Internet-based technologies such as IoT, GPS-based systems, and cellular networks enable the collection of geolocated mobility data of millions of people in large metropolitan areas. In addition, large, public datasets are made available on the Internet by open government programs, providing ways for citizens, NGOs, scientists, and public managers to perform a multitude of data analysis with the goal of better understanding the city dynamics to provide means for evidence-based public policymaking. However, it is challenging to visualize huge amounts of data from mobility datasets. Plotting raw trajectories on a map often causes data occlusion, impairing the visual analysis. Displaying the multiple attributes that these trajectories come with is an even larger challenge. One approach to solve this problem is trail bundling, which groups motion trails that are spatially close in a simplified representation. In this paper, we augment a recent bundling technique to support multi-attribute trail datasets for the visual analysis of urban mobility. Our case study is based on the travel survey from the São Paulo Metropolitan Area, which is one of the most intense traffic areas in the world. The results show that bundling helps the identification and analysis of various mobility patterns for different data attributes, such as peak hours, social strata, and transportation modes.

中文翻译：

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使用捆绑来可视化圣保罗大都市区的多元城市交通结构模式

物联网、基于 GPS 的系统和蜂窝网络等基于互联网的技术能够收集大都市地区数百万人的地理定位移动数据。此外，通过开放的政府计划在 Internet 上提供大型公共数据集，为公民、非政府组织、科学家和公共管理人员提供了执行大量数据分析的方法，目的是更好地了解城市动态，从而为循证公共政策制定。然而，从移动数据集中可视化大量数据是具有挑战性的。在地图上绘制原始轨迹通常会导致数据遮挡，从而影响视觉分析。显示这些轨迹带来的多个属性是一个更大的挑战。解决这个问题的一种方法是线索捆绑，它将空间上接近的运动轨迹分组为简化表示。在本文中，我们增强了最近的捆绑技术，以支持多属性路径数据集，用于城市交通的可视化分析。我们的案例研究基于圣保罗都会区的旅行调查，该地区是世界上交通最繁忙的地区之一。结果表明，捆绑有助于识别和分析不同数据属性的各种出行模式，例如高峰时段、社会阶层和交通方式。这是世界上交通最繁忙的地区之一。结果表明，捆绑有助于识别和分析不同数据属性的各种出行模式，例如高峰时段、社会阶层和交通方式。这是世界上交通最繁忙的地区之一。结果表明，捆绑有助于识别和分析不同数据属性的各种出行模式，例如高峰时段、社会阶层和交通方式。