ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Caminhos de Geografia |
Texto Completo: | https://seer.ufu.br/index.php/caminhosdegeografia/article/view/62229 |
Resumo: | By modeling COVID-19 data, and drawing up thematic maps, it was possible to analyze spatiotemporal behavior patterns of the new coronavirus in Pernambuco. It is possible to identify: 01- The highest numbers of cases and deaths are related to the cities with the highest GDP per capita; 02- The spread of the disease follows the directions of the main network of federal highways; 03- The thematic maps of municipalities added to the numbers of cases and deaths have repercussions on the articulated and intense propagation of patterns in blocks of municipalities connected by highways in the timeline, as the cases of Serra Talhada, São José de Belmonte, Mirandiba, Salgueiro and Parnamirim; 04- Other municipalities such as Santa Maria da Boa Vista and Orocó are isolated with fewer cases, as well as Belém do São Francisco and Carnaubeira da Penha, small isolated blocks with fewer cases and deaths, similarly to Ibimirim and Custódia; Águas Belas and Serra Talhada are outside the propagation block, the “island-municipalities”, thus revealing a behavior of propagation of the disease, of isolated or articulated municipalities around and along federal road axes. BR-104 and 232 assume regional influence and state and local roads may have contributed to greater "capillarity" of the spread of COVID-19. |
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ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZILANÁLISE DAS RELAÇÕES ESPACIAIS DOS CASOS CONFIRMADOS E ÓBITOS DA COVID-19 NO PERÍODO DE MARÇO A AGOSTO DE 2020 NO ESTADO DE PERNAMBUCO, BRASILCOVID-19PandemiaCartografia TemáticaCOVID-19PandemicThematic CartographyBy modeling COVID-19 data, and drawing up thematic maps, it was possible to analyze spatiotemporal behavior patterns of the new coronavirus in Pernambuco. It is possible to identify: 01- The highest numbers of cases and deaths are related to the cities with the highest GDP per capita; 02- The spread of the disease follows the directions of the main network of federal highways; 03- The thematic maps of municipalities added to the numbers of cases and deaths have repercussions on the articulated and intense propagation of patterns in blocks of municipalities connected by highways in the timeline, as the cases of Serra Talhada, São José de Belmonte, Mirandiba, Salgueiro and Parnamirim; 04- Other municipalities such as Santa Maria da Boa Vista and Orocó are isolated with fewer cases, as well as Belém do São Francisco and Carnaubeira da Penha, small isolated blocks with fewer cases and deaths, similarly to Ibimirim and Custódia; Águas Belas and Serra Talhada are outside the propagation block, the “island-municipalities”, thus revealing a behavior of propagation of the disease, of isolated or articulated municipalities around and along federal road axes. BR-104 and 232 assume regional influence and state and local roads may have contributed to greater "capillarity" of the spread of COVID-19.Modelando os dados da COVID-19 e elaborando mapas temáticos, foi possível analisar padrões de comportamento espaço-temporal do novo coronavírus em Pernambuco. Sendo possível identificar: 01- Os maiores números de casos e óbitos estão relacionados aos municípios de maior PIB per capita; 02- A dispersão da doença acompanha as direções da rede principal de rodovias federais; 03- Os mapas temáticos dos municípios agregados aos números de casos e óbitos repercutem na propagação articulada e intensa de padrões em blocos de municípios conectados por rodovias na linha do tempo, vide Serra Talhada, São José de Belmonte, Mirandiba, Salgueiro e Parnamirim; 04- Outros municípios como Santa Maria da Boa Vista e Orocó se apresentam isolados com menos casos, assim como Belém do São Francisco e Carnaubeira da Penha, pequenos blocos isolados com menos casos e óbitos, analogamente a Ibimirim e Custódia; já Águas Belas e Serra Talhada apresentam-se fora da propagação em bloco, os “municípios-ilhas”. Revelando, assim, um comportamento de propagação da doença, de municípios isolados ou articulados em torno e ao longo de eixos viários federais. As BR-104 e 232 assumem influência regional e as estradas estaduais e vicinais podem ter contribuído para maior "capilaridade" da disseminação da COVID-19.EDUFU - Editora da Universidade Federal de Uberlândia2023-02-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado pelos paresapplication/pdfhttps://seer.ufu.br/index.php/caminhosdegeografia/article/view/6222910.14393/RCG249162229Caminhos de Geografia; Vol. 24 No. 91 (2023): Fevereiro; 208-223Caminhos de Geografia; Vol. 24 Núm. 91 (2023): Fevereiro; 208-223Caminhos de Geografia; v. 24 n. 91 (2023): Fevereiro; 208-2231678-6343reponame:Caminhos de Geografiainstname:Universidade Federal de Uberlândia (UFU)instacron:UFUporhttps://seer.ufu.br/index.php/caminhosdegeografia/article/view/62229/35618Copyright (c) 2023 Bruna Araujo Candeia, Ana Lúcia Bezerra Candeias, João Rodrigues Tavares Juniorhttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessCandeia, Bruna AraujoCandeias, Ana Lúcia BezerraTavares Junior, João Rodrigues2023-02-22T20:06:05Zoai:ojs.www.seer.ufu.br:article/62229Revistahttps://seer.ufu.br/index.php/caminhosdegeografia/indexPUBhttp://www.seer.ufu.br/index.php/caminhosdegeografia/oaiflaviasantosgeo@gmail.com1678-63431678-6343opendoar:2023-02-22T20:06:05Caminhos de Geografia - Universidade Federal de Uberlândia (UFU)false |
dc.title.none.fl_str_mv |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL ANÁLISE DAS RELAÇÕES ESPACIAIS DOS CASOS CONFIRMADOS E ÓBITOS DA COVID-19 NO PERÍODO DE MARÇO A AGOSTO DE 2020 NO ESTADO DE PERNAMBUCO, BRASIL |
title |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL |
spellingShingle |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL Candeia, Bruna Araujo COVID-19 Pandemia Cartografia Temática COVID-19 Pandemic Thematic Cartography |
title_short |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL |
title_full |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL |
title_fullStr |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL |
title_full_unstemmed |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL |
title_sort |
ANALYSIS OF SPATIAL RELATIONSHIPS OF CONFIRMED COVID-19 CASES AND DEATHS FROM MARCH TO AUGUST 2020 IN THE STATE OF PERNAMBUCO, BRAZIL |
author |
Candeia, Bruna Araujo |
author_facet |
Candeia, Bruna Araujo Candeias, Ana Lúcia Bezerra Tavares Junior, João Rodrigues |
author_role |
author |
author2 |
Candeias, Ana Lúcia Bezerra Tavares Junior, João Rodrigues |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Candeia, Bruna Araujo Candeias, Ana Lúcia Bezerra Tavares Junior, João Rodrigues |
dc.subject.por.fl_str_mv |
COVID-19 Pandemia Cartografia Temática COVID-19 Pandemic Thematic Cartography |
topic |
COVID-19 Pandemia Cartografia Temática COVID-19 Pandemic Thematic Cartography |
description |
By modeling COVID-19 data, and drawing up thematic maps, it was possible to analyze spatiotemporal behavior patterns of the new coronavirus in Pernambuco. It is possible to identify: 01- The highest numbers of cases and deaths are related to the cities with the highest GDP per capita; 02- The spread of the disease follows the directions of the main network of federal highways; 03- The thematic maps of municipalities added to the numbers of cases and deaths have repercussions on the articulated and intense propagation of patterns in blocks of municipalities connected by highways in the timeline, as the cases of Serra Talhada, São José de Belmonte, Mirandiba, Salgueiro and Parnamirim; 04- Other municipalities such as Santa Maria da Boa Vista and Orocó are isolated with fewer cases, as well as Belém do São Francisco and Carnaubeira da Penha, small isolated blocks with fewer cases and deaths, similarly to Ibimirim and Custódia; Águas Belas and Serra Talhada are outside the propagation block, the “island-municipalities”, thus revealing a behavior of propagation of the disease, of isolated or articulated municipalities around and along federal road axes. BR-104 and 232 assume regional influence and state and local roads may have contributed to greater "capillarity" of the spread of COVID-19. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-22 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Avaliado pelos pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://seer.ufu.br/index.php/caminhosdegeografia/article/view/62229 10.14393/RCG249162229 |
url |
https://seer.ufu.br/index.php/caminhosdegeografia/article/view/62229 |
identifier_str_mv |
10.14393/RCG249162229 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://seer.ufu.br/index.php/caminhosdegeografia/article/view/62229/35618 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Bruna Araujo Candeia, Ana Lúcia Bezerra Candeias, João Rodrigues Tavares Junior http://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Bruna Araujo Candeia, Ana Lúcia Bezerra Candeias, João Rodrigues Tavares Junior http://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
EDUFU - Editora da Universidade Federal de Uberlândia |
publisher.none.fl_str_mv |
EDUFU - Editora da Universidade Federal de Uberlândia |
dc.source.none.fl_str_mv |
Caminhos de Geografia; Vol. 24 No. 91 (2023): Fevereiro; 208-223 Caminhos de Geografia; Vol. 24 Núm. 91 (2023): Fevereiro; 208-223 Caminhos de Geografia; v. 24 n. 91 (2023): Fevereiro; 208-223 1678-6343 reponame:Caminhos de Geografia instname:Universidade Federal de Uberlândia (UFU) instacron:UFU |
instname_str |
Universidade Federal de Uberlândia (UFU) |
instacron_str |
UFU |
institution |
UFU |
reponame_str |
Caminhos de Geografia |
collection |
Caminhos de Geografia |
repository.name.fl_str_mv |
Caminhos de Geografia - Universidade Federal de Uberlândia (UFU) |
repository.mail.fl_str_mv |
flaviasantosgeo@gmail.com |
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1797067010385903616 |