Discovery of crime event sequences with constricted spatio-temporal sequential patterns
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-06-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00780-x |
| _version_ | 1827928666305200128 |
|---|---|
| author | Piotr S. Maciąg Robert Bembenik Artur Dubrawski |
| author_facet | Piotr S. Maciąg Robert Bembenik Artur Dubrawski |
| author_sort | Piotr S. Maciąg |
| collection | DOAJ |
| description | 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 the 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. |
| first_indexed | 2024-03-13T06:10:57Z |
| format | Article |
| id | doaj.art-ee391f5569e2453596401026a1152689 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-13T06:10:57Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-ee391f5569e2453596401026a11526892023-06-11T11:16:28ZengSpringerOpenJournal of Big Data2196-11152023-06-0110113610.1186/s40537-023-00780-xDiscovery of crime event sequences with constricted spatio-temporal sequential patternsPiotr S. Maciąg0Robert Bembenik1Artur Dubrawski2Institute of Computer Science, Warsaw University of TechnologyInstitute of Computer Science, Warsaw University of TechnologyAuton Lab, The Robotics Institute, Carnegie Mellon UniversityAbstract 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 the 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.https://doi.org/10.1186/s40537-023-00780-xData miningSpatio-temporal sequential patternsCrime-data analysisPatterns discoveryConcise representation |
| spellingShingle | Piotr S. Maciąg Robert Bembenik Artur Dubrawski Discovery of crime event sequences with constricted spatio-temporal sequential patterns Journal of Big Data Data mining Spatio-temporal sequential patterns Crime-data analysis Patterns discovery Concise representation |
| title | Discovery of crime event sequences with constricted spatio-temporal sequential patterns |
| title_full | Discovery of crime event sequences with constricted spatio-temporal sequential patterns |
| title_fullStr | Discovery of crime event sequences with constricted spatio-temporal sequential patterns |
| title_full_unstemmed | Discovery of crime event sequences with constricted spatio-temporal sequential patterns |
| title_short | Discovery of crime event sequences with constricted spatio-temporal sequential patterns |
| title_sort | discovery of crime event sequences with constricted spatio temporal sequential patterns |
| topic | Data mining Spatio-temporal sequential patterns Crime-data analysis Patterns discovery Concise representation |
| url | https://doi.org/10.1186/s40537-023-00780-x |
| work_keys_str_mv | AT piotrsmaciag discoveryofcrimeeventsequenceswithconstrictedspatiotemporalsequentialpatterns AT robertbembenik discoveryofcrimeeventsequenceswithconstrictedspatiotemporalsequentialpatterns AT arturdubrawski discoveryofcrimeeventsequenceswithconstrictedspatiotemporalsequentialpatterns |