Spatial analysis weighting algorithm using Voronoi diagrams

Data layers that represent geographical constraints in a multidimensional GIS model must be appropriately weighted to effectively account for the diversity as well as the functional and spatial interrelationships between the constraints. This paper presents a spatial analysis weighting algorithm (SAWA) using Voronoi diagrams. The basic functions of the SAWA are defined so that the spatialization of weights is done according to two approaches: a global spatialization method based on the statistical distribution of the original data and a contextual approach where neighbourhood defined by Voronoi diagrams is integrated into the weighting functions. Different simulations on artificial and real maps applied to the problem of shortest path optimisation are analysed. The results show that the effective integration of the spatial dimension in a weighting process is not only possible but also improves the optimisation of shortest paths. Research is continuing to improve the contextual phase of the algorithm.