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首页> 外文期刊>ACM transactions on intelligent systems >Relation Lines: Visual Reasoning of Egocentric Relations from Heterogeneous Urban Data
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Relation Lines: Visual Reasoning of Egocentric Relations from Heterogeneous Urban Data

机译：关系线：基于异类城市数据的自我中心关系的视觉推理

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摘要

The increased accessibility of urban sensor data and the popularity of social network applications is enabling the discovery of crowd mobility and personal communication patterns. However, studying the egocentric relationships of an individual can be very challenging because available data may refer to direct contacts, such as phone calls between individuals, or indirect contacts, such as paired location presence. In this article, we develop methods to integrate three facets extracted from heterogeneous urban data (timelines, calls, and locations) through a progressive visual reasoning and inspection scheme. Our approach uses a detect-andfilter scheme such that, prior to visual refinement and analysis, a coarse detection is performed to extract the target individual and construct the timeline of the target. It then detects spatio-temporal co-occurrences or call-based contacts to develop the egocentric network of the individual. The filtering stage is enhanced with a line-based visual reasoning interface that facilitates a flexible and comprehensive investigation of egocentric relationships and connections in terms of time, space, and social networks. The integrated system, RelationLines, is demonstrated using a dataset that contains taxi GPS data, cell-base mobility data, mobile calling data, microblog data, and point-of-interest (POI) data from a city with millions of citizens. We examine the effectiveness and efficiency of our system with three case studies and user review.
机译：城市传感器数据可访问性的提高和社交网络应用程序的普及，使得人们能够发现人群的移动性和个人通信模式。但是，研究个人的以自我为中心的关系可能会非常具有挑战性，因为可用数据可能涉及直接联系人（例如，个人之间的电话呼叫）或间接联系人（例如，配对的位置存在）。在本文中，我们通过逐步的视觉推理和检查方案，开发了从异类城市数据（时间线，呼叫和位置）提取的三个方面进行集成的方法。我们的方法使用检测和过滤器方案，以便在视觉优化和分析之前，执行粗略检测以提取目标个体并构建目标的时间轴。然后，它检测时空共现或基于呼叫的联系人，以发展个人的以自我为中心的网络。基于行的视觉推理界面增强了过滤阶段，该界面有助于在时间，空间和社交网络方面灵活，全面地研究以自我为中心的关系和联系。使用包含出租车GPS数据，基于单元的移动性数据，移动呼叫数据，微博数据和拥有数百万市民的城市的兴趣点（POI）数据集的数据集演示了集成系统RelationLines。我们通过三个案例研究和用户审查来检查我们系统的有效性和效率。

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