Security alert: Generalized deep multi-view representation learning for crime forecasting
Security alert: Generalized deep multi-view representation learning for crime forecasting
复制标题
安全警报：用于犯罪预测的广义深度多视图表示学习
DOI：
10.1111/coin.12504
复制
发表时间：
2022
影响因子：
2.8
通讯作者：
Junwei Yao
中科院分区：
文献类型：
--
作者：
Ziwan Zheng;Yu Xia;Xiaocong Chen;Junwei Yao
犯罪是现代社会的一个焦点问题，影响着社会稳定、公共安全、经济发展和居民生活质量。准确、高精度地预测犯罪发生地点是一个非常重要和有意义的研究方向。随着社交媒体的快速发展（例如，Twitter），在线信息可以作为离线信息（犯罪记录）的有力补充。此外，社区之间的地理信息和出租车流可以模拟社区之间的空间关系，这已经在以前的工作中被证实是有效的。为了有效地解决犯罪预测问题，我们提出了一个用于犯罪预测的广义深度多视图表示学习框架。我们对一个为期4个月的全市范围的数据集进行了广泛的实验，该数据集包括77个社区和22种犯罪类型，结果表明我们的模型提高了对大多数犯罪类型的预测准确性。
Crime is a focal problem in modern society, affecting social stability, public safety, economic development, and life quality of residents. Promptly predicting crime occurrence places in a relatively high accuracy is a very important and meaningful research direction. Via the rapid development of social media (e.g., Twitter), the online information can act as a strong supplement for the offline information (crime records). Additionally, the geographic information and taxi flow between communities can model the spatial relationship between communities, which has already been confirmed effective in previous work. In order to efficiently solve crime prediction problem, we propose a generalized deep multi‐view representation learning framework for crime forecasting. Our extensive experiments on a 4‐month city‐wide dataset that consists of 77 communities and 22 crime types show our model improve the prediction accuracy on most crime types.