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Mapping Patterns of Multiple Deprivation and Well-Being using Self-Organizing Maps: An Application to Swiss Household Panel Data

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  • Mario Lucchini
  • Jenny Assi

Abstract

The aim of this paper is to propose multidimensional measures of deprivation and wellbeing in contemporary Switzerland, in order to overcome the limitations of standard approaches. More precisely, we have developed self organising maps (SOM) using data drawn from the 2009 Swiss Household Panel wave, in order to identify highly homogeneous clusters of individuals characterized by distinct profiles across 44 indicators of deprivation and well-being. SOM is a vector quantiser that performs a topology-preserving mapping of the k-dimensional input data to a two-dimensional, rectangular grid of output units, preserving as much as possible the information contained in the original input data. “Topology-preserving” means that, when an SOM is properly developed, units that are close in the output space are also close in the input space. Our results suggest that the SOM approach could improve our understanding of complex and multidimensional phenomena, like those of well-being, deprivation, vulnerability, that show only a partial overlapping with standard income poverty measures. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Mario Lucchini & Jenny Assi, 2013. "Mapping Patterns of Multiple Deprivation and Well-Being using Self-Organizing Maps: An Application to Swiss Household Panel Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(1), pages 129-149, May.
    • Handle: RePEc:spr:soinre:v:112:y:2013:i:1:p:129-149
    DOI: 10.1007/s11205-012-0043-7
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    2. Noor Hidayah Zakaria & Rohayanti Hassan & Muhamad Razib Othman & Zalmiyah Zakaria & Shahreen Kasim, 2017. "A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 7(11), pages 450-458, November.
    3. Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.
    4. Dong Lu & Ye Tian & Vincent Y. Liu & Yi Zhang, 2015. "The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis," Sustainability, MDPI, vol. 7(6), pages 1-18, June.
    5. Lara Maestripieri, 2018. "A Job of One’s Own. Does Women’s Labor Market Participation Influence the Economic Insecurity of Households?," Societies, MDPI, vol. 8(1), pages 1-32, January.

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