To Assess Collective Well-Being with a Synthetic and Autocorrelate Index Tourism of Italian Provinces

  • Domenico TebalaEmail author
  • Domenico Marino
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 180)


Tourism is a phenomenon that is particularly important in the contribution to collective wellbeing. Moreover, data measured in a specific area (or province) can be influenced by what happens in nearby areas, generating what is commonly called “spatial autocorrelation” or “spatial interdependence”. This study is aimed at identifying a composite “systemic” index to measure the impact of tourist goods and others determinants can make to collective well-being in the provincial context through a composite index through the BES methodology (Equitable and Sustainable Well-being) and to analyze possible spatial autocorrelations between Italian provinces.

The method of construction of the index followed these steps:

(1) description of the theoretical framework, methodology used and indicators;

(2) description of the descriptive analysis results: in order to assess the robustness of the identified method and, therefore, improve decision-making, we also completed an influence analysis in order to analyze the most significant indicators (software COMIC - COMposite Indices Creator);

(3) a summary of the conclusions through a georeferenced map of the synthetic index of Italian tourism provinces and a Cluster Map LISA which shows the provinces with statistically significant values of the LISA index, classified by five categories: (A) Not significant (white); (B) High-High (red); (C) Low-Low (Blue); (D) Low-High (light blue); (E) High-Low (Light Red) (software GeoDa).


Tourism Index Autocorrelation Provinces 


  1. 1.
    Pernicola, C.: Il Community Empowerment sulle Comunità Locali a vocazione turistica (2007)Google Scholar
  2. 2.
    Istat: UrBes 2015 – Il benessere equo e sostenibile in Italia (2015)Google Scholar
  3. 3.
  4. 4.
  5. 5.
  6. 6.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Institute of StatisticsCatanzaroItaly
  2. 2.Mediterranea University of Reggio CalabriaReggio CalabriaItaly

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