Voronoi graphs and their hierarchical extensions have become a widely used tool for deriving abstract spatial representations from environmental data. They are currently being studied and applied in several subfields of computer science such as AI and robotics, computational geometry, and geoinformatics.

Bayesian networks have received a lot of attention over the last few decades from both scientists and engineers, and across a number of fields, including artificial intelligence (AI), statistics, cognitive science, and philosophy.

Completely updated and containing two new chapters, this title covers spatial analysis and urban management using graph theory simulation.  Highly practical, the simulation approach allows readers to solve classic problems such as placement of high-speed roads, the capacity of a network, pollution emission control, and more.