The author introduces the network data structure, the theoretical basis of Network analysis in graph theory and topology. It also discusses that networks are an alternative way of representing the location and the difficulties in the network location problems.
I am particularly interested in the discussion of the 3 types of data models in the evolution of network analysis. The author proposes that it is important to preserve the topological properties of the data whereas these properties also impose difficult constraints on network analysts. The author gave the scenario in which a vertex must exist in the crossing whether or not a true intersection exists, which is problematic when modeling bridges or
tunnels. This reminds me of the project that I’m working on. I plan to use a supervised machining algorithm to extract the road network from satellite images and turn it into a road network. However, the preservation of the topological properties such as the identification of if a crossing is a true intersection or a bridge is extremely difficult. The author then discusses the pure network data structure that is currently widely used in GIS. I think this is a useful topic for me to look into, although I am still not so clear about what is the difference between these two structures and what is planarity requirements that the author mentioned in discussing the two data models.