Understanding criminal activities, their structure and dynamics are fundamental for designing tools for crime prediction that can also guide crime prevention. Here, we study crimes committed in city community areas based on police crime reports and demographic data for the City of Chicago collected over 16 consecutive years. Our goal is to understand how the network of city community areas shapes dynamics of criminal offenses and demographic characteristics of their inhabitants. Our results reveal the presence of criminal hot-spots and expose the dynamic nature of criminal activities. We identify the most influential features for forecasting the per capita crime rate in each community. Our results indicate that city community crime is driven by spatio-temporal dynamics since the number of crimes committed in the past among the spatial neighbors of each community area and in the community itself are the most important features in our predictive models. Moreover, certain urban characteristics appear to act as triggers for the spatial spreading of criminal activities. Using the k-Means clustering algorithm, we obtained three clearly separated clusters of community areas, each with different levels of crimes and unique demographic characteristics of the district’s inhabitants. Further, we demonstrate that crime predictive models incorporating both demographic characteristics of a community and its crime rate perform better than models relying only on one type of features. We develop predictive algorithms to forecast the number of future crimes in city community areas over the periods of one-month and one-year using varying sets of features. For one-month predictions using just the number of prior incidents as a feature, the critical length of historical data, τ_c, of 12 months arises. Using more than τ_c months ensures high accuracy of prediction, while using fewer months negatively impacts prediction quality. Using features based on demographic characteristics of the district’s inhabitants weakens this impact somewhat. We also forecast the number of crimes in each community area in the given year. Then, we study in which community area and over what period an increase in crime reduction funding in this area will yield the largest reduction of the crime in the entire city. Finally, we study and compare the performance of various supervised machine learning algorithms classifying reported crime incidents into the correct crime category. Using the temporal patterns of various crime categories improves the classification accuracy. The methodologies introduced here are general and can be applied to other cities for which data about criminal activities and demographics are available.

中文翻译：

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城市社区网络犯罪活动动态

了解犯罪活动、其结构和动态是设计犯罪预测工具的基础，这些工具也可以指导犯罪预防。在这里，我们根据警方犯罪报告和连续 16 年收集的芝加哥市人口数据来研究城市社区地区的犯罪行为。我们的目标是了解城市社区区域网络如何影响刑事犯罪的动态及其居民的人口特征。我们的结果揭示了犯罪热点的存在，并揭示了犯罪活动的动态本质。我们确定了预测每个社区人均犯罪率最有影响力的特征。我们的结果表明，城市社区犯罪是由时空动态驱动的，因为每个社区区域的空间邻居和社区本身过去犯下的犯罪数量是我们的预测模型中最重要的特征。此外，某些城市特征似乎会引发犯罪活动的空间蔓延。使用 k-Means 聚类算法，我们获得了三个清晰分离的社区区域集群，每个集群都有不同的犯罪水平和该地区居民独特的人口统计特征。此外，我们证明，结合社区人口统计特征及其犯罪率的犯罪预测模型比仅依赖一种特征的模型表现更好。我们开发预测算法，使用不同的特征集来预测一个月和一年内城市社区地区未来的犯罪数量。对于仅使用先前事件数量作为特征的一个月预测，历史数据的临界长度τ _c为 12 个月。使用超过τ _c个月可确保预测的高精度，而使用较少的月份会对预测质量产生负面影响。使用基于该地区居民人口特征的特征会在一定程度上削弱这种影响。我们还预测了特定年份每个社区区域的犯罪数量。然后，我们研究在哪个社区区域以及在什么时期内增加该地区减少犯罪资金将带来整个城市犯罪率的最大减少。最后，我们研究并比较各种监督机器学习算法的性能，将报告的犯罪事件分类为正确的犯罪类别。使用各种犯罪类别的时间模式可以提高分类的准确性。这里介绍的方法是通用的，可以应用于有犯罪活动和人口统计数据的其他城市。