Density Estimation from Aggregated Data with Integrated Auxiliary Information: Estimating Population Densities with Geospatial Data

Density estimation for geospatial data ideally relies on precise geocoordinates, typically defined by longitude and latitude. However, such detailed information is often unavailable due to confidentiality constraints. As a result, analysts frequently work with spatially aggregated data, commonly visualized through choropleth maps. Approaches that reverse the aggregation process using measurement error models in the context of kernel density estimation have been proposed in the literature. From a methodological perspective, we extend this line of work by incorporating auxiliary information to improve the precision of density estimates derived from aggregated data. Our approach employs a correlation-based weighting scheme to combine the auxiliary density with the estimate obtained from aggregated data. We evaluate the method through a series of model-based simulation scenarios reflecting varying conditions of auxiliary data quality. From an applied perspective, we demonstrate the utility of our method in two real-world case studies: (1) estimating population densities from the 2022 German Census in Bavaria, using satellite imagery of nighttime light emissions as auxiliary data; and (2) analyzing brown hare hunting bag data in the German state of Lower Saxony. Overall, our results show that integrating auxiliary information into the estimation process leads to more precise density estimates.