TelCoVis: Visual Exploration of Co-occurrence in Urban Human Mobility Based on Telco Data

Abstract

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.

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

“…This study provided a flow mapping view from different levels of resolutions and demonstrated the method with taxi data [25]. Wu et al studied the urban human mobility by grouping trips with the same destinations [27]. They called it co-occurrence and the approach helped to understand the traffic flow at a given time in a given area.…”

Visual Analytics of Anomalous User Behaviors: A Survey

“…This improves the efficiency of detection processes and reduces false positives. TelCoVis [61] emphasizes the correlation between spatial and temporal data for exploring the cooccurrence of human mobility. When analysts hover on a sector in the contour-based treemap, all sectors corresponding to the same region will be highlighted.…”

“…Analysts can change the color scheme and assign colors to clusters on maps and line charts. Analysts can choose the parameters to be mapped in the parallel coordinates, and adjust smoothing parameters as well as the time period for the contour-based treemap in TelCoVis [61].…”

“…Anomalous travel behaviors differ from the expected patterns indicated by individual historic records or activities of the crowd. Examples include irregular driving direction [60], [59], hotspots (e.g., crowded neighborhoods) [59], [61], [62], and characteristic travel patterns associated with groups of travelers [20], [63]. These anomalous behaviors can reveal potentially harmful events such as disease outbreaks and terrorist attacks.…”

“…When analysts select a type of data, related geographic information is plotted in the linked map, where arrows on the map indicate physical movement of people. In TelCoVis [61], Wu et al design a contour-based treemap to illustrate the spatial and temporal characteristics of human mobility. By combining with heat map, matrix, and parallel coordinates, analysts gain insights into co-occurrence of human mobility and correlations of co-occurrence.…”