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- Publisher Website: 10.1016/j.compenvurbsys.2019.101428
- Scopus: eid_2-s2.0-85075417540
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Article: Portraying the spatial dynamics of urban vibrancy using multisource urban big data
Title | Portraying the spatial dynamics of urban vibrancy using multisource urban big data |
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Authors | |
Keywords | Social media Points-of-interest Geographically weighted regression Urban vibrancy mobile phone data Big data |
Issue Date | 2020 |
Citation | Computers, Environment and Urban Systems, 2020, v. 80, article no. 101428 How to Cite? |
Abstract | Understanding urban vibrancy aids policy-making to foster urban space and therefore has long been a goal of urban studies. Recently, the emerging urban big data and urban analytic methods have enabled us to portray citywide vibrancy. From the social sensing perspective, this study presents a comprehensive and comparative framework to cross-validate urban vibrancy and uncover associated spatial effects. Spatial patterns of urban vibrancy indicated by multisource urban sensing data (points-of-interest, social media check-ins, and mobile phone records) were investigated. A comprehensive urban vibrancy metric was formed by adaptively weighting these metrics. The association between urban vibrancy and demographic, economic, and built environmental factors was revealed with global regression models and local regression models. An empirical experiment was conducted in Shenzhen. The results demonstrate that four urban vibrancy metrics are all higher in the special economic zone (SEZ) and lower in non-SEZs but with different degrees of spatial aggregation. The influences of employment and road density on all vibrancy metrics are significant and positive. However, the effects of metro stations, land use mix, building footprints, and distance to district center depend on the vibrancy indicator and location. These findings unravel the commonalities and differences in urban vibrancy metrics derived from multisource urban big data and the hidden spatial dynamics of the influences of associated factors. They further suggest that urban policies should be proposed to foster vibrancy in Shenzhen therefore benefit social wellbeing and urban development in the long term. They also provide valuable insights into the reliability of urban big data-driven urban studies. |
Persistent Identifier | http://hdl.handle.net/10722/299609 |
ISSN | 2023 Impact Factor: 7.1 2023 SCImago Journal Rankings: 1.861 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Tu, Wei | - |
dc.contributor.author | Zhu, Tingting | - |
dc.contributor.author | Xia, Jizhe | - |
dc.contributor.author | Zhou, Yulun | - |
dc.contributor.author | Lai, Yani | - |
dc.contributor.author | Jiang, Jincheng | - |
dc.contributor.author | Li, Qingquan | - |
dc.date.accessioned | 2021-05-21T03:34:46Z | - |
dc.date.available | 2021-05-21T03:34:46Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Computers, Environment and Urban Systems, 2020, v. 80, article no. 101428 | - |
dc.identifier.issn | 0198-9715 | - |
dc.identifier.uri | http://hdl.handle.net/10722/299609 | - |
dc.description.abstract | Understanding urban vibrancy aids policy-making to foster urban space and therefore has long been a goal of urban studies. Recently, the emerging urban big data and urban analytic methods have enabled us to portray citywide vibrancy. From the social sensing perspective, this study presents a comprehensive and comparative framework to cross-validate urban vibrancy and uncover associated spatial effects. Spatial patterns of urban vibrancy indicated by multisource urban sensing data (points-of-interest, social media check-ins, and mobile phone records) were investigated. A comprehensive urban vibrancy metric was formed by adaptively weighting these metrics. The association between urban vibrancy and demographic, economic, and built environmental factors was revealed with global regression models and local regression models. An empirical experiment was conducted in Shenzhen. The results demonstrate that four urban vibrancy metrics are all higher in the special economic zone (SEZ) and lower in non-SEZs but with different degrees of spatial aggregation. The influences of employment and road density on all vibrancy metrics are significant and positive. However, the effects of metro stations, land use mix, building footprints, and distance to district center depend on the vibrancy indicator and location. These findings unravel the commonalities and differences in urban vibrancy metrics derived from multisource urban big data and the hidden spatial dynamics of the influences of associated factors. They further suggest that urban policies should be proposed to foster vibrancy in Shenzhen therefore benefit social wellbeing and urban development in the long term. They also provide valuable insights into the reliability of urban big data-driven urban studies. | - |
dc.language | eng | - |
dc.relation.ispartof | Computers, Environment and Urban Systems | - |
dc.subject | Social media | - |
dc.subject | Points-of-interest | - |
dc.subject | Geographically weighted regression | - |
dc.subject | Urban vibrancy | - |
dc.subject | mobile phone data | - |
dc.subject | Big data | - |
dc.title | Portraying the spatial dynamics of urban vibrancy using multisource urban big data | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.compenvurbsys.2019.101428 | - |
dc.identifier.scopus | eid_2-s2.0-85075417540 | - |
dc.identifier.volume | 80 | - |
dc.identifier.spage | article no. 101428 | - |
dc.identifier.epage | article no. 101428 | - |
dc.identifier.isi | WOS:000515209500020 | - |