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Tingting Zhu, Wei Tu, Yang Yue, Chen Zhong, Tianhong Zhao, Qiuping Li, Qingquan Li

Translated title of the contributionSensing urban vibrancy using geo-tagged data
Original languageChinese
Pages (from-to)365-374
Number of pages10
JournalCehui Xuebao/Acta Geodaetica et Cartographica Sinica
Issue number3
Published1 Mar 2020

King's Authors


Promoting neighborhood vibrancy is vital for urban development. Recently, geotagged data provide unprecedented opportunities for discovering urban vibrancy patterns and their affecting mechanism. However, traditional urban vibrancy studies rely on fields survey therefore are time-consuming and highly-cost. This study constructs two urban vibrancy indices using point-of-interest and social media check in data. The spatial patterns of urban vibrancy are explored with spatial auto-regression analytic. Ordinary regression models (OLS) and spatial autoregression models (SAM) are established for revealing the influences of built environment on urban vibrancy by using geospatial data such as land use, roads and buildings. An empirical study in Shenzhen was implemented. The results show that: commercial land, industry land, mixed land use, the road density, and metro stations are five main factors highly influencing Shenzhen vibrancy. Residential land use and building footprints only have significant effects on vibrancy exhibited by POI. These exploratory findings demonstrate that urban vibrancy should be assessed and improved for different consideration.

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