An Interweaved Time Series Locally Connected Recurrent Neural Network Model on Crime Forecasting

Abstract

Forecasting events like crimes and terrorist activities is a vital important and challenging problem. Researches in recent years focused on qualitative forecasting of a single type event, such as protests or gun crimes. However, events like crimes usually have complicated correlations with each other, and a single type event forecasting cannot meet actual demands. In reality, a quantitative forecasting is more practical for policy making, decision making and police resources allocating. In this paper, we propose an interweaved time series and an interpretative locally connected Recurrent Neural Network model, which forecasts not only whether an event would happen but also how many it would be by each type. Using open source data from Crimes in Chicago provided by Chicago Police Department, we demonstrate our approach more accurately in forecasting the crime events than the existing methods.