Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data.

ABSTRACT:

Introduction

University campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, and nearby communities. However, the mechanisms through which campus environments influence walking activities remain insufficiently understood. This study examines universities in Wuhan, China, using crowdsourced data and machine learning methods to analyze the nonlinear and interactive effects of campus built environments on exercise walking.

Methods

This study utilized crowdsourced exercise walking data and incorporated diverse campus characteristics to construct a multidimensional variable system. By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning fram

SUBMITTER: Lu B 

PROVIDER: S-EPMC11821617 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature