Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations

Abstract

Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.