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High-resolution population data are a necessary basis for identifying affected regions (e.g., natural disasters, accessibility of social infrastructures) and deriving recommendations for policy and planning, but municipalities are, as in Germany, regularly the smallest available reference unit for data. The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. In addition to common test statistics like MAE or MAPE, the Gini-coefficient and the local Moran’s I are applied and their added value for accuracy assessment is tested. With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. With 23.6%, the dispersion measure cannot improve distribution accuracy. Overall, the algorithms perform better for urban than for rural areas. Gini-coefficients show that same spatial concentration patterns are achieved as in the actual population distribution. According to local Moran’s I, there are statistically significant underestimations, especially in the highly-dense inner-urban areas. Overestimates are found in the transition to less urbanized areas and the core areas of peripheral cities. Overall, the additional test statistics can provide important insights into the data, which go beyond common methods for evaluation.

Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany