File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)

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
Issue Date2019
Citation
International Journal of Environmental Research and Public Health., 2019, v. 16 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

 

DC FieldValueLanguage
dc.contributor.authorZhang, L-
dc.contributor.authorYe, Y-
dc.contributor.authorZENG, W-
dc.contributor.authorChiaradia, AJF-
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-
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.relation.ispartofInternational Journal of Environmental Research and Public Health.-
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, AJF: alainjfc@hku.hk-
dc.identifier.authorityChiaradia, AJF=rp02166-
dc.identifier.doihttps://doi.org/10.3390/ijerph16101782-
dc.identifier.hkuros305796-
dc.identifier.volume16-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats