Spatial aggregation with respect to a population distribution: Impact on inference.

ABSTRACT: Spatial aggregation with respect to a population distribution involves estimating aggregate population quantities based on observations from individuals. In this context, a geostatistical workflow must account for three major sources of aggregation error: aggregation weights, fine scale variation, and finite population variation. However, these sources of aggregation error are commonly ignored, and the population instead treated as a fixed population density surface. We improve common practice by introducing a sampling frame model allowing aggregation models to account for aggregation error simply and transparently. This preserves aggregate point estimates while increasing their uncertainties. We compare the proposed and the traditional approach using two simulation studies m

SUBMITTER: Paige J 

PROVIDER: S-EPMC11526805 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature