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		<issn>2179-4847</issn>
		<citationkey>TottiGuiMonEscSan:2025:IdFaDe</citationkey>
		<title>Identificação de fatores determinantes para o cultivo de cacau por meio de regressão espacial no Projeto de Assentamento Itatá, Pará</title>
		<shorttitle>Slides</shorttitle>
		<year>2025</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Totti, Camila,</author>
		<author>Guimaraes, Vinícius Lima,</author>
		<author>Monteiro, Antonio Miguel Vieira,</author>
		<author>Escada, Maria Isabel Sobral,</author>
		<author>Sant’Anna, Sidnei Joao Siqueira,</author>
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		<group>DIOTG-CGCT-INPE-MCTI-GOV-BR</group>
		<group>DIOTG-CGCT-INPE-MCTI-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>camila.andrade@inpe.br</electronicmailaddress>
		<electronicmailaddress>vinicius.lima@inpe.br</electronicmailaddress>
		<electronicmailaddress>miguel.monteiro@inpe.br</electronicmailaddress>
		<electronicmailaddress>isabel.escada@inpe.br</electronicmailaddress>
		<electronicmailaddress>sidnei.santanna@inpe.br</electronicmailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 25 (GEOINFO)</conferencename>
		<conferencelocation>São José dos Campos</conferencelocation>
		<date>19 a 21 mai. 2025</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<transferableflag>1</transferableflag>
		<abstract>O cultivo de cacau, relevante para a economia do Pará, se expande no sudoeste do estado, onde se localiza o Projeto de Assentamento Itatá. O mapeamento dessa cultura e fundamental para compreender sua distribuição  e os fatores espaciais associados a sua ocorrência. Este estudo avaliou a influência de variáveis indicadoras do cultivo de cacau por meio de regressão  espacial, comparando um modelo Conditional Autoregressive Model e um modelo Geographically Weighted Regression. Foram analisadas cinco variáveis independentes derivadas de um estudo previo com a técnica AHP. Os resultados indicam que o GWR apresentou maior capacidade de captar padrões espaciais,  destacando-se as variáveis 'influência do uso e ocupação da terra' e distância  às estradas na distribuição do cultivo. ABSTRACT: Cocoa cultivation, which is important to the economy of Pará, is expanding in the southwest of the state, where the Itata Settlement Project is located. Mapping this crop is essential to understand its distribution and the spatial factors associated with its occurrence. This study evaluated the influence of variables indicating cocoa cultivation through spatial regression, comparing a Conditional Autoregressive Model and a Geographically Weighted Regression model. Five independent variables derived from a previous study using the AHP technique were analyzed. The results indicate that the GWR presented a greater capacity to capture spatial patterns, with emphasis on the variables influence of land use and occupation and distance to roads in the distribution of the crop.</abstract>
		<area>SRE</area>
		<subject>Geoinformação</subject>
		<session>Geoinformação</session>
		<type>Geoinformação</type>
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