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Mapping Patterns of Multiple Deprivation Using Self-Organising Maps: An Application to EU-SILC Data for Ireland. ESRI WP286. March 2009

Pisati, Maurizio and Whelan, Christopher T. and Lucchini, Mario and Maitre, Bertrand (2009) Mapping Patterns of Multiple Deprivation Using Self-Organising Maps: An Application to EU-SILC Data for Ireland. ESRI WP286. March 2009. [Working Paper]

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    Abstract

    The development of conceptual frameworks for the analysis of social exclusion has somewhat out-stripped related methodological developments. This paper seeks to contribute to this process through the application of self-organising maps (SOMs) to the analysis of a detailed set of material deprivation indicators relating to the Irish case. The SOM approach allows us to offer a differentiated and interpretable picture of the structure of multiple deprivation in contemporary Ireland. Employing this approach, we identify 16 clusters characterised by distinct profiles across 42 deprivation indicators. Exploratory analyses demonstrate that position in the income distribution varies systematically by cluster membership. Moreover, in comparison with an analogous latent class approach, the SOM analysis offers considerable additional discriminatory power in relation to individuals’ experience of their economic circumstances. The results suggest that the SOM approach could prove a valuable addition to a ‘methodological platform’ for analysing the shape and form of social exclusion.

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    Item Type: Working Paper
    Subjects for non-EU documents: Countries > Ireland
    EU policies and themes > Policies & related activities > social policy > general
    Subjects for EU documents: UNSPECIFIED
    EU Series and Periodicals: UNSPECIFIED
    EU Annual Reports: UNSPECIFIED
    Series: Series > Economic and Social Research Institute (ESRI), Dublin > ESRI Working Papers
    Depositing User: Alyssa McDonald
    Official EU Document: No
    Language: English
    Date Deposited: 02 Dec 2017 18:14
    Number of Pages: 54
    Last Modified: 02 Dec 2017 18:14
    URI: http://aei.pitt.edu/id/eprint/87831

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