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 allow to select spatially balanced samples, which lead to particularly efficient estimates, by incorporating the distances among 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 implemented even when spatial information is not completely accurate. In particular, by means of a Montecarlo sampling simulation study about the estimation of water pollution, it is proved that the use of spatial sampling methods still lead to more spatially balanced samples, and more efficient estimates, also when the geocoding of population is not exact.