基于大规模手机定位数据的群体活动时空特征分析

发布时间：2018-06-27 15:46

本文选题：手机定位数据 + 轨迹分析　；参考：《地球信息科学学报》2017年04期

【摘要】：城市空间与居民行为不断交互,相互影响。探究城市空间中的群体活动分布及其时空变化能够帮助数据驱动的城市规划与城市治理。基于大数据的时空间群体活动研究是当前时空大数据研究的一个热点。本文以深圳市为例,基于约1000万手机用户在某一工作日的基站尺度的手机定位数据,识别用户停留位置和停留活动,重建活动语义信息,分析用户的停留点和停留活动的分布差异,研究群体活动的时空分布模式,探讨人群活动模式的多样分布特征。研究表明:停留位置和活动分布存在差异,每人每天平均的停留个数约为2.1个,而每人每天平均从事的活动约为3.4个;不同类型的活动在时间上存在波动;群体活动存在空间分异特征,整体上服从"空间幂律"。本研究揭示了城市空间中群体活动的多样性及其时空分布特征,对于城市居民活动研究、城市交通优化和城市规划具有重要的意义。
[Abstract]:Urban space and residents' behavior interact and interact with each other. Exploring the distribution of group activities and their temporal and spatial changes in urban space can help data-driven urban planning and urban governance. The research of temporal and spatial group activity based on big data is a hot topic in the research of spatiotemporal big data. Taking Shenzhen City as an example, based on the mobile phone location data of about 10 million mobile phone users at the base station scale of a working day, this paper identifies the user's stay position and stay activity, and reconstructs the semantic information of the activity. This paper analyzes the difference of the user's stopping point and the distribution of the staying activity, studies the spatial and temporal distribution pattern of the group activity, and probes into the diverse distribution characteristics of the crowd activity pattern. The results show that there are differences in residence location and activity distribution, the average number of residence per person per day is about 2.1, and the average activity per person per day is about 3.4, and different types of activities fluctuate in time. Group activity has the characteristic of spatial differentiation, so it can be applied to the law of space power as a whole. This study reveals the diversity and spatial and temporal distribution of group activities in urban space, which is of great significance to the study of urban residents' activities, urban traffic optimization and urban planning.
【作者单位】：武汉大学测绘遥感信息工程国家重点实验室;深圳大学土木工程学院空间信息智能感知与服务深圳市重点实验室;海岸带地理环境监测国家测绘地理信息局重点实验室;深圳大学智慧城市研究院;宁波诺丁汉大学国际博士创新研究中心;
【基金】：国家自然科学基金项目(41401444、41371377、41671387) 深圳大学青年教师科研启动项目(2016065) 国土资源部城市土地资源监测与仿真重点实验室开放基金资助课题(KF-2016-02-009)
【分类号】：P208;C912.6

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