利用地理标签数据感知城市活力

Translated title of the contribution: Sensing urban vibrancy using geo-tagged data

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

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.

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
Volume49
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • Geotagged check in data
  • POI
  • Spatial auto-regression
  • Urban vibrancy

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