The principal aim of the current study is to examine the potential of one network-based crime prediction method. Here, we focus on a network-based variant of ProMap, though we emphasise that the overall approach could be applied to other methods. Since ProMap is essentially a kernel-based method, its adaptation to the network setting relies on the translation of kernel density estimation to network space, for which we build on the approach developed by Okabe et al. (2009). This method has not previously been applied to dynamic prospective crime mapping, and we further extend the approach here by adding a temporal element. The resulting method therefore mirrors the prediction strategy introduced in ProMap, but with calculations based on network space.