Data warehouses have been used for a large variety of applications in order to provide support for the information decision support systems during knowledge discover process. Geographical information systems have gotten the benefits from data warehousing considering that the spatial data is by itself complex and represent a huge amount of information that can be retrieved only by means of a data warehouse representation. In this paper we present a design methodology for a fuzzy spatial data warehouse and the emphasis is on the step of how to build the data cube. In our approach we pay special attention on the data dimensionality concerning the spatial and fuzziness. We propose the use of membership functions to speed the query process up during the extraction, transformation and load (ETL) process. Mondrian , ArcGIS and Cube Designer are the tools used to evaluate the results. To demonstrate the accuracy of our approach, we consider the risk zone (a potential natural disaster area ) surrounding the Popocatetl volcano and the results are compared with the CENAPRED (Prediction National Center) agency has.
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