Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study

Bo Huang, Yulun Zhou, Zhigang Li, Yimeng Song, Jixuan Cai and Wei Tu
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Yulun Zhou: The Chinese University of Hong Kong, China
Zhigang Li: Wuhan University, China

Environment and Planning B, 2020, vol. 47, issue 9, 1543-1559

Abstract: Although people may recognize urban vibrancy when they see or sense it, developing direct and comprehensive measures of urban vibrancy remains a challenge. In the context of intense global competition, there is an increased realization that urban vibrancy is vital to the social and economic sustainability of cities. Such vibrancy may be significantly shaped by the urban built environment, yet we know little about the close connections between vibrancy and urban built environments. Empowered by newly available sources of spatial big data, which provide enormous amounts of information on both human dynamics and the built environment, this paper proposes a framework for evaluating and characterizing urban vibrancy. Thus far, vibrancy measures have mostly used single-source data that hardly reflect the multifaceted manifestations of urban vibrancy. Therefore, we propose a more comprehensive measure of urban vibrancy, extracted as the common latent factor from multiple surface attributes. Using the proposed framework, we evaluated and mapped the spatial dynamics of vibrancy in Shanghai, a typical large city in post-reform China, and investigated the associations between vibrancy and various urban built environment indicators. The evidence shows that the horizontal built-up density, rather than vertical height, is the leading generator of vibrancy in Shanghai, followed by the density and mixture of urban functions, accessibility, and walkability. In this vein, we contribute to current debates and future planning practices regarding vibrant spaces in large cities. This proposed evaluation framework, equipped with spatial big data, can benefit future urban studies.

Keywords: Urban vibrancy; urban built environment; spatial big data; factor analysis; Shanghai (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:47:y:2020:i:9:p:1543-1559

DOI: 10.1177/2399808319828730

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