Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China
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With the advent of the Big Data era, multi-source geo-tagged data provide a new perspective and data source for urban spatial analysis. In order to accurately identify the location and characteristics of consumer clusters in urban area and explore their formation mechanism, this study collects Sina Weibo check-in data and Dianping.com electronic word of mouth (e-WOM) data generated in the catering consumer space in the core region of Pearl River Delta, Guangdong and identifies location and characteristics of clusters with the help of DBSCAN clustering algorithm and CSA indices. In addition, the formation mechanism of these catering space clusters is explained by non-spatial and global spatial regression models. The result revealed that 4 levels of 19 catering space clusters are identified in the study area. The size and heat of consumer clusters are mainly affected by the geometric form, diversity of check-in, population density, distance from the city center and e-WOM corresponding to each cluster. The present study suggests that the new DBSCAN-based clustering method has a high accuracy. Compared with the traditional factors that reflect the objective attributes of cities and non-spatial models, the unstructured information elements contained in e-WOM and spatial error models can better explain the formation mechanism of the consumer clusters.
KeywordsGeo-tagged data DBSCAN algorithm Consumer clusters Spatial error model
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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