Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/94207
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorYang, Len_US
dc.creatorAo, Yen_US
dc.creatorKe, Jen_US
dc.creatorLu, Yen_US
dc.creatorLiang, Yen_US
dc.date.accessioned2022-08-11T01:08:36Z-
dc.date.available2022-08-11T01:08:36Z-
dc.identifier.issn0966-6923en_US
dc.identifier.urihttp://hdl.handle.net/10397/94207-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Yang, L., Ao, Y., Ke, J., Lu, Y., & Liang, Y. (2021). To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults. Journal of transport geography, 94, 103099 is available at https://doi.org/10.1016/j.jtrangeo.2021.103099.en_US
dc.subjectBig dataen_US
dc.subjectMachine learningen_US
dc.subjectPopulation agingen_US
dc.subjectRandom foresten_US
dc.subjectStreetscape greeneryen_US
dc.subjectTravel behavioren_US
dc.subjectWalking behavioren_US
dc.titleTo walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adultsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume94en_US
dc.identifier.doi10.1016/j.jtrangeo.2021.103099en_US
dcterms.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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of transport geography, June 2021, v. 94, 103099en_US
dcterms.isPartOfJournal of transport geographyen_US
dcterms.issued2021-06-
dc.identifier.scopus2-s2.0-85106960716-
dc.identifier.artn103099en_US
dc.description.validate202208 bckwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLMS-0036-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextFundamental Research Funds for the Central Universities of China; Education and Scientific Research Grant of Sichuan Province; Talent Cultivation Quality and Teaching Reform Project of Ideological and Political Theory Course of Chengdu University of Technology in 2020en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS55063648-
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Ke_Walk_Or_Not.pdfPre-Published version1.5 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

66
Last Week
2
Last month
Citations as of May 12, 2024

Downloads

197
Citations as of May 12, 2024

SCOPUSTM   
Citations

222
Citations as of May 17, 2024

WEB OF SCIENCETM
Citations

204
Citations as of May 16, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.