TY - JOUR
T1 - Leveraging parallel spatio-temporal computing for crime analysis in large datasets
T2 - analyzing trends in near-repeat phenomenon of crime in cities
AU - Ajayakumar, Jayakrishnan
AU - Shook, Eric
N1 - Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.
AB - Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.
KW - Spatio-temporal
KW - crime analysis
KW - near-repeat patterns
KW - parallel computing
UR - http://www.scopus.com/inward/record.url?scp=85080952263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85080952263&partnerID=8YFLogxK
U2 - 10.1080/13658816.2020.1732393
DO - 10.1080/13658816.2020.1732393
M3 - Article
AN - SCOPUS:85080952263
SN - 1365-8816
VL - 34
SP - 1683
EP - 1707
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
IS - 9
ER -