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.

福祉作为一个多维度的概念，已逐渐成为对地区进行分类以及评估和交流其经济、社会和环境绩效的重要衡量标准。特别是，主观措施越来越受到政策制定者的关注。在这种背景下，主观幸福感（SWB）概念的空间维度尚未得到充分探索，仍然是研究人员面临的重大挑战。本文试图检测和解释主观幸福感评估中的空间差异。为此，我们提出了一种适应SWB多维分析的创新聚类方法（ ClustGeo ）。该方法使我们能够建立感知生活质量的类别，并强调主观幸福感结构维度之间联系的空间模式。它是根据法国西南部沿海城市居民的看法，使用特定生活领域的数据进行实证应用的。结果强调了个体之间与生活质量感知相关的空间异质性。在测量中涉及空间成分使我们能够检测和识别局部趋势。即使在相似的周界上，聚类也能清晰区分类别，同时识别当地的特殊性，从而表明数据的可变性不一定和/或仅由地理位置驱动。ClustGeo提供的可能性为不同类型数据组合的进一步统计发展铺平了道路。