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.