  Making high-quality decisions in strategic spatial planning is heavily
dependent on extracting knowledge from vast amounts of data. Although many
decision-making problems like developing urban areas require such perception
and reasoning, existing methods in this field usually neglect the deep
knowledge mined from geographic databases and are based on pure statistical
methods. Due to the large volume of data gathered in spatial databases, and the
uncertainty of spatial objects, mining association rules for high-level
knowledge representation is a challenging task. Few algorithms manage
geographical and non-geographical data using topological relations. In this
paper, a novel approach for spatial data mining based on the MOSES evolutionary
framework is presented which improves the classic genetic programming approach.
A hybrid architecture called GGeo is proposed to apply the MOSES mining rules
considering fuzzy topological relations from spatial data. The uncertainty and
fuzziness aspects are addressed using an enriched model of topological
relations by fuzzy region connection calculus. Moreover, to overcome the
problem of time-consuming fuzzy topological relationships calculations, this a
novel data pre-processing method is offered. GGeo analyses and learns from
geographical and non-geographical data and uses topological and distance
parameters, and returns a series of arithmetic-spatial formulas as
classification rules. The proposed approach is resistant to noisy data, and all
its stages run in parallel to increase speed. This approach may be used in
different spatial data classification problems as well as representing an
appropriate method of data analysis and economic policy making.
