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Article: A systematic measurement of street quality through multi-sourced urban data: a human-oriented analysis

TitleA systematic measurement of street quality through multi-sourced urban data: a human-oriented analysis
Authors
Keywordssystematic measurement
street quality
multi-sourced urban data
urban design
human-oriented
Issue Date2019
PublisherMolecular Diversity Preservation International. The Journal's web site is located at http://www.mdpi.org/ijerph
Citation
International Journal of Environmental Research and Public Health, 2019, v. 16 n. 10, p. article no. 1782 How to Cite?
AbstractMany studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people’s holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.
Persistent Identifierhttp://hdl.handle.net/10722/277196
ISSN
2019 Impact Factor: 2.849
2015 SCImago Journal Rankings: 0.883
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, L-
dc.contributor.authorYe, Y-
dc.contributor.authorZENG, W-
dc.contributor.authorChiaradia, A-
dc.date.accessioned2019-09-20T08:46:27Z-
dc.date.available2019-09-20T08:46:27Z-
dc.date.issued2019-
dc.identifier.citationInternational Journal of Environmental Research and Public Health, 2019, v. 16 n. 10, p. article no. 1782-
dc.identifier.issn1661-7827-
dc.identifier.urihttp://hdl.handle.net/10722/277196-
dc.description.abstractMany studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people’s holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.-
dc.languageeng-
dc.publisherMolecular Diversity Preservation International. The Journal's web site is located at http://www.mdpi.org/ijerph-
dc.relation.ispartofInternational Journal of Environmental Research and Public Health-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectsystematic measurement-
dc.subjectstreet quality-
dc.subjectmulti-sourced urban data-
dc.subjecturban design-
dc.subjecthuman-oriented-
dc.titleA systematic measurement of street quality through multi-sourced urban data: a human-oriented analysis-
dc.typeArticle-
dc.identifier.emailZhang, L: zhanglz@hku.hk-
dc.identifier.emailChiaradia, A: alainjfc@hku.hk-
dc.identifier.authorityChiaradia, A=rp02166-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/ijerph16101782-
dc.identifier.pmid31137538-
dc.identifier.pmcidPMC6571925-
dc.identifier.scopuseid_2-s2.0-85067292970-
dc.identifier.hkuros305796-
dc.identifier.volume16-
dc.identifier.issue10-
dc.identifier.spagearticle no. 1782-
dc.identifier.epagearticle no. 1782-
dc.publisher.placeSwitzerland-

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