Evaluating temporally weighted kernel density methods for predicting the next event location in a series

摘要: One aspect of tactical crime or terrorism analysis is predicting the location next event in a series. The objective this article to present methodology identify optimal parameters and test performance temporally weighted kernel density estimation models for criminal terrorist By placing series space–time point pattern framework, prediction are shown be based on estimating conditional spatial function. We use temporal weights that indicate how much influence past events have toward future locations, which can also incorporate uncertainty timing. Results applying Baltimore County, MD, vary greatly by type little length fairly robust choice bandwidth.