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

arXiv: https://arxiv.org/abs/2507.03570 · HTML: https://mlsanity.com/paper/2507.03570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 58 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models](https://mlsanity.com/paper/2007.04131/) (2020)
- [UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction](https://mlsanity.com/paper/2504.04739/) (2025)

## Abstract (arXiv)

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.