The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the discovery of spatial knowledge. Nowadays, very rich databases of spatially referenced socio-economic data are available from local statistical offices and in the last few years the demand of spatially detailed statistical data is dramatically increased. Extracting interesting and useful patterns from spatial data sets is more difficult than extracting corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional techniques (i.e. data mining) for extracting spatial patterns. Moreover, the area definition and the assignment of the data to appropriate areas can pose problems in the estimation process. This paper presents statistical methods which face these problems and analyze the geographical pattern of the spatially referenced socio-economic data by incorporating the spatial location as an additional covariate.