Investigating the spatiotemporal pattern between the built environment and urban vibrancy using big data in Shenzhen, China

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Detail(s)

Original languageEnglish
Article number101827
Journal / PublicationComputers, Environment and Urban Systems
Volume95
Online published26 May 2022
Publication statusPublished - Jul 2022

Abstract

Promoting urban vibrancy is one of the major objectives of urban planners and government officials, and it is linked to various benefits, such as urban prosperity and human well-being. There is ample evidence that built environment characteristics are associated with urban vibrancy; however, the spatiotemporal associations between built environment and urban vibrancy have not been fully investigated owing to the inherent limitations of traditional data. To address this gap, we measured spatiotemporal urban vibrancy in Shenzhen, China, using Tencent location-based big data, which is characterized by fine-grained population-level spatiotemporal granularity. Built environment characteristics were systematically measured using the 5D framework (density, diversity, design, destination accessibility, and distance to transit) with multi-source datasets. We investigated the spatiotemporal non-stationary associations using a geographically and temporally weighted regression (GTWR) model. The results indicated that the GTWR models achieved better goodness-of-fit than linear regression models. Built environment factors such as population density; point of interest (POI) mix; residential, commercial, company, and public service POI; and metro station were significantly associated with urban vibrancy. Time series clustering revealed spatiotemporal clustered patterns of the associations between built environment factors and urban vibrancy. To promote urban vibrancy with urban planning and design strategies, both the spatial and temporal associations between the built environment and urban vibrancy should be considered.

Research Area(s)

  • Urban vibrancy, Built environment, Geographically and temporally weighted regression, Spatiotemporal analysis, Big data, Shenzhen

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