Spatial Sampling

Spatial sampling selects a sample from a geographically distributed target population, and then uses the sample to infer parameters of the target population, such as the mean and values at unsampled sites. A good sample provides accurate inference using a small sample size, which is determined by the properties of the target, the method of sampling, and the methods of inference, known collectively as the spatial sampling trinity. A simple random sample may be less efficient when the target presents spatial autocorrelation and stratified heterogeneity. These properties are taken into account in spatial sampling.