Visualizing Large-Scale Spatial Time Series with GeoChron

Fig. 1: Two-levenl visualizations (left and right) in GeoChron. (A) A snapshot of Storyline. (B) Entities in (A) are recolored. (C) The spatial distribution of (B). (D) The spatial distribution and EvoLens ((E) line charts and (F) trend motifs) of evolution patterns in (B).

AbstractÐIn geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

Spatiotemporal phenomena (e.g., traffic conditions, air pollution, rainfall, and temperature) are continuously monitored by geo-referred sensors, generating large-scale spatial time series (hereafter ªST seriesº) in many domains, such as geography [15], atmospheric science [24,46], and urban informatics [9,27,70]. ST series visualization is one of the important means of understanding spatiotemporal phenomena.

Traditionally, ST series are first depicted in temporal visualizations, e.g., line charts. These visualizations are then either plotted on a map by their geographic positions [54,65], or displayed in a separate view that is coordinated with a map [43], and thereby can be related back to the geographic context. The above methods, considered as the strategy of direct depiction [37], are not effective for large-scale ST series. An analyst may find it difficult to browse the spatial distribution in the vast