Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns

TL;DR

This paper introduces Constricted Spatio-Temporal Sequential (CSTS) patterns, a concise representation of all spatio-temporal patterns in crime data, and presents CSTS-Miner, an algorithm that efficiently discovers significant patterns with fewer results than existing methods.

Contribution

The paper proposes CSTS patterns as a novel, concise representation and develops CSTS-Miner, an algorithm that outperforms existing methods in discovering significant crime-related patterns.

Findings

CSTS-Miner discovers fewer, more significant patterns.

CSTS patterns effectively summarize complex spatio-temporal data.

Experimental results validate the efficiency and relevance of the proposed method.

Abstract

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.