Multi-criteria spatial analysis with machine learning algorithm : an application in the South of Brazil

Abstract

This paper explores a multicriteria spatial analysis methodology with a machine learning algorithm, the Classification Tree Analysis (CTA) within Idrisi GIS, to classify and identify homogeneous regions. The proposed approach is tested in a case study carried out in the South of Brazil. All the municipalities were classified and grouped within areas according to similar condition of urban preponderance in socioeconomic and environmental indicators. The results are evaluated and compared with two other methodologies previously implement by the authors: (a) a ranking of municipality through an aggregate index; and (b) using Kohonen´s Self-Organizing Map (SOM) as an unsupervised classifier. The identification of similar areas with analogous socioeconomic and environmental characteristics is important to the development of regional and municipal common sustainable strategies and advances in municipality partnerships.