Autoencoder-based feature extraction of spatial panel data for Brazilian agricultural heterogeneity cluster analysis - Santos, Flávio E. de O.; Silva, Marcos A. S. da; Matos, Leonardo N.; Dompieri, Márcia H. G.; Moura, Fábio R. de (Anais da Escola Regional de Computação Bahia, Alagoas e Sergipe (ERBASE); 2022: Anais da XXII Escola Regional de Computação Bahia, Alagoas e Sergipe, 2022)
Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article proposes a new approach to cluster Brazilian municipalities according to their agricultural production. It combines a feature extraction mechanism using Deep Learning based on Autoencoders and clustering based on k-means and Self-Organizing Maps. We clustered the panel data from IBGE’s annual estimates of Brazilian agricultural production between 1999 and 2018. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the Self-Organizing Maps and the k-means algorithm presented a better result than clustering the raw data using k-means. It demonstrated the ability of simple stacked autoencoders to reduce the dimensionality and create a new space of features in their latent layer where the data can be analyzed and clustered.