Accounting for spatial uncertainty in optimization with spatial decision support systems

Spatial decision support systems (SDSS) are designed to make complex resource allocation problems more transparent and to support the design and evaluation of allocation plans. Recent developments in this field focus on the design of allocation plans using optimization techniques. In this paper we analyze how uncertainty in spatial (input) data propagates through, and affects the results of, an optimization model. The optimization model calculates the optimal location for a ski run based on a slope map, which is derived from a digital elevation model (DEM). The uncertainty propagation is a generic method following a Monte Carlo approach, whereby realizations of the spatially correlated DEM error are generated using 'sequential Gaussian simulation'. We successfully applied the methodology to a case study in the Austrian Alps, showing the influence of spatial uncertainty on the optimal location of a ski run and the associated development costs. We also discuss the feasibility of routine incorporation of uncertainty propagation methodologies in an SDSS.