Spatial image processing, such as Retinex, ACE, spatialfrequency, or bilateral, filtering, use the entire image in rendering scenes. These algorithms process captured scene radiances as input; then use the spatial information to synthesize a new image for rendition to a display or print. Spatial algorithms have different properties from pixel-processing algorithms. Pixel processes apply the same transform to all image pixels, so that all pixels with the same input value (i) have the same output value (o). However, spatial algorithms can convert identical input values into different output values. We discuss techniques most appropriate for measuring the success of spatial algorithms.We would like a simple figure-of-merit calculation for our favorite algorithm. We found that goal impractical. Spatial color algorithms are in the middle of the imaging chain, and their success is affected by pre- and post-processing. There are a variety of distinct goals for different spatial algorithms: one is to find the objects' reflectance; one is to find the illumination; one is to make the best HDR picture; another is to model human vision. As well, there are different ground truth goals for each type of algorithm. Instead of presenting a universal solution to evaluate all types of algorithms, we describe a number of steps measuring scene characteristics that evaluate spatial processes. We describe examples of a number of control and test experiments that are useful in quantitative evaluation of portions of the imaging chain. This paper provides test images, measurements of scene characteristics, and examples of a set of flexible tools for quantitative evaluations of spatial color algorithms. Quantitative measurements of spatial algorithms evaluate the true performance of the central spatial process. This paper works in parallel with <http://sites.google.com/site/3dmondrians/> that provides appendices for detailed data.