Catastrophe models calculate the stochastic distributions of loss originating from events like hurricanes and earthquakes. These models are typically based on a stochastic event catalog. For each event spatial correlation needs to be simulated. The standard approach is based on the evaluation of a copula. However the complexity of the corresponding algorithm is O(n3) and it becomes difficult to execute for n in the thousands.
So as the number n of locations in the footprint of the event grows, this approach quickly becomes infeasible. We propose a slight modification of the well-known Kriging technique, in order to solve this problem. With our solution the creation of simulation data for catastrophe models becomes manageable with the use of Big Data techniques.