  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.
