A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

Abstract

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknow n random output variables. Though Monte Carlo simulation is a powerful technique for the probabilistic understanding of many processes, it can only be applied if it is possible to infer the probability distributions describing the required input variables. This is particularly challenging when the input probability distributions are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations dependent on population counts.