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The strength of GIS is in providing a rich data infiastructure for combining disparate data in meaningfid ways by using a spatial arrangement (e.g., proximity). As a toolbox, a GIS allows planners to perform spatial analysis using geo-processing functions such as map overlay, connectivity measurements or thematic map coloring. Although, this makes effective the geographic visualization of individual variables, complex multi-variate dependencies are easily overlooked. The required step to take GIS beyond a tool for automating cartography is to incorporate the ability of analyzing and condensing a large number of geo-referenced variables into a single forecast or score. This is where data mining promises great potential benefits and the reason why there is such a hand-in-glove fit between GIS and data mining. Following the mainstream of this research, we propose to integrate GIS and data mining fitnctionality in a closely coupled open and extensible GIS architecture. This is done by resorting to emerging spatial data mining technology that deals with the substantial complexity added from the spatial dimension. We illustrate an example of topographic map interpretation where resorting to data mining facilities to discover both operational definitions of morphologies characterizing the landscape (i.e., spatial classification rules) and frequent spatial interactions of two or more spatially-referred objects (i.e.. spatial association rules). In both cases, discovered patterns correspond to what geographers, geologists and town planners are interested in while interpreting a map, although they are never explicitly represented in topographic maps or in a GIS-model.
Date of Conference: 17-20 April 2007