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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/436TP9L
Repositorysid.inpe.br/mtc-m21c/2020/09.02.14.54   (restricted access)
Last Update2020:09.02.14.54.54 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/09.02.14.54.54
Metadata Last Update2022:01.04.01.35.20 (UTC) administrator
DOI10.1038/s41467-020-17634-2
ISSN2041-1723
Citation KeyFerreiraVeCoCaQuZhMa:2020:SpDaAn
TitleSpatiotemporal data analysis with chronological networks
Year2020
MonthDec.
Access Date2024, May 09
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4379 KiB
2. Context
Author1 Ferreira, Leonardo N.
2 Vega-Oliveros, Didier A.
3 Cotacallapa, Frank Moshé
4 Cardoso, Manoel Ferreira
5 Quile, Marcos G.
6 Zhao, Liang
7 Macau, Elbert Einstein Nehrer
Resume Identifier1
2
3
4
5
6
7 8JMKD3MGP5W/3C9JGUT
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2
3 CMS-ETES-SESPG-INPE-MCTIC-GOV-BR
4 COCST-COCST-INPE-MCTIC-GOV-BR
5
6
7 LABAC-COCTE-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Indiana University
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Universidade Federal de São Paulo (UNIFESP)
6 Universidade de São Paulo (USP)
7 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 ferreira@leonardonascimento.com
2
3 frank.moshe@inpe.br
4 manoel.cardoso@inpe.br
5
6
7 elbert.macau@inpe.br
JournalNature Communications
Volume11
Number1
Pagese4036
Secondary MarkA1_ZOOTECNIA_/_RECURSOS_PESQUEIROS A1_MEDICINA_VETERINÁRIA A1_CIÊNCIAS_BIOLÓGICAS_II A1_ASTRONOMIA_/_FÍSICA A2_ENGENHARIAS_IV C_CIÊNCIAS_BIOLÓGICAS_I
History (UTC)2020-09-02 14:54:54 :: simone -> administrator ::
2022-01-04 01:35:20 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
AbstractThe 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.
AreaCOMP
Arrangement 1urlib.net > Produção anterior à 2021 > LABAC > Spatiotemporal data analysis...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Spatiotemporal data analysis...
Arrangement 3urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CMS > Spatiotemporal data analysis...
Arrangement 4urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Spatiotemporal data analysis...
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4. Conditions of access and use
Languageen
Target Fileferreira_spatiotemporal.pdf
User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/3F358GL
8JMKD3MGPCW/3F3T29H
DisseminationWEBSCI
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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