Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of crime patterns, (2) collects relevant, openly available data sources to build a GIS-layer with static and dynamic geographical, as well as temporal features relevant to crime, (3) builds a virtual urban environment with these layers, in which individual offender agents navigate, (4) proposes a data-driven decision-making process using machine-learning for the agents to decide whether to engage in criminal activity based on their perception of the environment and, finally, (5) generates fine-grained crime patterns in a simulated urban environment. The novelty of this work lies in the various large-scale data layers, the integration of machine learning at individual agent level to process the data layers, and the high resolution of the resulting predictions. The results show that the spatial, temporal, and interaction layers are all required to predict the top street segments with the highest number of crimes. In addition, the spatial layer is the most informative, which means that spatial data contributes most to predictive performance. Thus, these findings highlight the importance of the inclusion of various open data sources and the potential of theory-informed, data-driven simulations for the purpose of crime prediction. The resulting model is applicable as a predictive tool and as a test platform to support crime reduction.

空间犯罪模拟有助于我们了解驱动犯罪的机制，并可以支持决策者制定有效的减少犯罪策略。近年来出现了整合地理环境以生成犯罪模式的基于代理的模型，尽管数据驱动犯罪模拟很少。本文 (1) 确定了犯罪模式的众多重要驱动因素，(2) 收集相关的、公开可用的数据源，以构建具有静态和动态地理以及与犯罪相关的时间特征的 GIS 层，(3) 构建虚拟具有这些层次的城市环境，其中个体罪犯代理导航，（4）提出了一个数据驱动的决策过程，使用机器学习让代理根据他们对环境的感知决定是否参与犯罪活动，最后, (5) 在模拟的城市环境中生成细粒度的犯罪模式。这项工作的新颖之处在于各种大规模数据层、单个代理级别的机器学习集成以处理数据层，以及由此产生的预测的高分辨率。结果表明，空间、时间和交互层都是预测犯罪率最高的街道段所必需的。此外，空间层的信息量最大，这意味着空间数据对预测性能的贡献最大。因此，这些发现强调了包含各种开放数据源的重要性以及以理论为依据的数据驱动模拟用于犯罪预测的潜力。由此产生的模型可用作预测工具和支持减少犯罪的测试平台。这意味着空间数据对预测性能的贡献最大。因此，这些发现强调了包含各种开放数据源的重要性以及以理论为依据的数据驱动模拟用于犯罪预测的潜力。由此产生的模型可用作预测工具和支持减少犯罪的测试平台。这意味着空间数据对预测性能的贡献最大。因此，这些发现强调了包含各种开放数据源的重要性以及以理论为依据的数据驱动模拟用于犯罪预测的潜力。由此产生的模型可用作预测工具和支持减少犯罪的测试平台。