Data/hora: 11/06/2024 07:54:30

Biblioteca(s): 	Embrapa Agricultura Digital; Embrapa Tabuleiros Costeiros.	
Data corrente: 	06/09/2017	
Data da última atualização: 	21/01/2020	
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.	
Título: 	A neural qualitative approach for automatic territorial zoning.	
Ano de publicação: 	2017	
Fonte/Imprenta: 	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.	
Idioma: 	Inglês
Português	
Notas: 	GeoComputation 2017.	
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.	
Palavras-Chave: 	Alto Taquari River Basin; Análise espacial; Bacia do Alto Taquari; Exploratory spatial analysis; Maps; Self-organizing maps; Similarity coefficients; Zoneamento.	
Thesagro: 	Mapa; Recurso hídrico; Rio.	
Thesaurus Nal: 	Correspondence analysis; Thematic maps; Zoning.	
Categoria do assunto: 	--
X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/171403/1/Neural-quantitative-Maciel-Geocomputing.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CPATC - (24980-1) - UPC - AA - DD -  -  -  - DIS
CNPTIA - (19589-1) - UPC - AA - DD -  -  -  - DIS