Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
    The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Método simples e eficiente de geolocalizar dados no Brasil. O 
    pacote é baseado em conjuntos de dados espaciais abertos de endereços 
    brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços 
    para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro 
    de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia 
    do Brasil. (A simple and efficient method for geolocating data in Brazil. The 
    package is based on open spatial datasets of Brazilian addresses, primarily 
    using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), 
    published by the Instituto Brasileiro de Geografia e Estatística (IBGE), 
    Brazil's official statistics and geography agency.)

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.