This article presents a multi‐dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification‐based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime‐prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real‐world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

中文翻译：

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基于分类的多维犯罪空间格局分析与预测模型

本文介绍了旧金山犯罪事件的多维空间格局分析。我们的分析包括空间分辨率对热点识别的影响，犯罪空间格局的时间影响以及各种犯罪类别之间的关系。在这项工作中，犯罪预测被视为一个分类问题。对特定类别进行预测时，将构建一个基于二元分类的模型，并且当考虑所有类别进行分析时，将制定一个多类模型。拟议的犯罪预测模型（HotBlock）利用时空分析来预测一段时间内固定空间区域内的犯罪。在模型参数变化的情况下，它是鲁棒的。将HotBlock的结果与基线实际犯罪数据集进行比较。