Beschreibung:
The introduction of geospatial data into modelling efforts carries many advantages but also introduces numerous challenges. A common challenge is the Modifiable Areal Unit Problem (MAUP), describing how results change as the spatial aggregation of data changes. Here, we have studied MAUP in geospatial least-cost electrification modelling. We do this by assessing the effects of using 26 different population bases each for Benin, Malawi and Namibia. We use the population bases to generate 2080 electrification scenarios per country and conducting a global sensitivity analysis using the Delta Moment-Independent Measure. We identify population aggregation to be highly influential to the model results with regards to method of aggregation (delta values of 0.06-0.24 depending on output studied), administrative division (0.05-0.14), buffer chosen in the clustering process (0.05-0.32) and the minimum number of neighbours within the buffer required for clustering (0.05-0.19). Based on our findings, we conclude that geospatial electrification studies are not robust concerning the choice of population data. We suggest, that modelers put larger emphasis on different population aggregation methods in their sensitivity analyses and that the methods chosen to conduct sensitivity analysis are global in nature (i.e. moving all inputs simultaneously through their possible range of values).