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.

中文翻译：

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利用并行时空计算进行大型数据集中的犯罪分析：分析城市犯罪近乎重复现象的趋势

摘要 犯罪往往在空间和时间上聚集。近重复模式提高了对犯罪可传播性及其时空相互作用的理解。近重复分析需要大量计算资源来评估时空相互作用的统计显着性。计算密集型基于蒙特卡罗模拟的方法用于评估近重复事件背后的时空模式的统计显着性。当前可用的用于识别近似重复模式的软件对于大型犯罪数据集是不可扩展的。在本文中，我们展示了并行空间编程如何帮助在大型数据集中利用基于时空模拟的分析。开发了并行近似重复计算器，并进行了一组实验，将新开发的软件与现有实现进行比较，评估并行计算带来的性能增益，测试软件处理大型犯罪数据集的可扩展性并评估效用用于现实世界犯罪数据分析的新软件。我们的实验结果表明，利用高性能计算和性能优化技术的高效设计的并行算法可用于开发可扩展到大型数据集的软件，并可以为犯罪分析中基于计算密集型统计模拟的方法提供解决方案。测试软件处理大型犯罪数据集的可扩展性，并评估新软件对现实世界犯罪数据分析的效用。我们的实验结果表明，利用高性能计算和性能优化技术的高效设计的并行算法可用于开发可扩展到大型数据集的软件，并可以为犯罪分析中基于计算密集型统计模拟的方法提供解决方案。测试软件处理大型犯罪数据集的可扩展性，并评估新软件对现实世界犯罪数据分析的效用。我们的实验结果表明，利用高性能计算和性能优化技术的高效设计的并行算法可用于开发可扩展到大型数据集的软件，并可以为犯罪分析中基于计算密集型统计模拟的方法提供解决方案。