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Article: The impact of street-scale built environments on urban park visitations: A case study in Wuhan

TitleThe impact of street-scale built environments on urban park visitations: A case study in Wuhan
Authors
KeywordsCOVID-19
Generalized ordered logit regression
Street view images
Urban park visitation
Issue Date1-Oct-2024
PublisherElsevier
Citation
Applied Geography, 2024, v. 171 How to Cite?
AbstractThe COVID-19 pandemic has changed human life globally. Existing studies have revealed that citizens' visitations to urban parks varied before and after the COVID-19 outbreak. However, few studies have examined how street-scale built environments (SBEs) on routes affect visitations to urban parks at varying COVID-19 risk levels. In this study, a stated-preference survey was conducted to investigate 3,218 visitors' changes in urban park visitation under various COVID-19 risk levels. In addition to park visit influencing factors, including park features, neighborhood built environment, socio-demographic attributes, and travel distances, multiple SBE indexes on visitors' routes to parks were obtained from 34,780 Baidu Map street view images using a deep neural network (DeepLabv3+) method. The results suggest that a high GVI and high traffic congestion on the route from the visitor's home to the urban park led to an increased probability of visiting the urban park by 188.1% (p = 0.044, OR = 2.881) and a decreased probability by 32.3% (p = 0.049, OR = 0.677), respectively. The high probability of visitation was also associated with socio-demographic attributes (including male gender, high income, high and medium education levels, and the elderly) and short travel distances.
Persistent Identifierhttp://hdl.handle.net/10722/362870
ISSN
2023 Impact Factor: 4.0
2023 SCImago Journal Rankings: 1.204

 

DC FieldValueLanguage
dc.contributor.authorZhang, Wenting-
dc.contributor.authorGuan, Haochun-
dc.contributor.authorLi, Shan-
dc.contributor.authorHuang, Bo-
dc.contributor.authorHong, Wuyang-
dc.contributor.authorLiu, Wenping-
dc.date.accessioned2025-10-03T00:35:42Z-
dc.date.available2025-10-03T00:35:42Z-
dc.date.issued2024-10-01-
dc.identifier.citationApplied Geography, 2024, v. 171-
dc.identifier.issn0143-6228-
dc.identifier.urihttp://hdl.handle.net/10722/362870-
dc.description.abstractThe COVID-19 pandemic has changed human life globally. Existing studies have revealed that citizens' visitations to urban parks varied before and after the COVID-19 outbreak. However, few studies have examined how street-scale built environments (SBEs) on routes affect visitations to urban parks at varying COVID-19 risk levels. In this study, a stated-preference survey was conducted to investigate 3,218 visitors' changes in urban park visitation under various COVID-19 risk levels. In addition to park visit influencing factors, including park features, neighborhood built environment, socio-demographic attributes, and travel distances, multiple SBE indexes on visitors' routes to parks were obtained from 34,780 Baidu Map street view images using a deep neural network (DeepLabv3+) method. The results suggest that a high GVI and high traffic congestion on the route from the visitor's home to the urban park led to an increased probability of visiting the urban park by 188.1% (p = 0.044, OR = 2.881) and a decreased probability by 32.3% (p = 0.049, OR = 0.677), respectively. The high probability of visitation was also associated with socio-demographic attributes (including male gender, high income, high and medium education levels, and the elderly) and short travel distances.-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofApplied Geography-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCOVID-19-
dc.subjectGeneralized ordered logit regression-
dc.subjectStreet view images-
dc.subjectUrban park visitation-
dc.titleThe impact of street-scale built environments on urban park visitations: A case study in Wuhan-
dc.typeArticle-
dc.identifier.doi10.1016/j.apgeog.2024.103374-
dc.identifier.scopuseid_2-s2.0-85201300395-
dc.identifier.volume171-
dc.identifier.eissn1873-7730-
dc.identifier.issnl0143-6228-

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