Estimating exercisality on urban trails using physical exercise trajectory data and network-constrained approach

Qiu, Cailin, Cheng, Jianquan ORCID: https://orcid.org/0000-0001-9778-9009, Lu, Yi and Zhang, Tianjie (2024) Estimating exercisality on urban trails using physical exercise trajectory data and network-constrained approach. Social Science and Medicine. 117361. ISSN 0277-9536

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Abstract

Green exercise is a key aspect of urban vitality, supporting the hypothesis that increased physical exercise boosts urban vitality. Although research on urban vitality considers green space a crucial aspect, existing studies have concentrated on external functioning from the perspective of special systems, often overlooking the unique internal functioning associated with exercisers. This study proposed an original conceptual framework of exercisality, which is composed of four dimensions: density, diversity, time continuity and energy expenditure. Considering urban trails are publicly accessible and linear-type green infrastructure for residents to conduct and maintain regular and habitual green exercise, we have developed an innovative quantitative approach to estimate and validate exercisality on urban trails (EUT), by utilizing physical exercise trajectory data from the Keep APP across central Beijing in 2022. The hot spots of EUT were identified through the innovative method of local indicators of network-constrained clusters. It is argued that this new index of EUT which is scale independence when applied to exercise trajectory big data, generates data driven evidence to support human well-being.