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Spatial Techniques for Cluster Identification. Application on Poverty Clusters in Dobrogea Plateau and The Black Sea Hinterland. Spatial data analysis expresses the relevance of the geographical context in any study concerning the spatial dimension of a social or natural phenomenon. One of the applications of spatial data analysis consists in identifying and statistically testing the spatial clusters regarding different phenomena at regional level. This paper underscores the spatial dimension of poverty rate in the Dobrogea Plateau and the Black Sea hinterland by identifying the poverty clusters at commune level. The methodology of this study is based on exploratory spatial data analysis (ESDA), including Morans’I and LISA (local indicators for spatial association) tests for recognizing whether there is a tendency of cluster formation, and the location of these spatial associations. The results show the presence of two clusters of high poverty values in the central-western and south-western parts of the study area. A cluster of low poverty values has been identified along the Black Sea Coast and within the influence area of the Constanţa town. ESDA techniques used for identifying and analyzing the spatial distribution of clusters contribute to the investigation of intraregional disparities, thus underling the relevance of the spatial analyses for geographical applied studies. Discussions on the interconnections between poverty and intraregional disparities, viewed within the context of regional development programs and strategies at the level of the studied area, complete the paper.

Keywords: exploratory spatial data analysis (ESDA), spatial clusters, intraregional disparities, Dobrogea.