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

ABSTRACT

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.

KEYWORDS:

• Spatio-temporal
• parallel computing
• near-repeat patterns
• crime analysis

Acknowledgments

This work used the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC) through allocation TG-SES190004, which is supported by National Science Foundation grant number ACI-1548562. The authors also gratefully acknowledge support from the Minnesota Population Center.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data and Codes Availability Statement

The data and codes that support the findings of this study are available in repository figshare with the identifier https://doi.org/10.6084/m9.figshare.11409996

Additional information

Funding

This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.

Notes on contributors

Jayakrishnan Ajayakumar

Jayakrishnan Ajayakumar is a Research Associate at Department of Population and Quantitative Health Sciences at Case Western Reserve University. He specializes in the area of Geographic Information Sciences (GIS) with a particular focus on developing efficient software methodologies to enable and enhance mixed-method research techniques such as Spatial Video and Spatial Video Geonarratives. His research interests also include geospatial computing.

Eric Shook

Eric Shook is an Assistant Professor in the Department of Geography, Environment, and Society at the University of Minnesota. His research interests include geospatial computing and big spatio-temporal data analytics.