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Spatial Patterns of Subjective Well-Being on the Aquitaine Coastline, France

Author

Listed:
  • Vanessa Kuentz-Simonet

    (INRAE)

  • Tina Rambonilaza

    (AgroSup Dijon, INRAE, Univ. Bourgogne Franche-Comté)

Abstract

Well-Being, as a mutidimensional concept, has progressively become an essential measure to categorize territories and to evaluate and communicate on their economic, social and environmental performance. Especially, subjective measures are gaining increasing attention for policy makers. In this context, the spatial dimension of the concept of Subjective Well-Being (SWB) has not been sufficiently explored and remains a major challenge for researchers. This paper is an attempt to detect and explain spatial differentiations in the assessment of SWB. To this end, we propose an innovative clustering method (ClustGeo) adapted to the multidimensional analysis of SWB. The approach enables us to establish classes of perceived quality of life, and to highlight spatial patterns of the linkages between the structuring dimensions of SWB. It is applied empirically, using data on specific life domains, as perceived by the inhabitants of coastal municipalities in south-western France. The results highlight the spatial heterogeneity among individuals in relation to their perception of quality of life. Involving the spatial component in the measurement enables us to detect and identify local trends. The clustering builds a clear distinction of classes even on similar perimeters while identifying local particularities, thus showing that the variability of data is not necessarily and/or solely driven by geographic location. The possibilities offered by ClustGeo pave the way for further statistical developments in the combination of different types of data.

Suggested Citation

  • Vanessa Kuentz-Simonet & Tina Rambonilaza, 2024. "Spatial Patterns of Subjective Well-Being on the Aquitaine Coastline, France," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 19(2), pages 415-444, April.
    • Handle: RePEc:spr:ariqol:v:19:y:2024:i:2:d:10.1007_s11482-023-10248-y
    DOI: 10.1007/s11482-023-10248-y
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    References listed on IDEAS

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    1. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
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