POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES

Detalhes bibliográficos
Autor(a) principal: Souza, Thaís Góes de
Data de Publicação: 2023
Outros Autores: Fernandes, Vivian de Oliveira, Pedrassoli, Julio César, Fonseca, Fernanda Doracy Rocha
Tipo de documento: Artigo
Idioma: por
Título da fonte: Caminhos de Geografia
Texto Completo: https://seer.ufu.br/index.php/caminhosdegeografia/article/view/68395
Resumo: While the availability of online data has increased, the processing and evaluation of this data give rise to discussions regarding the scientific instruments that enable the measurement of critical data characteristics while minimizing inherent inconsistencies related to the subject. This article analyzed the representativeness of data obtained through web scrapping from advertisements on two nationally recognized websites. It applied a data refinement technique to property sales data and examined its proportion concerning the municipal residential real estate registry. The method proposed the utilization of web-available data retrieved through the technique of online data scraping. The approach focused on average prices during reference periods, as well as on the assessment of the potentialities and limitations of big data, along with the spatial concentration mapping of real estate market prices categorized by type and spatialized by neighborhood. The research concluded that the Olx website dataset exhibited lower completeness and volume than Imovelweb (Iw) yet demonstrated greater diversity regarding spatial coverage of properties, including mapping the distribution of average per-square-meter prices by neighborhood.
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spelling POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICESPOTENCIALIDADES E LIMITAÇÕES DOS DADOS DE WEB SCRAPING PARA O MAPEAMENTO DOS PREÇOS DOS IMÓVEIS URBANOSMercado imobiliárioPreço da terraBig dataMapa dos preçosReal estate marketLand priceBig dataPrice mapWhile the availability of online data has increased, the processing and evaluation of this data give rise to discussions regarding the scientific instruments that enable the measurement of critical data characteristics while minimizing inherent inconsistencies related to the subject. This article analyzed the representativeness of data obtained through web scrapping from advertisements on two nationally recognized websites. It applied a data refinement technique to property sales data and examined its proportion concerning the municipal residential real estate registry. The method proposed the utilization of web-available data retrieved through the technique of online data scraping. The approach focused on average prices during reference periods, as well as on the assessment of the potentialities and limitations of big data, along with the spatial concentration mapping of real estate market prices categorized by type and spatialized by neighborhood. The research concluded that the Olx website dataset exhibited lower completeness and volume than Imovelweb (Iw) yet demonstrated greater diversity regarding spatial coverage of properties, including mapping the distribution of average per-square-meter prices by neighborhood.Embora a disponibilidade de dados online tenha aumentado, o tratamento e a avaliação destes dados incorrem em discussões acerca dos instrumentos científicos que permitem mensurar as principais características dos dados, minimizando inconsistências próprias relacionadas ao objeto. O propósito deste artigo foi analisar a representatividade dos dados obtidos por web scraping dos anúncios presentes em dois sites de extensão nacional ao aplicar uma forma de depuração sobre os dados de venda dos imóveis, bem como verificar sua proporção em relação ao cadastro imobiliário residencial municipal. O método propôs a utilização de dados disponíveis na web, recuperados através da técnica de raspagem de dados online (web scraping). Na abordagem, prestou-se atenção quanto aos preços das médias nos períodos de referência, como do levantamento das potencialidades e limitações dos dados de big data, assim como o mapeamento da concentração espacial dos preços do mercado imobiliário classificados por tipo e espacializados por bairro. A pesquisa concluiu que a base do site Olx apresentou menor completude e menor volume se comparado ao Imovelweb (Iw), porém maior variedade referente à cobertura espacial dos imóveis como o mapeamento da distribuição das médias dos preços do m² por bairro.EDUFU - Editora da Universidade Federal de Uberlândia2023-12-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado pelos paresapplication/pdfhttps://seer.ufu.br/index.php/caminhosdegeografia/article/view/6839510.14393/RCG249668395Caminhos de Geografia; Vol. 24 No. 96 (2023): Dezembro; 73–87Caminhos de Geografia; Vol. 24 Núm. 96 (2023): Dezembro; 73–87Caminhos de Geografia; v. 24 n. 96 (2023): Dezembro; 73–871678-6343reponame:Caminhos de Geografiainstname:Universidade Federal de Uberlândia (UFU)instacron:UFUporhttps://seer.ufu.br/index.php/caminhosdegeografia/article/view/68395/37243Copyright (c) 2023 Thaís Góes de Souza, Vivian de Oliveira Fernandes, Julio César Pedrassoli, Fernanda Doracy Rocha Fonsecahttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessSouza, Thaís Góes deFernandes, Vivian de OliveiraPedrassoli, Julio CésarFonseca, Fernanda Doracy Rocha2023-12-05T15:11:57Zoai:ojs.www.seer.ufu.br:article/68395Revistahttps://seer.ufu.br/index.php/caminhosdegeografia/indexPUBhttp://www.seer.ufu.br/index.php/caminhosdegeografia/oaiflaviasantosgeo@gmail.com1678-63431678-6343opendoar:2023-12-05T15:11:57Caminhos de Geografia - Universidade Federal de Uberlândia (UFU)false
dc.title.none.fl_str_mv POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
POTENCIALIDADES E LIMITAÇÕES DOS DADOS DE WEB SCRAPING PARA O MAPEAMENTO DOS PREÇOS DOS IMÓVEIS URBANOS
title POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
spellingShingle POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
Souza, Thaís Góes de
Mercado imobiliário
Preço da terra
Big data
Mapa dos preços
Real estate market
Land price
Big data
Price map
title_short POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
title_full POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
title_fullStr POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
title_full_unstemmed POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
title_sort POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
author Souza, Thaís Góes de
author_facet Souza, Thaís Góes de
Fernandes, Vivian de Oliveira
Pedrassoli, Julio César
Fonseca, Fernanda Doracy Rocha
author_role author
author2 Fernandes, Vivian de Oliveira
Pedrassoli, Julio César
Fonseca, Fernanda Doracy Rocha
author2_role author
author
author
dc.contributor.author.fl_str_mv Souza, Thaís Góes de
Fernandes, Vivian de Oliveira
Pedrassoli, Julio César
Fonseca, Fernanda Doracy Rocha
dc.subject.por.fl_str_mv Mercado imobiliário
Preço da terra
Big data
Mapa dos preços
Real estate market
Land price
Big data
Price map
topic Mercado imobiliário
Preço da terra
Big data
Mapa dos preços
Real estate market
Land price
Big data
Price map
description While the availability of online data has increased, the processing and evaluation of this data give rise to discussions regarding the scientific instruments that enable the measurement of critical data characteristics while minimizing inherent inconsistencies related to the subject. This article analyzed the representativeness of data obtained through web scrapping from advertisements on two nationally recognized websites. It applied a data refinement technique to property sales data and examined its proportion concerning the municipal residential real estate registry. The method proposed the utilization of web-available data retrieved through the technique of online data scraping. The approach focused on average prices during reference periods, as well as on the assessment of the potentialities and limitations of big data, along with the spatial concentration mapping of real estate market prices categorized by type and spatialized by neighborhood. The research concluded that the Olx website dataset exhibited lower completeness and volume than Imovelweb (Iw) yet demonstrated greater diversity regarding spatial coverage of properties, including mapping the distribution of average per-square-meter prices by neighborhood.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-05
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/68395
10.14393/RCG249668395
url https://seer.ufu.br/index.php/caminhosdegeografia/article/view/68395
identifier_str_mv 10.14393/RCG249668395
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://seer.ufu.br/index.php/caminhosdegeografia/article/view/68395/37243
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv 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. 96 (2023): Dezembro; 73–87
Caminhos de Geografia; Vol. 24 Núm. 96 (2023): Dezembro; 73–87
Caminhos de Geografia; v. 24 n. 96 (2023): Dezembro; 73–87
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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