Spatial analysis weighting algorithm using Voronoi diagrams

Data layers that represent geographical constraints in a multidimensional G IS model must be appropriately weighted to effectively account for the dive rsity as well as the functional and spatial interrelationships between the constraints. This paper presents a spatial analysis weighting algorithm (SA WA) using Voronoi diagrams. The basic functions of the SAWA are defined so that the spatialization of weights is done according to two approaches: a g lobal spatialization method based on the statistical distribution of the or iginal data and a contextual approach where neighbourhood defined by Vorono i diagrams is integrated into the weighting functions. Different simulation s on artificial and real maps applied to the problem of shortest path optim isation are analysed. The results show that the effective integration of th e spatial dimension in a weighting process is not only possible but also im proves the optimisation of shortest paths. Research is continuing to improv e the contextual phase of the algorithm.