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首页> 外文期刊>Computers, Environment and Urban Systems >Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area
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Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

机译：将空间和时间纳入随机林模型，分析了大都市区毒品有关犯罪事件的地理空间模式

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摘要

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.
机译：阿片危机已经努力打击美国城市，毒品相关活动的空间和时间模式研究，包括检测和预测涉及特定类型药物的犯罪事件的集群对于区分药物存在的热带，反过来可以进一步提供评估和提供相关治疗服务的基础。在这项研究中，我们调查了超过52,000个报告的毒药有关犯罪事件的时空模式，在2016年至2019年间芝加哥的植物群粒度。我们申请了一个时空分析框架和机器学习方法，以使用培训构建模型确定某些位置和建筑环境和社会血管监测因子是否与毒品相关的犯罪事件模式相关，并建立下列趋势的最大贡献因素。空间和时间与多个驱动因子一起被纳入随机林模型，分析这些变化的模式。我们在模型学习过程中容纳空间和时间自相关，以帮助捕获随时间的变化，并通过预测药物相关的活动热带来测试时空随机林模型的能力。我们特别专注于涉及海洛因和合成药物的犯罪事件，因为这些是在美国阿片类药物危机期间具有高度影响的城市的关键药物类型。

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