A Visual Analytics Approach to Understanding Spatiotemporal Hotspots

Abstract

As data sources become larger and more complex, the ability to effectively explore and analyze patterns amongst varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contains multiple variables, high signal to noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal datasets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geo-spatiotemporal datasets.

HotSketch: Drawing Police Patrol Routes among Spatiotemporal Crime Hotspots

“…Selecting an appropriate bandwidth is challenging, and Maciejewski et al have explored methods for creating variable spatial bandwidths in the analysis of crime data that help account for the difference in densely and sparsely populated regions [7]. For data that has a temporal component, such as crime, variations of the KDE approach can be applied to the temporal aspects of the data as well.…”

Spatio-temporal disease tracking using news articles

“…Heatmaps are common tools for visualizing the spatial distribution of various datasets, with or without a temporal component. An example of a system that uses heatmaps for spatio-temporal data is that of Maciejewski et al [31], which focuses on the identification of hotspots where many events are clustered. The interface allows selecting data from individual days or constrained geographic regions upon which to generate a heatmap.…”

Visualization of Vessel Traffic

“…Time-series are visualized with a wide range of tools [74,153,157]. Furthermore, trajectories are a specialization of spatio-temporal data [115], such as spread of a disease over time [100], spread of eddies in oceans [166], or migration patterns [25] (see Fig. 2.2).…”