测绘学报

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利用位置签到数据探索城市热点与商圈

胡庆武1,王明1,李清泉2   

  1. 1. 武汉大学遥感信息工程学院
    2. 武汉大学测绘遥感信息工程国家重点实验室
  • 收稿日期:2012-07-23 修回日期:2012-12-13 出版日期:2014-03-20 发布日期:2013-12-19
  • 通讯作者: 胡庆武
  • 基金资助:

    海陆相互作用和海岸带地质灾害研究资助项目

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

  • Received:2012-07-23 Revised:2012-12-13 Online:2014-03-20 Published:2013-12-19

摘要:

众源地理数据(Crowd Sourcing Geographic Data)是指由大众采集并向大众开放共享的地理空间数据。众源位置签到数据作为众源地理数据的一种,客观真实的反映了大众日常生活行为,包含大量丰富的社会化属性信息。本文提出了一种基于众源位置签到数据的城市热点探测与商圈挖掘方法,首先对位置签到数据时空分布特性进行了研究,设计并提出了众源位置签到数据时空数据库模型;其次,提出了位置签到数据探索性空间分析方法,通过对众源位置签到数据的空间聚类分析,实现基于位置签到数据的商圈热点探测;最后,以武汉市为例,对街旁网截止2011年9月30日的众源位置签到数据进行了城市热点探测与商圈挖掘分析实验,结果表明,基于众源位置签到数据挖掘的武汉市商圈分布与城市规划商圈具有强相关性,可用于城市社会经济发展预测与区域经济规划。

关键词: 众源地理数据, 位置签到数据, 数据挖掘, 热点探测, 商圈分布

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

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