Techniques for obtaining random point samples from spatial databases are described. Random points are sought from a continuous domain that satisfy a spatial predicate which is represented in the database as a collection of polygons. Several applications of spatial sampling are described. Sampling problems are characterized in terms of two key parameters: coverage (selectivity), and expected stabbing number (overlap). Two fundamental approaches to sampling with spatial predicates, depending on whether one samples first or evaluates the predicate first, are discussed. The approaches are described in the context of both quadtrees and R-trees, detailing the sample-first, A/R-tree, and partial area tree algorithms. A sequential algorithm, the one-pass spatial reservoir algorithm, is also described.<>