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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。
Urban space and residents continue to interact with each other. Exploring the distribution of population activities and their spatial and temporal changes in urban space can help data-driven urban planning and urban governance. Research on the group activities of time and space based on big data is a hot spot in the research of big data in space-time. This paper takes Shenzhen as an example. Based on the cell-phone positioning data of about 10 million cell-phone users on base station scale of a certain working day, this paper identifies the user’s location and stay activities, rebuilds the semantic information of activities, analyzes the distribution differences of stay points and stay activities , Study the spatiotemporal distribution patterns of group activities, and explore the diverse distribution patterns of population activity patterns. The study shows that there are differences in the locations and distribution of activities. The average number of people staying per day is about 2.1, while the average number of activities per person per day is about 3.4. Different types of activities fluctuate in time, and group activities exist Spatial differentiation characteristics, as a whole obey “space power law ”. This study reveals the diversity of population activities and their spatial and temporal distribution characteristics in urban space, which is of great significance for urban residents’ activity research, urban traffic optimization and urban planning.