In a world where researchers are more and more confronted to large sets of micro-data, new algorithms are constantly developed that have to be translated into usable programs. Modular GIS toolkits such as GRASS GIS offer a middle way between low-level programming approaches and GUI-based desktop GIS. The modules can be seen as elements of a programming language which makes the implementation of algorithms for spatial analysis very easy for researchers. Using two examples of algorithms in economic geography, for estimating regional exports and for determining raster-object neighborhood matrices, this paper shows how just a few module calls can replace more complicated low-level programs, as long as the researcher can change perspective from a pixel-by-pixel view to a map view of the problem at hand. Combining GRASS GIS with Python as general glue between modules also offers options for easy multi-processing, as well as supporting the increasingly loud call for open research, including open source computing tools in research.