Prediction of Spatial and Temporal Data: A Web Tool based on Georeferenced Resources
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , |
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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Repositório Institucional da UNESP |
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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:29462024-05-23T11:08:45.185649Repositório 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_ |
1803045779575668736 |