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Article: To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults

TitleTo walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults
Authors
KeywordsBig data
Machine learning
Population aging
Random forest
Streetscape greenery
Travel behavior
Walking behavior
Issue Date2021
Citation
Journal of Transport Geography, 2021, v. 94, article no. 103099 How to Cite?
AbstractPopulation aging is a conspicuous demographic trend shaping the world profoundly. Walking is a critical travel mode and physical activity for older adults. As such, there is a need to determine the factors influencing the walking behavior of older people in the era of population aging. Streetscape greenery is an easily perceived built-environment attribute and can promote walking behavior, but it has received insufficient attention. More importantly, the non-linear effects of streetscape greenery on the walking behavior of older adults have not been examined. We therefore use readily available Google Street View imagery and a fully convolutional neural network to evaluate human-scale, eye-level streetscape greenery. Using data from the Hong Kong Travel Characteristic Survey, we adopt a machine learning technique, namely random forest modeling, to scrutinize the non-linear effects of streetscape greenery on the walking propensity of older adults. The results show that streetscape greenery has a positive effect on walking propensity within a certain range, but outside the range, the positive association no longer holds. The non-linear associations of other built-environment attributes are also examined.
Persistent Identifierhttp://hdl.handle.net/10722/308870
ISSN
2023 Impact Factor: 5.7
2023 SCImago Journal Rankings: 1.791
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, Linchuan-
dc.contributor.authorAo, Yibin-
dc.contributor.authorKe, Jintao-
dc.contributor.authorLu, Yi-
dc.contributor.authorLiang, Yuan-
dc.date.accessioned2021-12-08T07:50:18Z-
dc.date.available2021-12-08T07:50:18Z-
dc.date.issued2021-
dc.identifier.citationJournal of Transport Geography, 2021, v. 94, article no. 103099-
dc.identifier.issn0966-6923-
dc.identifier.urihttp://hdl.handle.net/10722/308870-
dc.description.abstractPopulation aging is a conspicuous demographic trend shaping the world profoundly. Walking is a critical travel mode and physical activity for older adults. As such, there is a need to determine the factors influencing the walking behavior of older people in the era of population aging. Streetscape greenery is an easily perceived built-environment attribute and can promote walking behavior, but it has received insufficient attention. More importantly, the non-linear effects of streetscape greenery on the walking behavior of older adults have not been examined. We therefore use readily available Google Street View imagery and a fully convolutional neural network to evaluate human-scale, eye-level streetscape greenery. Using data from the Hong Kong Travel Characteristic Survey, we adopt a machine learning technique, namely random forest modeling, to scrutinize the non-linear effects of streetscape greenery on the walking propensity of older adults. The results show that streetscape greenery has a positive effect on walking propensity within a certain range, but outside the range, the positive association no longer holds. The non-linear associations of other built-environment attributes are also examined.-
dc.languageeng-
dc.relation.ispartofJournal of Transport Geography-
dc.subjectBig data-
dc.subjectMachine learning-
dc.subjectPopulation aging-
dc.subjectRandom forest-
dc.subjectStreetscape greenery-
dc.subjectTravel behavior-
dc.subjectWalking behavior-
dc.titleTo walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jtrangeo.2021.103099-
dc.identifier.scopuseid_2-s2.0-85106960716-
dc.identifier.volume94-
dc.identifier.spagearticle no. 103099-
dc.identifier.epagearticle no. 103099-
dc.identifier.isiWOS:000672856800012-

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