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Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design-based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations. [ABSTRACT FROM AUTHOR]

Two-dimensional systematic sampling and maximal stratification are frequently used in spatial surveys, because of their ease of implementation and design efficiency. An important drawback of these designs, however, is that no direct estimator of the design variance is available. In this paper estimation of the sampling variance of a total in a model-based context is considered. The estimation strategy is based on the use of the sample variogram which can be either a non-parametric or a parametric one. Consistency of the estimators is discussed; simulations and an application to real data show the good performance of the proposed procedure in practice. [ABSTRACT FROM AUTHOR]