Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou

Xucai Zhang, Yeran Sun, Ting On Chan, Ying Huang, Anyao Zheng and Zhang Liu
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Xucai Zhang: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yeran Sun: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Ting On Chan: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Ying Huang: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Anyao Zheng: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Zhang Liu: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China

Sustainability, 2021, vol. 13, issue 2, 1-23

Abstract: Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.

Keywords: urban vibrancy; GTWR; POI; spatiotemporal pattern; tencent location-aware data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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