Spatial data warehouses (SDWs) and spatial OLAP (SOLAP) are well-known business intelligence (BI) technologies that aim to support multidimensional and online analysis of huge volumes of data with spatial reference. Spatial vagueness is one of the most neglected imperfections of spatial data. Although several works propose new ad-hoc models for handling spatial vagueness, their implementation in spatial database management systems (DBMS) and SDW is still in an embryonic state. In this paper, we present a new design method for SOLAP datacubes that allows handling vague spatial data analysis issues. This method relies on a risk management method applied to the potential risks of data misinterpretation and decision makersÂ’ tolerance levels to those risks. We also present a tool implementing our method and a validation of the method is done based on the designed datacubes schemas testing.