An Integrated Graph Model for Spatial-Temporal Urban Crime Prediction Based on Attention Mechanism
An Integrated Graph Model for Spatial-Temporal Urban Crime Prediction Based on Attention Mechanism
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DOI：
10.3390/ijgi11050294
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Miaomiao Hou;Xiaofeng Hu;Jitao Cai;Xinge Han;Shuaiqi Yuan
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文献类型：
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作者：
Miaomiao Hou;Xiaofeng Hu;Jitao Cai;Xinge Han;Shuaiqi Yuan
犯罪问题由于其意想不到的和巨大的后果，已经引起了市民和城市管理者的广泛关注。数据驱动的时空犯罪预测可以提供与犯罪热点相关的合理估计，是预防和控制城市犯罪的一种有效技术。从而有助于在有限资源下相关部门的决策，促进城市的文明发展。然而，在城市-区域尺度的日常犯罪时空预测方面的不足需要进一步解决，这对警察资源配置至关重要。为了建立一个实用有效的城市警区尺度的日常犯罪预测框架，提出了一种“在线”集成图模型。在该模型中，残差神经网络（ResNet）、图卷积网络（GCN）和长短期记忆（LSTM）与注意机制相结合，提取并融合时空特征、拓扑图和外部特征。然后，通过2015年1月1日至2020年1月7日美国芝加哥市22个警区的日常盗窃和袭击数据验证了“在线”集成图形模型。此外，使用相同的数据集，从定量的角度比较了几种广泛使用的基线模型，包括自回归综合移动平均（ARIMA）、脊回归、支持向量回归（SVR）、随机森林、极端梯度增强（XGBoost）、LSTM、卷积神经网络（CNN）和卷积-LSTM模型。结果表明，该模型预测的时空格局与观测值较为接近。此外，由于其平均绝对误差（MAE）和均方根误差（RMSE）的平均值较其他八种模型低，因此综合图模型的性能更准确。因此，所提出的模型在支持警察在巡逻和侦查领域的决策以及资源配置方面具有很大的潜力。
Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial–temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an “online” integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial–temporal features, topological graphs, and external features. Then, the “online” integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial–temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation.