Incomplete geocoding and spatial sampling: The effects of locational errors on population total estimation

Due to the increasing availability of georeferenced microdata in several fields of research, surveys can benefit greatly from the use of the most recent spatial sampling methods. These methods facilitate selection of spatially balanced samples, which lead to particularly efficient estimates, by incorporating the distances between the exact locations of statistical units into the design. Unfortunately, since locations of units are rarely exact in practice due to imperfections in the geocoding processes, the implementation of spatial sampling designs is actually often limited. This paper aims at demonstrating that spatial sampling designs can be effectively carried out even when spatial information is not completely accurate. By means of Monte Carlo simulation studies, this paper proves that even when the geocoding of population is not exact, spatial sampling methods still facilitate more spatially balanced samples and more efficient estimates.