Background Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects. Methods We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics. Results The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon. Limitations Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems. Conclusions There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction. Implications Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study’s key data items.

中文翻译：

---

对空间犯罪预测的系统评价

背景 以时空为重点的预测性警务和犯罪分析越来越受到各种科学界的关注，并且已经作为有效的警务工具实施。本文的目的是概述和评估空间犯罪预测的最新技术，重点关注研究设计和技术方面。方法 我们遵循 PRISMA 指南报告这一系统性文献综述，我们分析了 2000 年至 2018 年的 32 篇论文，这些论文是从 786 篇进入筛选阶段的论文和总共 193 篇通过资格阶段的论文中选出的。资格阶段包括几个标准，这些标准分为：（a）出版物类型，（b）与研究范围的相关性，以及（c）研究特征。结果 最主要的预测推理类型是热点（即二元分类）方法。主要使用传统的机器学习方法，但也使用基于核密度估计的方法，以及较少使用的点过程和深度学习方法。评估性能的最高指标是预测准确度，其次是预测准确度指数和 F1 分数。最后，最常见的验证方法是训练-测试拆分，而其他方法包括交叉验证、留一法和滚动水平。局限性 当前的研究通常缺乏对研究实验、特征工程程序的清晰报告，并且使用不一致的术语来解决类似问题。结论 由于不同背景的学者所做的跨学科技术工作，空间犯罪预测研究取得了显着增长。这些研究解决了了解和打击犯罪的社会需求以及执法部门对几乎实时预测的兴趣。影响 虽然我们发现了一些机会和优势，但我们也针对一些弱点和威胁提供了建议。未来的研究不应忽视（现有）算法的并列，其中数量不断增加（我们招募了 66 个）。为了允许研究的比较和可重复性，我们概述了对空间预测方法的协议或标准化的需求，并建议报告研究的关键数据项。