Modelling Spatial Relations by Generalized Proximity Matrices

One of the main challenges for the development of spatial information theory is the formalization of the concepts of space and spatial relations. Currently, most spatial data structures and spatial analytical methods used in GIS embody the notion of space as a set of absolute locations in a Cartesian coordinate system, thus failing to incorporate spatial relations which are dependent on topological connections and fluxes between physical or virtual networks. To answer this challenge, we introduce the idea of a generalized proximity matrix (GPM) , an extension of the spatial weights matrix where the weights are computed taking into account both absolute space relations such as Euclidean distance or adjacency and relative space relations such as network connection. Using the GPM, two geographic objects (e.g. municipalities) can be considered "near" each other if they were connected through a transportation or telecommunication network, even if thousands of kilometers apart, or, using even more abstract concepts, if they are part of the same productive chain in a given economical activity. The generalized proximity matrix allows the extension of spatial analysis formalisms and techniques such as spatial autocorrelation indicators and spatial regression models to incorporate relations on relative space, providing a new way for exploring complex spatial patterns and non-local relationships in spatial statistics. The GPM can also be used as a support for map algebra operations and cellular automata models.