A Voronoi diagram for a set of input data points (called generators) defines cells (polygons) around each generator such that any point within a given cell is closer to its generator than to any other generator in the system. On a sphere for example, this means you could rapidly identify the closest airport to a search and rescue region on the surface of the earth. I've produced an open source Python implementation of the Voronoi diagram on the surface of a sphere. The (https://github.com/tylerjereddy/py_sphere_Voronoi "repository") and (http://py-sphere-voronoi.readthedocs.org/en/latest/voronoi_utility.html "documentation") are both available online. The code is capable of producing spherical Voronoi diagrams for a wide range of input test cases as verified by the unit tests, and further exposes the individual surface areas and coordinates of each Voronoi cell on the surface. I will discuss the procedure (algorithm) I've used to generate the spherical Voronoi diagram and highlight limitations / challenges moving forward along with some example applications (i.e., viruses).

The remaining challenges, for which computational geometry and engineering expertise would be most welcome, will be discussed in some detail. For example, although the % surface area reconstitution when summing up the areas of the Voronoi cells is generally > 95 % of the theoretical surface area of the sphere, there are occasional (albeit rare) cases where a vertex is excluded from a Voronoi cell, leaving 'blank' spaces in the diagrams. There is also a general susceptibility to numerical / floating point instability in some of the calculations, presumably due to the substantial number of trigonometric operations. Again, input from experienced engineers and mathematicians would be most helpful in improving this open source effort.