Wu Wenchao, Xu Jiayi, Zeng Haipeng, Zheng Yixian, Qu Huamin, Ni Bing, Yuan Mingxuan, Ni Lionel M
IEEE Trans Vis Comput Graph. 2016 Jan;22(1):935-44. doi: 10.1109/TVCG.2015.2467194. Epub 2015 Aug 12.
Understanding co-occurrence in urban human mobility (i.e. people from two regions visit an urban place during the same time span) is of great value in a variety of applications, such as urban planning, business intelligence, social behavior analysis, as well as containing contagious diseases. In recent years, the widespread use of mobile phones brings an unprecedented opportunity to capture large-scale and fine-grained data to study co-occurrence in human mobility. However, due to the lack of systematic and efficient methods, it is challenging for analysts to carry out in-depth analyses and extract valuable information. In this paper, we present TelCoVis, an interactive visual analytics system, which helps analysts leverage their domain knowledge to gain insight into the co-occurrence in urban human mobility based on telco data. Our system integrates visualization techniques with new designs and combines them in a novel way to enhance analysts' perception for a comprehensive exploration. In addition, we propose to study the correlations in co-occurrence (i.e. people from multiple regions visit different places during the same time span) by means of biclustering techniques that allow analysts to better explore coordinated relationships among different regions and identify interesting patterns. The case studies based on a real-world dataset and interviews with domain experts have demonstrated the effectiveness of our system in gaining insights into co-occurrence and facilitating various analytical tasks.
理解城市人口流动中的共现情况（即来自两个地区的人在同一时间段内访问同一个城市地点）在诸如城市规划、商业智能、社会行为分析以及传染病防控等各种应用中具有重要价值。近年来，手机的广泛使用为获取大规模、细粒度的数据以研究人口流动中的共现情况带来了前所未有的机遇。然而，由于缺乏系统且高效的方法，分析人员进行深入分析并提取有价值信息具有挑战性。在本文中，我们提出了TelCoVis，一种交互式可视化分析系统，它帮助分析人员利用其领域知识，基于电信数据深入了解城市人口流动中的共现情况。我们的系统将可视化技术与新设计相结合，并以新颖的方式将它们组合起来，以增强分析人员的感知能力，实现全面探索。此外，我们建议通过双聚类技术研究共现中的相关性（即来自多个地区的人在同一时间段内访问不同地点），这使分析人员能够更好地探索不同地区之间的协调关系并识别有趣的模式。基于真实世界数据集的案例研究以及与领域专家的访谈证明了我们的系统在深入了解共现情况和促进各种分析任务方面的有效性。
