Visualization of multivariate spatiotemporal data through graph wavelet transform

Os dados espaço-temporais são de grande valor, já que contextos sociais e ambientais comumente apresentam padrões e relações espaciais e temporais (e até mesmo espaço-temporais) importantes. A complexidade desses dados aumenta ao considerar as relações entre diferentes variáveis espaço-temporais. As...

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Detalles Bibliográficos
Autor: Valdrighi, Giovani de Almeida
Tipo de recurso: tesis de maestría
Estado:Versión publicada
Fecha de publicación:2024
País:Brasil
Institución:Fundação Getulio Vargas (FGV)
Repositorio:Repositório Institucional do FGV (FGV Repositório Digital)
Idioma:inglés
OAI Identifier:oai:repositorio.fgv.br:10438/35440
Acceso en línea:https://hdl.handle.net/10438/35440
Access Level:acceso abierto
Palabra clave:Espaço-temporal
Visualização
Multivariado
Processamento de sinais em grafos
Visualization
Spatiotemporal
Multivariate
Graph signal processing
Aprendizado do computador
Análise multivariada
Análise espacial (Estatística)
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dc.title.eng.fl_str_mv Visualization of multivariate spatiotemporal data through graph wavelet transform
title Visualization of multivariate spatiotemporal data through graph wavelet transform
spellingShingle Visualization of multivariate spatiotemporal data through graph wavelet transform
Valdrighi, Giovani de Almeida
Espaço-temporal
Visualização
Multivariado
Processamento de sinais em grafos
Visualization
Spatiotemporal
Multivariate
Graph signal processing
Aprendizado do computador
Visualização
Análise multivariada
Análise espacial (Estatística)
title_short Visualization of multivariate spatiotemporal data through graph wavelet transform
title_full Visualization of multivariate spatiotemporal data through graph wavelet transform
title_fullStr Visualization of multivariate spatiotemporal data through graph wavelet transform
title_full_unstemmed Visualization of multivariate spatiotemporal data through graph wavelet transform
title_sort Visualization of multivariate spatiotemporal data through graph wavelet transform
dc.creator.none.fl_str_mv Valdrighi, Giovani de Almeida
author Valdrighi, Giovani de Almeida
author_facet Valdrighi, Giovani de Almeida
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
dc.contributor.member.none.fl_str_mv Ferreira Júnior, Nivan Roberto
Santos, Emanuele Marques Rodrigues
Miranda, Fábio Markus Nunes
dc.contributor.advisor1.fl_str_mv Poco, Jorge
contributor_str_mv Poco, Jorge
dc.subject.por.fl_str_mv Espaço-temporal
Visualização
Multivariado
Processamento de sinais em grafos
topic Espaço-temporal
Visualização
Multivariado
Processamento de sinais em grafos
Visualization
Spatiotemporal
Multivariate
Graph signal processing
Aprendizado do computador
Visualização
Análise multivariada
Análise espacial (Estatística)
dc.subject.eng.fl_str_mv Visualization
Spatiotemporal
Multivariate
Graph signal processing
dc.subject.bibliodata.por.fl_str_mv Aprendizado do computador
Visualização
Análise multivariada
Análise espacial (Estatística)
description Os dados espaço-temporais são de grande valor, já que contextos sociais e ambientais comumente apresentam padrões e relações espaciais e temporais (e até mesmo espaço-temporais) importantes. A complexidade desses dados aumenta ao considerar as relações entre diferentes variáveis espaço-temporais. As técnicas de visualização permitem que especialistas realizem uma análise interativa desses dados complexos. Diversos trabalhos já foram realizados na visualização de séries temporais geolocalizadas, principalmente utilizando múltiplas visualizações vinculadas a um mapa. Recentemente, a Transformada Wavelet também tem sido empregada para lidar com os padrões espaço-temporais presentes nos dados, criando uma descrição do sinal em diferentes frequências. No entanto, essas técnicas consideram a análise do cenário unidimensional e não apresentam adaptações que considerem o caso multivariado. Neste trabalho, apresentamos uma metodologia para estender o uso da Transformada Wavelet para cenários multivariados, permitindo extrair eventos de interesse de dados de múltiplas fontes. Essa metodologia foi empregada para analisar o desmatamento da Floresta Amazônica, considerando as diferentes atividades humanas que ocorrem na região. Posteriormente, essa metodologia foi incorporada a um sistema desenvolvido para a análise visual e interativa de dados multivariados espaço-temporais, chamado \textit{ManyWaves}. Utilizando a maioria das capacidades da Transformada Wavelet, nossa abordagem não apresenta um alto custo computacional e pode lidar tanto com mudanças espaciais quanto temporais.
publishDate 2024
dc.date.accessioned.fl_str_mv 2024-06-14T16:57:53Z
dc.date.available.fl_str_mv 2024-06-14T16:57:53Z
dc.date.issued.fl_str_mv 2024-05-22
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/35440
url https://hdl.handle.net/10438/35440
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional do FGV (FGV Repositório Digital)
instname:Fundação Getulio Vargas (FGV)
instacron:FGV
instname_str Fundação Getulio Vargas (FGV)
instacron_str FGV
institution FGV
reponame_str Repositório Institucional do FGV (FGV Repositório Digital)
collection Repositório Institucional do FGV (FGV Repositório Digital)
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spelling Escolas::EMApFerreira Júnior, Nivan RobertoSantos, Emanuele Marques RodriguesMiranda, Fábio Markus NunesPoco, Jorge 2024-06-14T16:57:53Z2024-06-14T16:57:53Z2024-05-22https://hdl.handle.net/10438/35440Os dados espaço-temporais são de grande valor, já que contextos sociais e ambientais comumente apresentam padrões e relações espaciais e temporais (e até mesmo espaço-temporais) importantes. A complexidade desses dados aumenta ao considerar as relações entre diferentes variáveis espaço-temporais. As técnicas de visualização permitem que especialistas realizem uma análise interativa desses dados complexos. Diversos trabalhos já foram realizados na visualização de séries temporais geolocalizadas, principalmente utilizando múltiplas visualizações vinculadas a um mapa. Recentemente, a Transformada Wavelet também tem sido empregada para lidar com os padrões espaço-temporais presentes nos dados, criando uma descrição do sinal em diferentes frequências. No entanto, essas técnicas consideram a análise do cenário unidimensional e não apresentam adaptações que considerem o caso multivariado. Neste trabalho, apresentamos uma metodologia para estender o uso da Transformada Wavelet para cenários multivariados, permitindo extrair eventos de interesse de dados de múltiplas fontes. Essa metodologia foi empregada para analisar o desmatamento da Floresta Amazônica, considerando as diferentes atividades humanas que ocorrem na região. Posteriormente, essa metodologia foi incorporada a um sistema desenvolvido para a análise visual e interativa de dados multivariados espaço-temporais, chamado \textit{ManyWaves}. Utilizando a maioria das capacidades da Transformada Wavelet, nossa abordagem não apresenta um alto custo computacional e pode lidar tanto com mudanças espaciais quanto temporais.Spatiotemporal data is highly valuable as many social and environmental systems present important spatial and temporal (and even spatiotemporal) patterns and relations. The complexity of this data increases when considering relations between different spatiotemporal variables. Visualization techniques enable experts to perform an interactive analysis of this complex data. Diverse work has already been done on visualizing geolocated time series, mainly using multiple views linked to a map. The Wavelet Transform has recently been employed to tackle the spatiotemporal patterns in the data, creating a description of the signal at different frequencies. However, these techniques consider analyzing the unidimensional scenario without support for adaptations that consider the multivariate case. We present a methodology to extend Wavelet Transform to multivariate scenarios, allowing us to extract events of interest from data from multiple sources. This methodology was employed to analyze the deforestation of the Amazonia Rainforest, considering the different human activities that occur in the region. This methodology was later incorporated into a system designed for the visual and interactive analysis of multivariate spatiotemporal from multiple sources, called ManyWaves. Using most of the capabilities of the Wavelet Transform, our approach does not present a high computational cost, and can deal with both spatial and temporal changes.engEspaço-temporalVisualizaçãoMultivariadoProcessamento de sinais em grafosVisualizationSpatiotemporalMultivariateGraph signal processingAprendizado do computadorVisualizaçãoAnálise multivariadaAnálise espacial (Estatística)Visualization of multivariate spatiotemporal data through graph wavelet transforminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVValdrighi, Giovani de Almeida 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19:16:49.84open.accessoai:repositorio.fgv.br:10438/35440https://repositorio.fgv.brRepositório InstitucionalPRIhttp://bibliotecadigital.fgv.br/dspace-oai/requestopendoar:39742024-06-18T19:16:49Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas 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score 15.301629

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