Fuzzy Geo-processing For Characterization OfSocial Groups: An Application To ABrazilian Mid-size City

This paper presents a method for the spatial representation of social-economic groups. This work is based on the Brazilian census geo-referenced data for the revenue and education level of 540 districts in a mid-size city. The data was analyzed by k-means clustering algorithms for determination of groups of similar behavior based only on the revenue and education level data. The groups were then plotted into the city map using geo-referenced information. The aim of this study is to analyze the spatial distribution of groups of equivalent socio-economic levels, taking into account the uncertainty of the classification process. The results show that the model is able to represent the distribution of the social groups in an inter-related and continuous space.