Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis
Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This c...
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| Fformat: | Online |
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Firenze University Press
2022
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| Mynediad Ar-lein: | ONIX_20220601_9788855184618_544 |
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Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| _version_ | 1869518924375130112 |
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| author | CUSATELLI, Carlo GIACALONE, Massimiliano nissi, eugenia |
| author_browse | CUSATELLI, Carlo GIACALONE, Massimiliano nissi, eugenia |
| author_facet | CUSATELLI, Carlo GIACALONE, Massimiliano nissi, eugenia |
| author_sort | CUSATELLI, Carlo |
| collection | Directory of Open Access Books |
| description | Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces. |
| format | Online |
| id | doab-20.500.12854ir-83565 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-835652022-06-02T04:33:47Z Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis CUSATELLI, Carlo GIACALONE, Massimiliano nissi, eugenia Well being Spatial Principal Component Analysis (sPCA) Composite Indicators Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces. 2022-06-02T04:33:46Z 2022-06-02T04:33:46Z 2022-06-01T12:20:34Z 2021 chapter ONIX_20220601_9788855184618_544 2704-5846 https://library.oapen.org/handle/20.500.12657/56359 9788855184618 https://directory.doabooks.org/handle/20.500.12854/83565 eng Proceedings e report open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/56359/1/26245.pdf Firenze University Press 10.36253/978-88-5518-461-8.27 10.36253/978-88-5518-461-8.27 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855184618 6 Florence open access |
| spellingShingle | Well being Spatial Principal Component Analysis (sPCA) Composite Indicators CUSATELLI, Carlo GIACALONE, Massimiliano nissi, eugenia Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis |
| title | Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis |
| title_full | Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis |
| title_fullStr | Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis |
| title_full_unstemmed | Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis |
| title_short | Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis |
| title_sort | chapter exploring competitiveness and wellbeing in italy by spatial principal component analysis |
| topic | Well being Spatial Principal Component Analysis (sPCA) Composite Indicators |
| topic_facet | Well being Spatial Principal Component Analysis (sPCA) Composite Indicators |
| url | ONIX_20220601_9788855184618_544 |
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