequality.

Findings

Conceived spatial attributes most influence exercise intensity

Identified seven deprivation typologies in urban streets

Pinpointed high-demand, low-support street segments for intervention

Abstract

Urban streets are essential everyday health infrastructure, yet their capacity to support physical activity is unevenly distributed. This study develops a theory-informed and explainable framework to diagnose street-level exercise deprivation by integrating Lefebvre's spatial triad with multi-source urban data and SHAP-based analysis. Using Shenzhen as a case study, we show that while conceived spatial attributes have the strongest overall influence on exercise intensity, local deprivation mechanisms vary substantially across contexts. We identify a seven-mode typology of deprivation and locate high-demand but low-support street segments as priority areas for intervention. The study offers both a theory-grounded analytical framework and a practical diagnostic tool for promoting spatial justice in everyday physical activity.