College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
Department of Integrative Biology, University of Texas at Austin, Austin, 78705, USA.
Sci Data. 2018 Oct 30;5:180228. doi: 10.1038/sdata.2018.228.
Mobile data are a feasible way for us to understand and reveal the feature of human mobility. However, it is extremely hard to have a fine-grained picture of large-scale mobility data, in particular at an urban scale. Here, we present a large-scale dataset of 2-million mobile phone users with time-varying locations, denoted as the temporal network of individuals, conducted by an open-data program in Changchun Municipality. To reveal human mobility across locations, we further construct the aggregated mobility network for each day by taking cellular base stations as nodes coupled by edges weighted by the total number of users' movements between pairs of nodes. The resulting temporal network of mobile phone users and the dynamic, weighted and directed mobility network are released in simple formats for easy access to motivating research using this new and extensive data of human mobility.
移动数据是我们理解和揭示人类移动性特征的一种可行方法。然而，要获得大规模移动数据的精细图片，特别是在城市规模上，这是极其困难的。在这里，我们展示了一个由长春市开放数据计划进行的、具有 200 万具有时变位置的手机用户的大型数据集，记为个体的时变网络。为了揭示跨位置的人类移动性，我们进一步通过将蜂窝基站作为节点，并通过节点对之间用户移动总数作为边的权重，构建了每天的聚合移动性网络。由此产生的手机用户时变网络和动态、加权、有向移动性网络以简单的格式发布，便于使用这种新的广泛的人类移动性数据来激发研究。
