The book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial\ud
information in support of decision systems. Over the last few years there has been a considerable interest in\ud
these tools and in the role they can play in spatial planning and environmental modelling.\ud
One of the earliest and most famous definition of spatial planning was “a geographical expression to the\ud
economic, social, cultural and ecological policies of society”: borrowing from this point of view, this text shows\ud
how an interdisciplinary approach is an effective way to an harmonious integration of national policies with\ud
regional and local analysis.\ud
A wide range of spatial models and techniques is, also, covered: spatial data mining, point processes analysis,\ud
nearest neighbor statistics and cluster detection, Fuzzy Regression model and local indicators of spatial\ud
association; all of these tools provide the policy-maker with a valuable support to policy development
The numerous concepts of socio-economic hardship are, furthermore, attributable to a traditional distinction between absolute and relative conditions of hardship. The options of scientific research were therefore oriented towards the establishment of a multi-dimensional approach, sometimes abandoning dichotomous logic in order to arrive at fuzzy classifications in which each unit belongs and, at the same time, does not belong, to a category. A multidimensional index that considers hardship as the overall condition of being disadvantaged and deprived seems the most appropriate in view of the socio-economic differential analysis of demographic phenomena. The approach used in this work to synthesize and measure the conditions of the hardship of a population is based on a clustering procedure (Fuzzy c-means) aimed at outlining various not defined a priori profiles, which should be assigned to each family with different socio-economic behaviours. In comparison with conventional methods, this clustering method allows a set of data to belong not only to a main cluster but also to two or more clusters with "fuzzy profiles".
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