The spatial data sampling methods are geographic variations on the traditional sampling schemes of random, systematic and stratified sampling. These "random, systematic and stratified" concepts are essentially used on "location" to determine the chosen samples. The choice of method is also dependent on whether the population is discrete or continuous.

We will consider a number of methods in this virtual field trip. They include:

1. Random spatial sampling - based completelyl random locations within the study region.
2. Systematic (or grid) spatial sampling - based on a regular spaced pattern of sample points.
3. Stratified spatial sampling - based on sampling within defined geographic subregions.

Finally, we will also consider sampling along a linear feature. This is referred to as transect sampling.

Spatial dependency

A key factor in spatial sampling is the concept of spatial dependency which recognises the need to rely on locations in proximity to one another. Locations in closer geographic proximity are expected to have more similar values than those farther away.

To think about...

Are these "broad" sampling schemes mutually exclusive? For example, is stratified sampling totally independent of systematic sampling, or can systematic (or random) sampling be used within the strata?