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Article: Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston

TitleAssessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston
Authors
KeywordsBoston
Machine Learning
Running
Street Environment
Street Measures
Street View Image
Issue Date2023
Citation
Landscape and Urban Planning, 2023, v. 235, article no. 104756 How to Cite?
AbstractThe built environment is found to relate to running behaviors. However, the impacts of the street environment on running were less addressed due to the lack of running data in large geospatial urban regions, while the potential of semi-open data sources like Strava Heatmap for running studies is rarely verified. Moreover, how objective features and the subjective perceptions of the street environment are related to running is still largely unknown. We hypothesize that the eye-level subjective and objective streetscapes may complement the macro-scale built environment factors to better inform running amount prediction. Therefore, we evaluated the associations between running and street attributes by applying multi-sourced data, street view imagery (SVI) and artificial intelligence (AI) technologies, taking Boston as an example. We found that, first, the street environment is significantly correlated with running. Accounting for the spatial effects, the collective strength of street attributes was almost the same as the counterpart of the built environment, validating the value of including subjective and objective streetscapes measures in running studies. Second, street factors can complement built environment factors, indicating the necessity of using both macro-scale and eye-level environmental features to interpret running. Third, in addition to higher accessibility and more public transportation, the safer, wider and relatively open streets with more natural views, street lights, amenities and furniture, could promote running, while the enclosed environment, dense and overwhelming buildings, excessive interruptions on streets might hinder running. Our study provides an important example of using semi-open running data and integrating multi-sourced data and AI to bring new insights into running and urban environment studies. The findings could provide instructive suggestions for the establishment of a running-friendly urban environment and ultimately help to improve public health.
Persistent Identifierhttp://hdl.handle.net/10722/336372
ISSN
2023 Impact Factor: 7.9
2023 SCImago Journal Rankings: 2.358
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDong, Lin-
dc.contributor.authorJiang, Hongchao-
dc.contributor.authorLi, Wenjing-
dc.contributor.authorQiu, Bing-
dc.contributor.authorWang, Hao-
dc.contributor.authorQiu, Waishan-
dc.date.accessioned2024-01-15T08:26:15Z-
dc.date.available2024-01-15T08:26:15Z-
dc.date.issued2023-
dc.identifier.citationLandscape and Urban Planning, 2023, v. 235, article no. 104756-
dc.identifier.issn0169-2046-
dc.identifier.urihttp://hdl.handle.net/10722/336372-
dc.description.abstractThe built environment is found to relate to running behaviors. However, the impacts of the street environment on running were less addressed due to the lack of running data in large geospatial urban regions, while the potential of semi-open data sources like Strava Heatmap for running studies is rarely verified. Moreover, how objective features and the subjective perceptions of the street environment are related to running is still largely unknown. We hypothesize that the eye-level subjective and objective streetscapes may complement the macro-scale built environment factors to better inform running amount prediction. Therefore, we evaluated the associations between running and street attributes by applying multi-sourced data, street view imagery (SVI) and artificial intelligence (AI) technologies, taking Boston as an example. We found that, first, the street environment is significantly correlated with running. Accounting for the spatial effects, the collective strength of street attributes was almost the same as the counterpart of the built environment, validating the value of including subjective and objective streetscapes measures in running studies. Second, street factors can complement built environment factors, indicating the necessity of using both macro-scale and eye-level environmental features to interpret running. Third, in addition to higher accessibility and more public transportation, the safer, wider and relatively open streets with more natural views, street lights, amenities and furniture, could promote running, while the enclosed environment, dense and overwhelming buildings, excessive interruptions on streets might hinder running. Our study provides an important example of using semi-open running data and integrating multi-sourced data and AI to bring new insights into running and urban environment studies. The findings could provide instructive suggestions for the establishment of a running-friendly urban environment and ultimately help to improve public health.-
dc.languageeng-
dc.relation.ispartofLandscape and Urban Planning-
dc.subjectBoston-
dc.subjectMachine Learning-
dc.subjectRunning-
dc.subjectStreet Environment-
dc.subjectStreet Measures-
dc.subjectStreet View Image-
dc.titleAssessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.landurbplan.2023.104756-
dc.identifier.scopuseid_2-s2.0-85151246833-
dc.identifier.volume235-
dc.identifier.spagearticle no. 104756-
dc.identifier.epagearticle no. 104756-
dc.identifier.isiWOS:000971042800001-

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