Modelling abstention rate using spatial regression

Detalhes bibliográficos
Autor(a) principal: Mota, Afonso Manita Santos
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/64940
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
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spelling Modelling abstention rate using spatial regressionVoter turnoutAbstention rateSociological VariablesEconomic VariablesSpatial AnalysisGeographically Weighted RegressionSpatial Non-StationarityDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementDuring the last few elections that were held in Portugal, there have been very low percentages of voter turnout. This will obviously impact the result of those elections and can maybe be related to the general disenchantment of the population regarding the country’s recent political environment. This study aims to contribute to a better understanding of the patterns in the abstention rate of the last elections in Portugal. Sociological and economic variables such as age, unemployment rate, education level and many others will be used in trying to find out if they influence the abstention rate. It is logical to assume that the abstention rate in a certain municipality will be related to the abstention in neighboring municipalities. Therefore, the study also investigates if there is spatial autocorrelation in the abstention rates. Modeling a phenomenon like this with a simple linear regression model, estimated by Ordinary Least Squares (OLS), will render less efficient and biased results because of the spatial correlation of the observations and possible spatial clustering of values. Spatial regression methods have been proposed to overcome these drawbacks, particularly the Geographically Weighted Regression (GWR). This method will take into account possible local influences, allowing the coefficients of the model to vary depending on the geographic location, possibly obtaining a more appropriate fit. Many different OLS and GWR models were investigated by considering different combinations of explanatory variables and diagnosing their results through statistical tests and goodness-of-fit measures. Results show that indeed the data exhibits a non-random spatial pattern, and that a GWR model is a better approach in modeling abstention rates, when compared to an OLS model. Hence, the percentage of voter turnout in a municipality is likely to be better modelled taking into account its geographic location.Costa, Ana Cristina Marinho daRUNMota, Afonso Manita Santos2019-03-29T17:36:56Z2019-03-152019-03-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/64940TID:202208214enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:30:52Zoai:run.unl.pt:10362/64940Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:34:13.917802Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Modelling abstention rate using spatial regression
title Modelling abstention rate using spatial regression
spellingShingle Modelling abstention rate using spatial regression
Mota, Afonso Manita Santos
Voter turnout
Abstention rate
Sociological Variables
Economic Variables
Spatial Analysis
Geographically Weighted Regression
Spatial Non-Stationarity
title_short Modelling abstention rate using spatial regression
title_full Modelling abstention rate using spatial regression
title_fullStr Modelling abstention rate using spatial regression
title_full_unstemmed Modelling abstention rate using spatial regression
title_sort Modelling abstention rate using spatial regression
author Mota, Afonso Manita Santos
author_facet Mota, Afonso Manita Santos
author_role author
dc.contributor.none.fl_str_mv Costa, Ana Cristina Marinho da
RUN
dc.contributor.author.fl_str_mv Mota, Afonso Manita Santos
dc.subject.por.fl_str_mv Voter turnout
Abstention rate
Sociological Variables
Economic Variables
Spatial Analysis
Geographically Weighted Regression
Spatial Non-Stationarity
topic Voter turnout
Abstention rate
Sociological Variables
Economic Variables
Spatial Analysis
Geographically Weighted Regression
Spatial Non-Stationarity
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
publishDate 2019
dc.date.none.fl_str_mv 2019-03-29T17:36:56Z
2019-03-15
2019-03-15T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/64940
TID:202208214
url http://hdl.handle.net/10362/64940
identifier_str_mv TID:202208214
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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