Analysis of hotspots in the state of Pará from 2016 to 2019
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/25793 |
Resumo: | This work aimed to analyze the incidence of hotspots located in the state of Pará, from 2016 to 2019. For analysis, the hotspots that occurred in the state of Pará were mapped, highlighting the mesoregions with the highest occurrence. Statistical variables of measures of central trends were used, as well as maps were generated through the Kernel density estimator. For data manipulation, GIS system software was used. Maps were generated with five kernel density levels represented by colors, which are dark green (very low), light green (low), yellow (medium), orange (high) and red (very high), which is applied to the three municipalities with the highest number of outbreaks. The results showed that in Pará the incidence of hotspots is concentrated, above all, along the access roads. The mesoregions that had the highest incidence of hotspots are Southeast Pará (38,989), followed by Southwest Pará (38,411), Baixo Amazonas (16,547) and Northeast Pará (16,294). The municipalities that had a high incidence of hotspots are located in the mesoregions with the highest concentration, namely São Félix do Xingu (13,077) and the municipalities of Altamira (11,710) and Novo Progresso (6,840). The hotspots tend to increase in the driest months of the year, this increase starts from the month of July, with the month of November being the most critical. |
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Analysis of hotspots in the state of Pará from 2016 to 2019Análisis de hotspots en el estado de Pará de 2016 a 2019Análise dos focos de calor no estado do Pará no período de 2016 a 2019GeoprocesamientoMétodo del Kernel.GeoprocessamentoMétodo de Kernel.GeoprocessingKernel Method.This work aimed to analyze the incidence of hotspots located in the state of Pará, from 2016 to 2019. For analysis, the hotspots that occurred in the state of Pará were mapped, highlighting the mesoregions with the highest occurrence. Statistical variables of measures of central trends were used, as well as maps were generated through the Kernel density estimator. For data manipulation, GIS system software was used. Maps were generated with five kernel density levels represented by colors, which are dark green (very low), light green (low), yellow (medium), orange (high) and red (very high), which is applied to the three municipalities with the highest number of outbreaks. The results showed that in Pará the incidence of hotspots is concentrated, above all, along the access roads. The mesoregions that had the highest incidence of hotspots are Southeast Pará (38,989), followed by Southwest Pará (38,411), Baixo Amazonas (16,547) and Northeast Pará (16,294). The municipalities that had a high incidence of hotspots are located in the mesoregions with the highest concentration, namely São Félix do Xingu (13,077) and the municipalities of Altamira (11,710) and Novo Progresso (6,840). The hotspots tend to increase in the driest months of the year, this increase starts from the month of July, with the month of November being the most critical.Este trabajo tuvo como objetivo analizar la incidencia de los hotspots ubicados en el estado de Pará, de 2016 a 2019. Para el análisis, se mapearon los hotspots que ocurrieron en el estado de Pará, destacando las mesorregiones con mayor ocurrencia. Se utilizaron variables estadísticas de medidas de tendencia central, así como se generaron mapas a través del estimador de densidad Kernel. Para la manipulación de datos se utilizó el software del sistema GIS. Se generaron mapas con cinco niveles de densidad kernel representados por colores, los cuales son verde oscuro (muy bajo), verde claro (bajo), amarillo (medio), naranja (alto) y rojo (muy alto), el cual se aplica a los tres municipios con el mayor número de brotes. Los resultados mostraron que en Pará la incidencia de hotspots se concentra, sobre todo, a lo largo de las vías de acceso. Las mesorregiones que presentaron mayor incidencia de hotspots son el Sudeste de Pará (38.989), seguido del Sudoeste de Pará (38.411), Baixo Amazonas (16.547) y Nordeste de Pará (16.294). Los municipios que presentaron alta incidencia de hotspots están ubicados en las mesorregiones con mayor concentración, a saber, São Félix do Xingu (13.077) y los municipios de Altamira (11.710) y Novo Progresso (6.840). Los hotspots tienden a aumentar en los meses más secos del año, este aumento se inicia a partir del mes de julio, siendo el mes de noviembre el más crítico.Este trabalho visou analisar a incidência de focos de calor localizados no estado do Pará, nos anos de 2016 a 2019. Para análise, mapeou-se os focos de calor que ocorreram nos estado do Pará, dando destaque para a mesorregiões de maior ocorrência. Utilizou-se variáveis estatísticas de medidas de tendências centrais, assim como, foram gerados mapas através do estimador de densidade de Kernel. Para manipulação dos dados foi utilizado software de sistema SIG. Foram gerados mapas com cinco níveis de densidade de Kernel representados por cores, sendo elas, verde escuro (muito baixa), verde claro (baixa), amarelo (média), laranja (alta) e vermelho (muito alta), sendo este aplicado para os três municípios com maior número de focos. Os resultados demonstraram que no Pará a incidência de focos de calor concentram-se, sobretudo, ao longo das vias de acesso. Já as mesorregiões que obtiveram a maior incidência de focos de calor destaca-se o Sudeste Paraense (38.989), seguido pelo Sudoeste Paraense (38.411), o Baixo Amazonas (16.547) e o Nordeste Paraense (16.294). Os municípios que apresentaram alta incidência de focos de calor ficam localizados nas mesorregiões de maior concentração, sendo eles, São Félix do Xingu (13.077) e os municípios de Altamira (11.710) e Novo Progresso (6.840). Os focos de calor tendem a aumentar nos meses mais secos do ano, este aumento começa a partir do mês de julho, sendo o mês de novembro o mais crítico.Research, Society and Development2022-04-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2579310.33448/rsd-v11i6.25793Research, Society and Development; Vol. 11 No. 6; e31611625793Research, Society and Development; Vol. 11 Núm. 6; e31611625793Research, Society and Development; v. 11 n. 6; e316116257932525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/25793/25251Copyright (c) 2022 Renan Ribeiro Costa ; Bianca Lobato Oliveira; Paula Fernanda Pinheiro Ribeiro Paiva; Eduardo Saraiva da Rocha; Orleno Marques da Silva Junior; Francimary da Silva Carneiro; Klewton Adriano Oliveira Pinheiro; Marcio Braga Amorimhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCosta , Renan Ribeiro Oliveira, Bianca Lobato Paiva, Paula Fernanda Pinheiro Ribeiro Rocha, Eduardo Saraiva da Silva Junior, Orleno Marques da Carneiro, Francimary da Silva Pinheiro, Klewton Adriano Oliveira Amorim, Marcio Braga 2022-05-13T18:04:10Zoai:ojs.pkp.sfu.ca:article/25793Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:43:59.549746Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Analysis of hotspots in the state of Pará from 2016 to 2019 Análisis de hotspots en el estado de Pará de 2016 a 2019 Análise dos focos de calor no estado do Pará no período de 2016 a 2019 |
title |
Analysis of hotspots in the state of Pará from 2016 to 2019 |
spellingShingle |
Analysis of hotspots in the state of Pará from 2016 to 2019 Costa , Renan Ribeiro Geoprocesamiento Método del Kernel. Geoprocessamento Método de Kernel. Geoprocessing Kernel Method. |
title_short |
Analysis of hotspots in the state of Pará from 2016 to 2019 |
title_full |
Analysis of hotspots in the state of Pará from 2016 to 2019 |
title_fullStr |
Analysis of hotspots in the state of Pará from 2016 to 2019 |
title_full_unstemmed |
Analysis of hotspots in the state of Pará from 2016 to 2019 |
title_sort |
Analysis of hotspots in the state of Pará from 2016 to 2019 |
author |
Costa , Renan Ribeiro |
author_facet |
Costa , Renan Ribeiro Oliveira, Bianca Lobato Paiva, Paula Fernanda Pinheiro Ribeiro Rocha, Eduardo Saraiva da Silva Junior, Orleno Marques da Carneiro, Francimary da Silva Pinheiro, Klewton Adriano Oliveira Amorim, Marcio Braga |
author_role |
author |
author2 |
Oliveira, Bianca Lobato Paiva, Paula Fernanda Pinheiro Ribeiro Rocha, Eduardo Saraiva da Silva Junior, Orleno Marques da Carneiro, Francimary da Silva Pinheiro, Klewton Adriano Oliveira Amorim, Marcio Braga |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Costa , Renan Ribeiro Oliveira, Bianca Lobato Paiva, Paula Fernanda Pinheiro Ribeiro Rocha, Eduardo Saraiva da Silva Junior, Orleno Marques da Carneiro, Francimary da Silva Pinheiro, Klewton Adriano Oliveira Amorim, Marcio Braga |
dc.subject.por.fl_str_mv |
Geoprocesamiento Método del Kernel. Geoprocessamento Método de Kernel. Geoprocessing Kernel Method. |
topic |
Geoprocesamiento Método del Kernel. Geoprocessamento Método de Kernel. Geoprocessing Kernel Method. |
description |
This work aimed to analyze the incidence of hotspots located in the state of Pará, from 2016 to 2019. For analysis, the hotspots that occurred in the state of Pará were mapped, highlighting the mesoregions with the highest occurrence. Statistical variables of measures of central trends were used, as well as maps were generated through the Kernel density estimator. For data manipulation, GIS system software was used. Maps were generated with five kernel density levels represented by colors, which are dark green (very low), light green (low), yellow (medium), orange (high) and red (very high), which is applied to the three municipalities with the highest number of outbreaks. The results showed that in Pará the incidence of hotspots is concentrated, above all, along the access roads. The mesoregions that had the highest incidence of hotspots are Southeast Pará (38,989), followed by Southwest Pará (38,411), Baixo Amazonas (16,547) and Northeast Pará (16,294). The municipalities that had a high incidence of hotspots are located in the mesoregions with the highest concentration, namely São Félix do Xingu (13,077) and the municipalities of Altamira (11,710) and Novo Progresso (6,840). The hotspots tend to increase in the driest months of the year, this increase starts from the month of July, with the month of November being the most critical. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-29 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/25793 10.33448/rsd-v11i6.25793 |
url |
https://rsdjournal.org/index.php/rsd/article/view/25793 |
identifier_str_mv |
10.33448/rsd-v11i6.25793 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/25793/25251 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 11 No. 6; e31611625793 Research, Society and Development; Vol. 11 Núm. 6; e31611625793 Research, Society and Development; v. 11 n. 6; e31611625793 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052703324504064 |