Sensing urban vibrancy using geo-tagged data

doi:10.11947/j.AGCS.2020.20190051

Abstract

Abstract: 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.

Key words: urban vibrancy, POI, geotagged check in data, spatial auto-regression

CLC Number:

P208

ZHU Tingting, TU Wei, YUE Yang, ZHONG Chen, ZHAO Tianhong, LI Qiuping, LI Qingquan. Sensing urban vibrancy using geo-tagged data[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(3): 365-374.

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