Urban Hotspot Detection and Commercial Area Analysis based on Check-in Data Using Exploratory Spatial Data Analysis

Abstract

Abstract:

Crowd sourcing geographic data is an open source geographic data which is contributed by lots of non-professionals and provided to the public. As a kind of crowd sourcing geographic data, check-in data contains multitudes of social attribute data and reflects people’s activities of daily living objectively and actually. The paper proposes a method of urban commercial area mining and analysis based on check-in data: Firstly, the paper studies the spatio-temporal distribution characteristics of check-in data and designs a spatio-temporal database model for check-in data; Secondly, the paper proposes an exploratory spatial data analysis method of check-in data and achieves commercial hotspot detection based on check-in data by spatial clustering analysis of check-in data; Finally, an experiment of urban commercial mining and analysis with the check-in data obtained from Jiepang by the deadline of September 30, 2011 in Wuhan is designed and implemented. The result shows that the urban commercial area distribution of Wuhan based on check-in data has a high correlation with urban planning and can be used for regional planning of urban society development.

Key words: crowd sourcing geographic data, check-in data, data mining, hotspot detection, distribution of commercial area

CLC Number:

P208

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