Parameters to use a fuzzy rulebase approach to remap gridded spatial data

Geographically correlated data are often represented in a gridded format: a grid that covers the region of interest divides the map in cells, and a value is associated with each cell of this grid. This is for instance the case for land use information, air pollution data, etc. The value is considered representative for the entire grid cell, but the grid cell is considered to be the smallest unit. Grids often need to be combined to perform data analysis, and in general do not line up nicely (known as the map overlay problem). In traditional methods, data are considered to be spread uniformly or following some other mathematical distribution over the grid cell, which often is too crude an approximation of the real situation. Treating a cell in such a way immediately introduces errors that are carried on and possibly amplified during subsequent analysis. In general, the problem can be reduced to the problem of remapping data that is presented on one grid onto another grid. To perform this remapping, a novel approach using a fuzzy rulebase has been developed. In this article, the parameters this method are discussed and determined. This discussion gives better insight in the data that is needed to determine the rulebase, which is a first step to an optimization of the rulebase parameters.