Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns
This work addresses the challenge of efficiently mining crime event sequences for law enforcement or urban planning, but it appears incremental as it builds on existing pattern mining methods.
The authors tackled the problem of discovering spatio-temporal sequential patterns in crime data by introducing Constricted Spatio-Temporal Sequential (CSTS) patterns as a concise representation, and their CSTS-Miner algorithm discovered much fewer patterns than four state-of-the-art algorithms in experiments on Pittsburgh and Boston crime datasets.
In this article, we introduce a novel type of spatio-temporal sequential patterns called Constricted Spatio-Temporal Sequential (CSTS) patterns and thoroughly analyze their properties. We demonstrate that the set of CSTS patterns is a concise representation of all spatio-temporal sequential patterns that can be discovered in a given dataset. To measure significance of the discovered CSTS patterns we adapt the participation index measure. We also provide CSTS-Miner: an algorithm that discovers all participation index strong CSTS patterns in event data. We experimentally evaluate the proposed algorithms using two crime-related datasets: Pittsburgh Police Incident Blotter Dataset and Boston Crime Incident Reports Dataset. In the experiments, the CSTS-Miner algorithm is compared with the other four state-of-the-art algorithms: STS-Miner, CSTPM, STBFM and CST-SPMiner. As the results of experiments suggest, the proposed algorithm discovers much fewer patterns than the other selected algorithms. Finally, we provide the examples of interesting crime-related patterns discovered by the proposed CSTS-Miner algorithm.