Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information
Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information
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DOI：
10.1109/icbk.2017.3
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发表时间：
2017-08
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影响因子：
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通讯作者：
Yong Zhuang;Matthew Almeida;Melissa Morabito;W. Ding
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文献类型：
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作者：
Yong Zhuang;Matthew Almeida;Melissa Morabito;W. Ding
犯罪是美国的一个主要社会问题，威胁着公共安全，扰乱着经济。了解犯罪活动的模式可以预测未来的高风险犯罪“热点”，并使警察分局能够更有效地分配人员，以防止或应对事件。随着国家和组织收集和存储跟踪犯罪发生的详细数据的能力不断提高，已经收集了大量具有空间和时间信息的数据。如何利用海量时空信息的优势对区域犯罪率进行精确预测成为必要。递归神经网络模型已被广泛证明是有效的检测时间序列中的时间模式。在这项研究中，我们提出了时空神经网络（STNN），以精确预测犯罪热点嵌入空间信息。我们使用波特兰，俄勒冈州警察局（PPB）从2012年3月到2016年12月底的5年期间提供的呼叫服务数据来评估该模型。我们表明，我们的STNN模型优于一些经典的机器学习方法和一些替代的神经网络架构。
Crime is a major social problem in the United States, threatening public safety and disrupting the economy. Understanding patterns in criminal activity allows for the prediction of future high-risk crime “hot spots” and enables police precincts to more effectively allocate officers to prevent or respond to incidents. With the ever-increasing ability of states and organizations to collect and store detailed data tracking crime occurrence, a significant amount of data with spatial and temporal information has been collected. How to use the benefit of massive spatial-temporal information to precisely predict the regional crime rates becomes necessary. The recurrent neural network model has been widely proven effective for detecting the temporal patterns in a time series. In this study, we propose the Spatio-Temporal neural network (STNN) to precisely forecast crime hot spots with embedding spatial information. We evaluate the model using call-for-service data provided by the Portland, Oregon Police Bureau (PPB) for a 5-year period from March 2012 through the end of December 2016. We show that our STNN model outperforms a number of classical machine learning approaches and some alternative neural network architectures.