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This paper discusses the software D-STEM as a statistical tool for the analysis and mapping of environmental space-time variables. The software is based on a flexible hierarchical space-time model which is able to deal with multiple variables, heterogeneous spatial supports, heterogeneous sampling networks and missing data. Model estimation is based on the expectation maximization algorithm and it can be performed using a distributed computing environment to reduce computing time when dealing with large data sets. The estimated model is eventually used to dynamically map the variables over the geographic region of interest. Three examples of increasing complexity illustrate usage and capabilities of D-STEM, both in terms of modeling and implementation, starting from a univariate model and arriving at a multivariate data fusion with tapering.