Geographic Profiling Through Six-Dimensional Nonparametric Density Estimation

Geographic profiling is the problem of identifying the location of the offender anchor point (offender residence, place of work, etc.) of a linked crime series using the spatial coordinates of the crimes or other information. A standard approach to the problem is 2D kernel density estimation, which relies on the assumption that the anchor point is located in close proximity to the crimes. Recently introduced Bayesian methods allow for a wider range of criminal behaviors, as well as the incorporation of geographic and demographic information. The complexity of these methods, however, make them computationally expensive when implemented. We have developed a nonparametric method for geographic profiling that allows for more complex criminal behaviors than 2D kernel density estimation, but is fast and easy to implement. For this purpose, crime locations and anchor point are considered as one data point in the space of all crime series. Dimension reduction is then used to construct a 6D probability density estimate of offender behavior using historical solved crime series data, from which an anchor point density corresponding to an unsolved series can be computed. We discuss the advantages and disadvantages of the method, as well as possible real-world implementation.