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.

翻译：城市街道是重要的日常健康基础设施，但其支撑体力活动的空间能力分布并不均衡。本研究通过融合列斐伏尔的空间三元组理论、多源城市数据与基于SHAP的分析方法，构建了一个兼具理论依据与可解释性的框架，用以诊断街道层面的锻炼剥夺现象。以深圳市为案例，我们发现，虽然概念性空间属性对锻炼强度整体影响最大，但地方性剥夺机制在不同情境下存在显著差异。我们识别出七种剥夺模式类型，并将高需求、低支持的街道区段定位为优先干预区域。本研究为促进日常体力活动中的空间正义，既提供了具有理论基础的（概念性）分析框架，也提供了实用的诊断工具。