A competitive design-based spatial predictor

Under the finite population design-based framework, locations' spatial information coordinates of a population have traditionally been used to develop efficient sampling designs rather than for estimation or prediction. We propose to enhance design-based individual prediction by exploiting the spatial information derived from geography, which is available for each population element before sampling. Individual predictors are obtained by reinterpreting deterministic interpolators under the finite population design-based framework, making it possible to derive their statistical properties. Monte Carlo experiments on real and simulated data help to appreciate the performances of the proposed approach in comparison both with estimators that do not employ spatial information and with kriging. We found that under the most favorable conditions for kriging, the proposed predictor shows at least the same performances, while outperforming kriging for small sample sizes.