Modularity Maximization and Tree Clustering: Novel Ways to Determine Effective Geographic Borders

D. Grady (), R. Brune (), C. Thiemann (), F. Theis () and D. Brockmann ()
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D. Grady: Northwestern University
R. Brune: Max Planck Institute for Dynamics and Self-Organization
C. Thiemann: Max Planck Institute for Dynamics and Self-Organization
F. Theis: Helmholtz Zentrum München
D. Brockmann: Northwestern University

Chapter Chapter 7 in Handbook of Optimization in Complex Networks, 2012, pp 169-208 from Springer

Abstract: Abstract Territorial subdivisions and geographic borders are essential for understanding phenomena in sociology, political science, history, and economics. They influence the interregional flow of information and cross-border trade and affect the diffusion of innovation and technology. However, most existing administrative borders were determined by a variety of historic and political circumstances along with some degree of arbitrariness. Societies have changed drastically, and it is doubtful that currently existing borders reflect the most logical divisions. Fortunately, at this point in history we are in a position to actually measure some aspects of the geographic structure of society through human mobility. Large-scale transportation systems such as trains and airlines provide data about the number of people traveling between geographic locations, and many promising human mobility proxies are being discovered, such as cell phones, bank notes, and various online social networks. In this chapter we apply two optimization techniques to a human mobility proxy (bank note circulation) to investigate the effective geographic borders that emerge from a direct analysis of human mobility.

Keywords: Null Model; Mobility Network; Gravity Model; Simulated Annealing Algorithm; Modularity Maximization (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/978-1-4614-0754-6_7

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