IEEE Trans Vis Comput Graph. 2018 Mar;24(3):1287-1300. doi: 10.1109/TVCG.2017.2666146. Epub 2017 Feb 8.
Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance.
地理可视化研究集中于各种技术，以表示和探索时空数据。这些技术的目标是使用户能够探索空间和时间上的事件和交互，以便在数据中发现模式、异常和关系。然而，对于没有轨迹信息的非定向统计数据，很难提取和可视化随时间变化的数据流模式。在这项工作中，我们开发了一种新颖的流分析技术，用于提取、表示和分析无轨迹信息的非定向时空数据的流图。我们估计这些事件在空间和时间上的连续分布，并利用重力模型提取空间和时间变化的流场。然后，我们通过使用流可视化技术来可视化数据中的时空模式。用户在地图上看到带有地理参考的离散事件的时间趋势。因此，整体时空数据流模式有助于用户分析带有地理参考的时间事件，如疾病爆发、犯罪模式等。为了验证我们的模型，我们在一个源-目标数据集丢弃轨迹信息，并将我们的技术应用于数据并比较所得到的轨迹和原始轨迹。最后，我们对包括推特数据、海上搜救事件和症状监测在内的统计数据集进行时空趋势分析。
