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

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.

Authors

Institutions

Publication Details

Journal
Social Science & Medicine
Published
2024-09-21
DOI
https://doi.org/10.1016/j.socscimed.2024.117361
Citations
2
Primary Topic
Urban Transport and Accessibility
Type
article
Field-Weighted Citation Impact
1.37
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Tianjie Zhang, Cailin Qiu, Jianquan Cheng, Yi Lü
2 citations
Social Science & Medicine
Urban Transport and Accessibility
1.37
article

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

Tianjie Zhang, Cailin Qiu, Jianquan Cheng, Yi Lü
article en
2 citations

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.

Social Science & MedicineVol. 361
Manchester Metropolitan University (GB), Tianjin University (CN), Tianjin Chengjian University (CN), City University of Hong Kong (HK), City University of Hong Kong, Shenzhen Research Institute (CN)
Sustainable cities and communities
Openalex Percentile: Top 16%
Urban Transport and Accessibility
1.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.