 On the Potential for Using Selected PCA-Based Methods to Analyze the Crime Rate in PolandMisztal Małgorzata ()
Econometrics. Advances in Applied Data Analysis, 2019, vol. 23, issue 2, 15-32 Abstract: The aim of the paper is to assess the potential for using some selected PCA-based methods to analyze the spatial diversity of crime in Poland during 2000-2017. Classical principal components analysis (PCA) deals with two-way matrices, usually taking into account objects and variables. In the case of data analyzed in the study, apart from two dimensions (objects – voivodships, variables– criminal offences), there is also the dimension of time, so the dataset can be seen as data cube: objects × variables × time. Therefore, this type of data requires the use of methods handling three-way data structures. In the paper the variability of some selected categories of criminal offences in time (2000–2017) and space (according to voivodships) is analyzed using the between-class and the within-class principal component analysis. The advantage of these methods is, among others, the possibility of the graphical presentation of the results in two-dimensional space with the use of factorial maps. Keywords: crime; criminal offence; multivariate exploratory data analysis; principal component analysis; factorial maps (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:23:y:2019:i:2:p:15-32:n:2 Access Statistics for this article Econometrics. Advances in Applied Data Analysis is currently edited by Józef Dziechciarz More articles in Econometrics. Advances in Applied Data Analysis from Sciendo |
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