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		<doi>10.1038/s41467-020-17634-2</doi>
		<issn>2041-1723</issn>
		<citationkey>FerreiraVeCoCaQuZhMa:2020:SpDaAn</citationkey>
		<title>Spatiotemporal data analysis with chronological networks</title>
		<year>2020</year>
		<month>Dec.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Ferreira, Leonardo N.,</author>
		<author>Vega-Oliveros, Didier A.,</author>
		<author>Cotacallapa, Frank Moshé,</author>
		<author>Cardoso, Manoel Ferreira,</author>
		<author>Quile, Marcos G.,</author>
		<author>Zhao, Liang,</author>
		<author>Macau, Elbert Einstein Nehrer,</author>
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		<group>LABAC-COCTE-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Indiana University</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade Federal de São Paulo (UNIFESP)</affiliation>
		<affiliation>Universidade de São Paulo (USP)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>ferreira@leonardonascimento.com</electronicmailaddress>
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		<electronicmailaddress>frank.moshe@inpe.br</electronicmailaddress>
		<electronicmailaddress>manoel.cardoso@inpe.br</electronicmailaddress>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>elbert.macau@inpe.br</electronicmailaddress>
		<journal>Nature Communications</journal>
		<volume>11</volume>
		<number>1</number>
		<pages>e4036</pages>
		<secondarymark>A1_ZOOTECNIA_/_RECURSOS_PESQUEIROS A1_MEDICINA_VETERINÁRIA A1_CIÊNCIAS_BIOLÓGICAS_II A1_ASTRONOMIA_/_FÍSICA A2_ENGENHARIAS_IV C_CIÊNCIAS_BIOLÓGICAS_I</secondarymark>
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		<abstract>The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.</abstract>
		<area>COMP</area>
		<language>en</language>
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