Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources

Detalhes bibliográficos
Autor(a) principal: Valencio, Carlos Roberto [UNESP]
Data de Publicação: 2014
Outros Autores: El Hetti Laurenti, Carlos Henrique [UNESP], Baida, Luiz Carlos [UNESP], Ferrari, Fernando [UNESP], Kawabata, Thatiane [UNESP], Colombini, Angelo Cesar, IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PDCAT.2014.29
http://hdl.handle.net/11449/186402
Resumo: Spatiotemporal data stored in geographic databases provide an evolutionary panorama about the characteristics of a specific region. With integration of prediction concepts and statistical functions to that data, it is possible to make inferences of obtained information, to support in many areas such as management of occupational health, environmental resources, quality of life and, others. In this article is proposed a strategy to calculate the locality of predicted spatial points with the temporal and statistic function series, which will be able to find regions with critical levels. In the concentrations of more dense occurrences, this strategy supports to choose prevention methods and offers a prediction analysis based on georeferenced resources. This work contributes towards to prediction, analysis and visualization of georeferenced data to reduce costs and improve the life quality.
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spelling Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resourcesprediction of spatial dataanalysis of geographic dataWeb-based Geographic Information System (WebGIS)occupational healthSpatiotemporal data stored in geographic databases provide an evolutionary panorama about the characteristics of a specific region. With integration of prediction concepts and statistical functions to that data, it is possible to make inferences of obtained information, to support in many areas such as management of occupational health, environmental resources, quality of life and, others. In this article is proposed a strategy to calculate the locality of predicted spatial points with the temporal and statistic function series, which will be able to find regions with critical levels. In the concentrations of more dense occurrences, this strategy supports to choose prevention methods and offers a prediction analysis based on georeferenced resources. This work contributes towards to prediction, analysis and visualization of georeferenced data to reduce costs and improve the life quality.Sao Paulo State Univ, Dept Ciencia Comp & Estat, Sao Paulo, BrazilUniv Fed Sao Carlos, Dept Ciencia Comp, Sao Paulo, BrazilSao Paulo State Univ, Dept Ciencia Comp & Estat, Sao Paulo, BrazilIeeeUniversidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Valencio, Carlos Roberto [UNESP]El Hetti Laurenti, Carlos Henrique [UNESP]Baida, Luiz Carlos [UNESP]Ferrari, Fernando [UNESP]Kawabata, Thatiane [UNESP]Colombini, Angelo CesarIEEE2019-10-04T20:36:37Z2019-10-04T20:36:37Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject124-130http://dx.doi.org/10.1109/PDCAT.2014.292014 15th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat 2014). New York: Ieee, p. 124-130, 2014.http://hdl.handle.net/11449/18640210.1109/PDCAT.2014.29WOS:000374910000019Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 15th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat 2014)info:eu-repo/semantics/openAccess2021-10-23T16:15:39Zoai:repositorio.unesp.br:11449/186402Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T16:15:39Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
title Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
spellingShingle Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
Valencio, Carlos Roberto [UNESP]
prediction of spatial data
analysis of geographic data
Web-based Geographic Information System (WebGIS)
occupational health
title_short Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
title_full Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
title_fullStr Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
title_full_unstemmed Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
title_sort Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
author Valencio, Carlos Roberto [UNESP]
author_facet Valencio, Carlos Roberto [UNESP]
El Hetti Laurenti, Carlos Henrique [UNESP]
Baida, Luiz Carlos [UNESP]
Ferrari, Fernando [UNESP]
Kawabata, Thatiane [UNESP]
Colombini, Angelo Cesar
IEEE
author_role author
author2 El Hetti Laurenti, Carlos Henrique [UNESP]
Baida, Luiz Carlos [UNESP]
Ferrari, Fernando [UNESP]
Kawabata, Thatiane [UNESP]
Colombini, Angelo Cesar
IEEE
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Federal de São Carlos (UFSCar)
dc.contributor.author.fl_str_mv Valencio, Carlos Roberto [UNESP]
El Hetti Laurenti, Carlos Henrique [UNESP]
Baida, Luiz Carlos [UNESP]
Ferrari, Fernando [UNESP]
Kawabata, Thatiane [UNESP]
Colombini, Angelo Cesar
IEEE
dc.subject.por.fl_str_mv prediction of spatial data
analysis of geographic data
Web-based Geographic Information System (WebGIS)
occupational health
topic prediction of spatial data
analysis of geographic data
Web-based Geographic Information System (WebGIS)
occupational health
description Spatiotemporal data stored in geographic databases provide an evolutionary panorama about the characteristics of a specific region. With integration of prediction concepts and statistical functions to that data, it is possible to make inferences of obtained information, to support in many areas such as management of occupational health, environmental resources, quality of life and, others. In this article is proposed a strategy to calculate the locality of predicted spatial points with the temporal and statistic function series, which will be able to find regions with critical levels. In the concentrations of more dense occurrences, this strategy supports to choose prevention methods and offers a prediction analysis based on georeferenced resources. This work contributes towards to prediction, analysis and visualization of georeferenced data to reduce costs and improve the life quality.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-04T20:36:37Z
2019-10-04T20:36:37Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/PDCAT.2014.29
2014 15th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat 2014). New York: Ieee, p. 124-130, 2014.
http://hdl.handle.net/11449/186402
10.1109/PDCAT.2014.29
WOS:000374910000019
url http://dx.doi.org/10.1109/PDCAT.2014.29
http://hdl.handle.net/11449/186402
identifier_str_mv 2014 15th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat 2014). New York: Ieee, p. 124-130, 2014.
10.1109/PDCAT.2014.29
WOS:000374910000019
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2014 15th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat 2014)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 124-130
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1799964392019722240

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