Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities

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 inte...

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Bibliographic Details
Published in:International journal of geographical information science : IJGIS Vol. 34; no. 9; pp. 1683 - 1707
Main Authors: Ajayakumar, Jayakrishnan, Shook, Eric
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 01.09.2020
Taylor & Francis LLC
Subjects:
Algorithms
Computer programs
Computer simulation
Crime
crime analysis
Data analysis
Datasets
Monte Carlo simulation
near-repeat patterns
Optimization
Optimization techniques
parallel computing
Parallel processing
Software
Software development
Spatio-temporal
Statistical significance
Statistics
ISSN:1365-8816, 1362-3087, 1365-8824
Online Access:Get full text
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Summary: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.
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ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2020.1732393