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Making high-quality decisions in strategic spatial planning is heavilydependent on extracting knowledge from vast amounts of data. Although manydecision-making problems like developing urban areas require such perceptionand reasoning, existing methods in this field usually neglect the deepknowledge mined from geographic databases and are based on pure statisticalmethods. Due to the large volume of data gathered in spatial databases, and theuncertainty of spatial objects, mining association rules for high-levelknowledge representation is a challenging task. Few algorithms managegeographical and non-geographical data using topological relations. In thispaper, a novel approach for spatial data mining based on the MOSES evolutionaryframework is presented which improves the classic genetic programming approach.A hybrid architecture called GGeo is proposed to apply the MOSES mining rulesconsidering fuzzy topological relations from spatial data. The uncertainty andfuzziness aspects are addressed using an enriched model of topologicalrelations by fuzzy region connection calculus. Moreover, to overcome theproblem of time-consuming fuzzy topological relationships calculations, this anovel data pre-processing method is offered. GGeo analyses and learns fromgeographical and non-geographical data and uses topological and distanceparameters, and returns a series of arithmetic-spatial formulas asclassification rules. The proposed approach is resistant to noisy data, and allits stages run in parallel to increase speed. This approach may be used indifferent spatial data classification problems as well as representing anappropriate method of data analysis and economic policy making.

A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm