Spatially correlated Poisson sampling

A new method for sampling from a finite population that is spread in one, two or more dimensions is presented. Weights are used to create strong negative correlations between the inclusion indicators of nearby units. The method can be used to produce unequal probability samples that are well spread over the population in every dimension, without any spatial stratification. Since the method is very general there are numerous possible applications, especially in sampling of natural resources where spatially balanced sampling has proven to be efficient. Two examples show that the method gives better estimates than other commonly used designs.

► A new method for selecting spatially balanced samples is introduced.
► The method can select samples that are balanced in any number of dimensions.
► Theory and examples indicate that the method gives a high degree of spatial balance.
► Examples show that the method is efficient for populations with spatial trends.