Pebble_Pond performs morphologically based wave propagation on an input set of points on the plane, with the points corresponding to the locations of detected image features. The waves are allowed to pass through each other, resulting in an complex evolving state space from which can be obtained a diverse class of non-planar spatial measures and structures, e.g., all k nearest neighbors, k-th order Voronoi tessellations, and k-th order Gabriel graphs. One perspective on Pebble_Pond is that it takes spatial structure and transforms it into temporal structure. That is, at each iteration in the wave propagation, measures on the state space reflect spatial structure at the scale corresponding to the current iteration. Thus, at each iteration all measures obtained (in parallel) from the state space report on all spatial relations falling within the distance that the waves have propagated. This paper investigates how particular measures of the underlying state space can be used to provide basic input to a spatial learning system. The learning system to be described is based on induction via classifier systems that are modified by bucket-brigade and genetic algorithms. Classifier systems are especially useful in environments where pattern recognition actions at given time steps need to be linked with related pattern recognition actions occurring at latter time steps. This paper will describe how a classifier system can be built which exploits the parallelism and temporal ordering of the measures that arise out of the basic Pebble_Pond algorithm.