Le résumé en français n'a pas été communiqué par l'auteur.The combination of several socio-economic data bases originating from different administrative sources collected on several different partitions of a geographic zone of interest into administrative units induces the so called areal interpolation problem. This problem is that of allocating the data from a set of source spatial units to a set of target spatial units. At the European level for example, the EU directive ’INSPIRE’, or INfrastructure for Spatial InfoRmation, encourages the states to provide socio-economic data on a common grid to facilitate economic studies across states. In the literature, there are three main types of such techniques: proportional weighting schemes, smoothing techniques and regression based interpolation. We propose a theoretical evaluation of these statistical techniques for the case of count related data. We find extensions of some of these methods to new cases : for example, we extend the ordinary dasymetric weightingmethod to the case of an intensive target variable Y and an extensive auxiliary quantitative variable X and we introduce a scaled version of the Poisson regression method which satisfies the pycnophylactic property. We present an empirical study on an American database as well as an R-package for implementing these methods
