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Klustring av Sveriges kommuner: En studie av sambandet mellan kommuners egenskaper och valresultatet
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Clustering of the municipalities of Sweden : A study of the relationship between municipality characteristics and the general election results (English)
Abstract [sv]

Hösten 2018 hölls politiska val i Sverige och i samband med detta är det intressant att undersöka vad som kan påverka hur personer röstar. Syftet med denna rapport är att undersöka om det finns överensstämmelser mellan kommuners egenskaper och hur personerna i kommunerna röstade i riksdagsvalet. Klustring på datamängder med kommuners egenskaper och kommuners valstatistik för riksdagsvalet 2018 utgör underlag till studien. K-meansklustring och hierarkisk klustring är de klustringsmetoder som används. I rapporten presenteras resultat av klustringen och konstruktionen av en analysmetod som jämför klustringar. Resultaten visar på att det finns viss överensstämmelse men att klustring inte är den optimala metoden för att analysera denna datamängd.

 

Abstract [en]

In the autumn 2018 political elections were held in Sweden and consequently it is interesting to investigate what can affect how people vote. The purpose with this report is investigating if there are correspondences between the characteristics of a municipality and how the people in that municipality voted in the general election. Clustering on data sets with municipality characteristics and municipality general election statistics from 2018 is the basis of this study. K-means clustering and hierarchical clustering are the clustering methods that are used. In the report results of the clustering and the construction of a method for comparing clusterings are presented. The results show that there are some correspondences but that clustering is not the optimal method for analysing this data set.

 

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:194
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-255687OAI: oai:DiVA.org:kth-255687DiVA, id: diva2:1341272
Supervisors
Examiners
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved

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