The traditional categorization of crime types relies on a hierarchical structure, from high-level categories to lower-level subtypes. This tree-based classification treats crime types as mutually independent when they do not branch from the same higher-level category, therefore lacking inter-category semantic relations. The issue then extends over crime distribution analysis of urban regions, often reporting statistics based on crime type counts, but neglecting implicit relations between different crime categories. Our study aims to fill this information gap, providing a more complete understanding of urban crime in both qualitative and quantitative terms. Specifically, we propose a vector-based crime type representation, constructed via unsupervised machine learning on temporal and geographic factors. The general idea is to define crime types as “related” if they often occur in the same area at the same time span, regardless of any initial hierarchical categorization. This opens to a new metric of comparison that goes beyond pre-defined structures, revealing hidden relationships between crime types by generating a vector space in a completely data-driven manner. Crime types are represented as points in this space, and their relative distances disclose stronger or weaker semantic relations. A direct application on urban crime distribution analysis stands out in the form of visualization tools for intuitive data investigations and convenient comparison measures on composite vectors of urban regions. Meaningful insights on crime type distributions and a better understanding of urban crime characteristics determine a valuable asset to urban management and development.

中文翻译：

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CrimeVec-探索基于时空的城市犯罪类型和与犯罪有关的城市地区的矢量表示

犯罪类型的传统分类依赖于从高级别类别到低级别子类型的层次结构。当基于犯罪类型而不是从同一较高级别的类别中分支出来时，这种基于树的分类将犯罪类型视为相互独立，因此缺乏类别间的语义关系。然后，问题扩展到城市地区的犯罪分布分析，通常基于犯罪类型计数报告统计信息，但忽略了不同犯罪类别之间的隐式关系。我们的研究旨在填补这一信息空白，从定性和定量的角度更全面地了解城市犯罪。具体来说，我们提出了一种基于矢量的犯罪类型表示形式，它是通过对时间和地理因素进行无监督的机器学习来构造的。一般的想法是，如果犯罪类型经常出现在同一地区的同一时间跨度，则将其定义为“相关”类型，而与任何初始等级分类无关。这开启了一种新的比较指标，该指标超越了预定义的结构，通过以完全数据驱动的方式生成向量空间，揭示了犯罪类型之间的隐藏关系。犯罪类型以该空间中的点表示，它们的相对距离显示出更强或更弱的语义关系。以可视化工具的形式在城市犯罪分布分析中的直接应用脱颖而出，可以直观地进行数据调查，并可以方便地对城市区域的复合矢量进行比较。