Supporting Event-based Geospatial Anomaly Detection with Geovisual Analytics

Abstract

Collecting multiple geospatial datasets that describe the same real-world events can be useful in monitoring and enforcement situations (e.g., independently tracking where a fishing vessel travelled and where it reported to have fished). While finding the obvious anomalies between such datasets may be a simple task, discovering more subtle inconsistencies can be challenging when the datasets describe many events that cover large geographic and temporal ranges. This paper presents a geovisual analytics approach to this problem domain, automatically extracting potential event anomalies from the data, visualizing these on a map, and providing interactive filtering tools to allow expert analysts to discover and analyze patterns that are of interest. A case study is presented, illustrating the value of the approach for discovering anomalies between commercial fishing vessel movement data and their reported fishing locations. Field trial evaluations confirm the benefits of this geovisual analytics approach for supporting real-world data analyst needs.