Explaining the Spatial Misalignment Between Routine Use and Experiential Attention Based on Multi-Source Data and Interpretable Machine Learning

Cheng Cheng

doi:10.3390/buildings16153002

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Explaining the Spatial Misalignment Between Routine Use and Experiential Attention Based on Multi-Source Data and Interpretable Machine Learning

2026-09-14

DOI：10.3390/buildings16153002

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Abstract

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As digital platforms reshape cities, urban spaces now host both routine functions and attention-driven experiential activities. However, traditional studies often conflate these distinct modes into aggregate activity intensity or urban vitality metrics. This study operationalizes spatial engagement through two proxy dimensions—routine use (RU) and digitally expressed experiential attention (EA)—and applies Ordinary Least Squares (OLS) and Random Forest SHapley Additive exPlanations (Random Forest SHAP) to multi-source behavioral and built-environment data from Shanghai to evaluate their spatial misalignment and built-environment associations. The results show that RU and EA are only moderately aligned and exhibit distinct spatial patterns. RU follows a more continuous center-to-periphery distribution, whereas EA is more selectively concentrated around landmarks and commercial destinations. Their built-environment associations also differ: RU is more strongly related to building density (β = 0.159) and functional mix (β = 0.226), while EA is more closely associated with service accessibility (β = 0.262) and syntactic integration (β = 0.210). In addition, under the random-split evaluation, RU is more readily predicted from the included built-environment variables, whereas the spatial block results indicate limited geographic transferability for EA and UAG. These findings provide a differentiated framework for moving beyond monolithic metrics and for interpreting relative contrasts between recurrent functional use and digitally expressed attention.

Keywords:

urban vitality

routine use

experiential attention

spatial misalignment

built environment

interpretable machine learning