Abstract
There are two important problems in clustering of small area statistics: handling of outliers in small area statistics and complexity of spatial distribution of typologies created by classifying small areas. The purpose of this paper is to show a procedure of geographical clustering using small area statistics in order to solve these problems. According to the results of two examinations, Self-Organizing Maps (SOM) constraining the range of updating weights is better classifier than K-means. As to the issue of simplifying spatial distribution of typologies, we showed that there are relations between the level of the spatial smoothing and the spatial extent of the study area.
