Título:	A neural qualitative approach for automatic territorial zoning.
Autoria:	MACIEL, R. J. S. SILVA, M. A. S. da MATOS, L. N. DOMPIERI, M. H. G.
Afiliação:	RENATO JOSE SANTOS MACIEL, CNPTIA; MARCOS AURELIO SANTOS DA SILVA, CPATC; UFS; MARCIA HELENA GALINA DOMPIERI, CPATC.
Ano de publicação:	2017
Referência:	A neural qualitative approach for automatic territorial zoning. In: INTERNATIONAL CONFERENCE ON GEOCOMPUTATION, 21., 2017, Leeds. Celebrating 21 years of GeoComputation: extended abstracts. Leeds: University of Leeds, 2017.
Páginas:	p. 1-7.
Conteúdo:	This article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic territorial zoning by combining the handle of categorical data and the other for automatic clustering. The SOM online learning algorithm had been chosen to treat categorical data by using the dot product method and the Sorense-Dice binary similarity coefficient. To automatically perform a spatial clustering, an adaptation of the automatic clustering Costa-Netto algorithm had been also proposed. The correspondence analysis had been used to examine the profiles of each homogeneous zones. To explore the approach it has been performed the territorial zoning of the Alto Taquari River Basin, Brazil, using as input data a set of thematic maps. The results indicate the applicability of the approach to perform the exploratory territorial zoning.
NAL Thesaurus:	Correspondence analysis Zoning Thematic maps
Palavras-chave:	Bacia do Alto Taquari Zoneamento Análise espacial Self-organizing maps Exploratory spatial analysis Similarity coefficients Alto Taquari River Basin
Notas:	GeoComputation 2017.
Tipo do material:	Artigo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPTIA)