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首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >TelCoVis: Visual Exploration of Co-occurrence in Urban Human Mobility Based on Telco Data
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TelCoVis: Visual Exploration of Co-occurrence in Urban Human Mobility Based on Telco Data

机译:TelCoVis:基于Telco数据的城市人口流动共现的可视化探索

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摘要

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.
机译:了解城市人口流动中的共现(例如,两个地区的人们在同一时间跨度访问城市)在各种应用中具有重要价值,例如城市规划,商业智能,社会行为分析以及传染性疾病。近年来,移动电话的广泛使用带来了前所未有的机会来捕获大规模且细粒度的数据,以研究人类移动性的同时发生。但是,由于缺乏系统,有效的方法,分析人员进行深入的分析并提取有价值的信息是一项挑战。在本文中,我们介绍了一种交互式视觉分析系统TelCoVis,该系统可帮助分析人员利用其领域知识来洞悉基于电信数据的城市人类出行的共现现象。我们的系统将可视化技术与新设计集成在一起,并以新颖的方式将它们组合在一起,以增强分析师的全面了解。此外,我们建议通过双聚类技术研究共现的相关性(即来自多个区域的人在同一时间跨度访问不同的地方),该技术可使分析师更好地探索不同区域之间的协调关系并确定有趣的模式。基于现实世界数据集的案例研究以及与领域专家的访谈表明,我们的系统在获得对共现的见解和促进各种分析任务方面的有效性。

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