A Model to Visualize Information in a Complex Streets’ Network
This paper discusses a process to graphically view and analyze information obtained from a network of urban streets, using an algorithm that establishes a ranking of importance of the nodes of the network itself. The basis of this process is to quantify the network information obtained by assigning numerical values to each node, representing numerically the information. These values are used to construct a data matrix that allows us to apply a classification algorithm of nodes in a network in order of importance. From this numerical ranking of the nodes, the process finish with the graphical visualization of the network. An example is shown to illustrate the whole process.
KeywordsCommercial Activity Primal Graph Short Path Tree Urban Network PageRank Algorithm
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- 1.Afryzkov, T., Oliver, J.L., Tortosa, L., Vicent, J.: An algorithm for ranking the nodes of an urban network based on concept of PageRank vector. Applied Mathematics and Computation (219), 2186–2193 (2012)Google Scholar
- 2.Barabasi, A.L.: Emergence of Scaling in Random Networks Science, pp. 286–509 (1999)Google Scholar
- 3.Barabasi, A.L.: Statistical Mechanics of Complex Networks. Review of Modern Physics 74, 47 (2002)Google Scholar
- 5.Batty, M.: Cities and Complexity. Understanding Cities with Cellular Automata, Agent Based Models, and Fractals. The MIT Press, Cambridge (2005)Google Scholar
- 7.Crucitti, P., Latora, V., Porta, S.: Centrality measures in spatial networks of urban streets. Physical Review E 73, 036125 (2006)Google Scholar
- 16.Noh, J.D., Rieger, H.: Random walks on complex networks. Physical Review Letters 92, 118701-1–118701-4 (2004)Google Scholar
- 17.Page, L., Brin, S., Motwani, R., Winogrand, T.: The pagerank citation ranking: Bringing order to the web. Technical report 1999-66, Stanford InfoLab (1999)Google Scholar