A Unifying Framework for Analysis of Spatial-Temporal Event Sequence Similarity and Its Applications

Abstract

Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among event sequences extracted from time series observed at spatially deployed monitoring locations with the aim of enhancing the understanding of process similarity over time and geospatial locations. We present a framework for a novel matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This unified representation of spatiotemporal event sequences (STES) supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the Jaccard index with temporal order constraints and accommodates different event data types. The approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. As a case study, we demonstrate the use of these similarity measures in a spatiotemporal analysis of event sequences extracted from space time series of a water quality monitoring system.