The problem I want to solve is to tessellate the map given with the Blue Nodes(Source Nodes) as given input points, Once I am able to do this I would like to see how many Black Nodes(Demand Nodes) fall within each cell and assign it to the Blue Node associated with that cell.
I would like to know if there is a easier way of doing this without using Fortune's Algorithm.I came across this function under Mahotas called Mahotas.segmentation.gvoronoi(image)source. But I am not sure if this will solve my problem.
Also please suggest me if there is a better way of doing this segmentation(other than Voronoi tessellation). I am not sure if clustering algorithms would be a good choice. I am a programming newbie.
Here is an alternative approach to using Voronoi tessellation:
Build a k-d tree over the source nodes. Then for every demand node, use the k-d tree to find the nearest source node and increment a counter associated with that nearby source node.
The implementation of a k-d tree found at http://code.google.com/p/python-kdtree/ should be useful.