Cross Sectional and Longitudinal multivariate analysis of spatio-temporal well-being indicators

Anno
2019
Proponente Silvia Polettini - Professore Associato
Sottosettore ERC del proponente del progetto
PE1_14
Componenti gruppo di ricerca
Componente Categoria
Leonardo Salvatore Alaimo Dottorando/Assegnista/Specializzando componente non strutturato del gruppo di ricerca
Pierpaolo D'Urso Componenti strutturati del gruppo di ricerca
Paola Giacomello Componenti strutturati del gruppo di ricerca
Abstract

GDP was never designed to be a comprehensive measure of prosperity and well-being. Finding alternative indicators of progress, wealth and well-being, more inclusive of environmental and social aspects, have become a mainstream issue in the public and scientific debate.
The Equitable and Sustainable Well-being (BES) project, led by Istat, is a crucial source of information for researchers and policy makers in Italy, and many studies describing several aspects of well-being in Italy have been published based on these data.
The main aim of the project is to develop statistical procedures useful to analyse the evolution of well-being over time in Italy, to find patterns or similarities in the geographical areas and to highlight their main characteristics across space and time, a problem that has not been fully investigated so far.
The research aims at developing statistical procedures for the description and the interpretation of well being with the following main objectives:

1)  Inspecting the cross-sectional structure of the set of indicators at each time occasion
2) Highlighting the temporal and spatio-temporal structure of the BES data,  to highlight the evolution over time of the whole set of well-being indicators, and  finding groups of units that exhibit similar patterns across time;  characterizing the spatial profile of well-being in the multivariate space defined by the set of indicators
3) Describing the longitudinal and spatial variation of well-being in Italy based on the BES data made available by Istat

ERC
PE1_14, PE1_18, SH1_3
Keywords:
ANALISI MULTIVARIATA, CLUSTER ANALYSIS, ANALISI DI DATI SPAZIALI, ANALISI DELLE SERIE TEMPORALI, INCERTEZZA

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