基于移动通信大数据的城市人口空间分布统计

针对移动通信空间大数据的计算与分析，通过Geometry API构建ArcGIS和Hadoop的集成计算平台。采用COO定位技术采集手机用户位置数据。在ArcGIS中用Voronoi图构建地图信息模型；通过圈层人口密度计算构建人口密度模型；通过DBSCAN密度聚类算法构建职住地分布模型；采用核密度估算构建报警电话分布模型。实验选取中国移动杭州分公司2017-04至2017-06之间的移动通话数据，结果显示：杭州市区人口密度Moran’s I值为0.46724，人口分布的总体特征表现为集聚，高值集聚涵盖滨江、上城、下城全境和江干、拱墅、西湖的部分区域。与杭州市2015年全国1%人口抽样调查数据分析结果基本一致。以上各模型适用于城市人口时空分布统计。

Spatial Distribution Statistics of Urban Population Based on Mobile Communication Big Data

Aiming at the calculation and analysis of big data in mobile communication space, the integrated computing platform of ArcGIS and Hadoop is built through Geometry API. COO positioning technology is adopted to collect mobile phone user location data. At the same time, in ArcGIS, Voronoi diagram is used to construct the map information model, and the population density model is built through the density calculation of the circle. Then, the distribution model of the worksite and residence is constructed based on DBSCAN density clustering algorithm. Moreover, the kernel density estimation is used to build the alarm telephone distribution model. The experiment chooses mobile data from Hangzhou Branch of China Mobile Phone between 2017-04 and 2017-06. The result shows that the value of Moran’s I for Hangzhou urban population density is 0.46724. It also evinces that the general characteristics of population distribution is agglomeration. High value agglomeration covers Binjiang, Shangcheng, Xiacheng, as well as parts of Jianggan, Gongshu and Xihu. Thus, the results in this experiment are basically consistent with the results of data analysis of 1% population sampling survey in Hangzhou in 2015. Therefore, the above models are applicable to the spatial and temporal distribution statistics of urban population.