Discovering co-occurrence patterns of heterogeneous events from unevenly-distributed spatiotemporal data

Spatiotemporal co-occurrence patterns represent subsets of event features that are often located together in space and time. However, such spatiotemporal co-occurrence patterns can fail to capture disaster-related events that often occur unexpectedly and in limited regions and limited time intervals. In addition, previous studies for discovering co-occurrence patterns do not consider using patterns for prediction problem. In this paper, we define the problem of discovering co-occurrence patterns, each of which is annotated with a valid spatial and temporal subregion. We also define a new interest measure of cooccurrence patterns for prediction problem and then based on this measure, we propose a method for discovering such co-occurrence patterns in form of association rules by incorporating repeatedly spatiotemporal clusterings to remove spatiotemporal bias. Our algorithm is suitable to large datasets. We evaluate our method for real-world datasets by discovering and then predicting traffic disaster events co-occurring with torrential rain events in Kansai area, Japan. By only using 80% most interesting discovered patterns, our experimental result shows 24% improvement of prediction performance on F-measure against a baseline.