Security alert: Generalized deep multi‐view representation learning for crime forecasting. (27th January 2022) - Zheng, Ziwan; Xia, Yu; Chen, Xiaocong; Yao, Junwei
Abstract: 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.