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Clustering of Small Territories Based on Axes of Inequality

Background: In the present paper, we conduct a study before creating an e-cohort for the design of the sample. This e-cohort had to enable the effective representation of the province of Girona to facilitate its study according to the axes of inequality. Methods: The territory under study is divided...

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Autores principales: Perafita, Xavier, Saez, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955561/
https://www.ncbi.nlm.nih.gov/pubmed/35329047
http://dx.doi.org/10.3390/ijerph19063359
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author Perafita, Xavier
Saez, Marc
author_facet Perafita, Xavier
Saez, Marc
author_sort Perafita, Xavier
collection PubMed
description Background: In the present paper, we conduct a study before creating an e-cohort for the design of the sample. This e-cohort had to enable the effective representation of the province of Girona to facilitate its study according to the axes of inequality. Methods: The territory under study is divided by municipalities, considering these different axes. The study consists of a comparison of 14 clustering algorithms, together with 3 data sets of municipal information to detect the grouping that was the most consistent. Prior to carrying out the clustering, a variable selection process was performed to discard those that were not useful. The comparison was carried out following two axes: results and graphical representation. Results: The intra-cluster results were also analyzed to observe the coherence of the grouping. Finally, we study the probability of belonging to a cluster, such as the one containing the county capital. Conclusions: This clustering can be the basis for working with a sample that is significant and representative of the territory.
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spelling pubmed-89555612022-03-26 Clustering of Small Territories Based on Axes of Inequality Perafita, Xavier Saez, Marc Int J Environ Res Public Health Article Background: In the present paper, we conduct a study before creating an e-cohort for the design of the sample. This e-cohort had to enable the effective representation of the province of Girona to facilitate its study according to the axes of inequality. Methods: The territory under study is divided by municipalities, considering these different axes. The study consists of a comparison of 14 clustering algorithms, together with 3 data sets of municipal information to detect the grouping that was the most consistent. Prior to carrying out the clustering, a variable selection process was performed to discard those that were not useful. The comparison was carried out following two axes: results and graphical representation. Results: The intra-cluster results were also analyzed to observe the coherence of the grouping. Finally, we study the probability of belonging to a cluster, such as the one containing the county capital. Conclusions: This clustering can be the basis for working with a sample that is significant and representative of the territory. MDPI 2022-03-12 /pmc/articles/PMC8955561/ /pubmed/35329047 http://dx.doi.org/10.3390/ijerph19063359 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Perafita, Xavier
Saez, Marc
Clustering of Small Territories Based on Axes of Inequality
title Clustering of Small Territories Based on Axes of Inequality
title_full Clustering of Small Territories Based on Axes of Inequality
title_fullStr Clustering of Small Territories Based on Axes of Inequality
title_full_unstemmed Clustering of Small Territories Based on Axes of Inequality
title_short Clustering of Small Territories Based on Axes of Inequality
title_sort clustering of small territories based on axes of inequality
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955561/
https://www.ncbi.nlm.nih.gov/pubmed/35329047
http://dx.doi.org/10.3390/ijerph19063359
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