IEEE Trans Vis Comput Graph. 2017 Sep;23(9):2120-2136. doi: 10.1109/TVCG.2016.2616404. Epub 2016 Oct 11.
Origin-destination (OD) movement data describe moves or trips between spatial locations by specifying the origins, destinations, start, and end times, but not the routes travelled. For studying the spatio-temporal patterns and trends of mass mobility, individual OD moves of many people are aggregated into flows (collective moves) by time intervals. Time-variant flow data pose two difficult challenges for visualization and analysis. First, flows may connect arbitrary locations (not only neighbors), thus making a graph with numerous edge intersections, which is hard to visualize in a comprehensible way. Even a single spatial situation consisting of flows in one time step is hard to explore. The second challenge is the need to analyze long time series consisting of numerous spatial situations. We present an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps. The aggregated data are used for clustering of time intervals by similarity of the spatial situations. Temporal and spatial displays of the clustering results facilitate the discovery of periodic patterns and longer-term trends in the mass mobility behavior.
出行起讫 (OD) 移动数据通过指定起点、终点、开始和结束时间来描述空间位置之间的移动或出行，但不包括所经过的路线。为了研究大规模移动的时空模式和趋势，许多人的个体 OD 移动被按时间间隔聚合为流（集体移动）。时变流数据为可视化和分析带来了两个难题。首先，流可能连接任意位置（不仅是邻居），从而使图形具有大量的边交叉点，难以以可理解的方式进行可视化。即使是由一个时间步长的流组成的单个空间情况也难以探索。第二个挑战是需要分析由大量空间情况组成的长时间序列。我们提出了一种通过空间和时间抽象来促进对长期流数据进行探索的方法。它涉及一种特殊的数据聚合方式，通过使用示意图地图而不是流图来表示空间情况，从而减少与流图相关的交点和遮挡。聚合后的数据用于通过空间情况的相似性对时间间隔进行聚类。聚类结果的时空显示有助于发现大规模移动行为中的周期性模式和长期趋势。
