Spatial Analysis 3D is a user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. Examples of the types of analyses performed include those derived from the Delaunay tessellation associated with such spatial point patterns, and those associated with the correlation of such point patterns, including autocorrelation analysis and its derived density recovery profile, as well as the related K, F, and G-functions. The stimulus for the development of Spatial Analysis 3D has been the study of neuronal positioning within the central nervous system, but many other applications in science, engineering, statistics and mathematics should benefit from this suite of programs.
Spatial Analysis 3D is the project of a collaborative research effort between Drs. Benjamin Reese, Mary Raven, and Dan Lofgreen at the Unversity of California at Santa Barbara and Dr. Stephen Eglen at the University of Cambridge. It has been supported by a grant from the National Institute of Mental Health through the Neurotechnology Research, Development and Enhancement Program. It grew out of our efforts to quantify the regularity and simulate the patterning found in distributions of nerve cells across the retina, a structure in the central nervous system where uniformity in nerve cell spacing plays a critical role in retinal function.