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Introducing a new centrality measure from the transportation network analysis in Greece

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Abstract

This paper introduces a new centrality measure, nominated by the authors as mobility centrality (C m), which arose during the study of the interregional road network in Greece. The proposed measure is constructed by the use of the anagogic method considering the formula of the kinetic energy of a particle in Physics and adjusting its mathematical analogue to the case of the interregional road network. The new centrality measure is estimated to be useful for the operational analysis of a network (such as the analysis of network flows), since it characterizes as central these vertices that appear to have the greatest tendency to attract or expel network flows. The ability of the proposed centrality measure to illustrate flow tendencies was examined empirically by correlating and regressing mobility centrality to the commuting status of the Greek interregional network and to four of the most common existing centrality measures (betweenness, closeness, straightness and degree centrality) of the available data. The empirical analysis indicated that the proposed centrality measure (C m) presents sufficient ability to describe the status of the Greek interregional commuting system and it is believed that it is able to describe the inner potential that produces flows in a network.

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References

  • Adler, N., Hakkert, A., Kornbluth, J., Raviv, T., & Sher, M. (2013). Location-allocation models for traffic police patrol vehicles on an interurban network. Annals of Operations Research. doi:10.1007/s10479-012-1275-2.

    Google Scholar 

  • Albert, R., & Barabasi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–94.

    Article  Google Scholar 

  • Aronson, J. (1989). A survey of dynamic network flows. Annals of Operations Research, 20, 1–66.

    Article  Google Scholar 

  • Barnett, V., & Lewis, T. (1994). Outliers in statistical data (3rd ed.). Chichester: Wiley.

    Google Scholar 

  • Borgatti, S., & Everett, M. (2006). A graph-theoretic perspective on centrality. Social Networks, 28, 466–484.

    Article  Google Scholar 

  • Carling, K., Han, M., & Hakansson, J. (2012). Does Euclidean distance work well when the p-median model is applied in rural areas? Annals of Operations Research, 201, 83–97.

    Article  Google Scholar 

  • Crucitti, P., Latora, V., & Porta, S. (2006). Centrality in networks of urban streets. Chaos, 16(015113), 1–9.

    Google Scholar 

  • Devore, J., & Berk, K. (2012). Modern mathematical statistics with applications. London: Springer.

    Book  Google Scholar 

  • Diestiel, R. (2005). Graph theory (3rd ed.). Heidelberg: Springer.

    Google Scholar 

  • Erath, A., Lochl, M., & Axhausen, K. (2009). Graph-theoretical analysis of the swiss road and railway networks over time. Networks and Spatial Economics, 9, 379–400.

    Article  Google Scholar 

  • Estrada, E., & Bodin, O. (2008). Using network centrality measures to manage landscape connectivity. Ecological Applications, 18(7), 1810–1825.

    Article  Google Scholar 

  • Fernandez, F., & Puerto, J. (2012). The minimum cost shortest-path tree game. Annals of Operations Research. doi:10.1007/s10479-011-1043-8.

    Google Scholar 

  • Fleming, D., & Hayuth, Y. (1994). Spatial characteristics of transportation hubs: centrality and intermediacy. Journal of Transport Geography, 2(1), 3–18.

    Article  Google Scholar 

  • GNSS—Greek National Statistics Service (ESYE) (2007). Statistical yearbook. Data from National Census 2001.

  • Hamacher, H., Heller, S., & Rupp, B. (2011). Flow location (FlowLoc) problems: dynamic network flows and location models for evacuation planning. Annals of Operations Research. doi:10.1007/s10479-011-0953-9.

    Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Data mining, inference and prediction (2nd ed.). New York: Springer.

    Google Scholar 

  • Hays, L. (1981). Statistics (3rd ed.). New York: Holt, Rinehart and Winston.

    Google Scholar 

  • Irwin, M., & Hughes, H. (1992). Centrality and the structure of urban interaction: measures, concepts, and applications. Social Forces, 71(1), 17–51.

    Article  Google Scholar 

  • Kalantzi, O., & Tsiotas, D. (2011). Social network sites: the case of FacebookTM in education. In D. Tseles, K. Malafantis, & A. Pamouktsoglou (Eds.), Education and society: research and innovation in new technologies (pp. 85–93). Athens: Sygchroni Ekdotiki (Modern Publishing).

    Google Scholar 

  • Koschutzki, D., Lehmann, K., Peeters, L., & Richter, S. (2005). Centrality indices. In U. Brandes & T. Erlebach (Eds.), Network analysis (pp. 16–61). Berlin: Springer.

    Chapter  Google Scholar 

  • Lewer, J., & Van den Berg, H. (2008). A gravity model of immigration. Economics Letters, 99, 164–167.

    Article  Google Scholar 

  • Marin, A., & Jaramillo, P. (2009). Urban rapid transit network design: accelerated Benders decomposition. Annals of Operations Research, 169, 35–53.

    Article  Google Scholar 

  • Norusis, M. (2004). SPSS 13.0 statistical procedures companion. New York: Prentice Hall.

    Google Scholar 

  • Patuelli, R., Reggiani, A., Gorman, S., Nijkamp, P., & Bade, F. J. (2007). Network analysis of commuting flows: a comparative static approach to German data. Networks and Spatial Economics, 7, 315–331.

    Article  Google Scholar 

  • Piva, B., & Carvalho de Souza, C. (2011). Polyhedral study of the maximum common induced subgraph problem. Annals of Operations Research. doi:10.1007/s10479-011-1019-8.

    Google Scholar 

  • Polyzos, S. (2009). The Egnatia motorway and the changes in interregional trade in Greece: an ex ante assessment. European Spatial Research and Policy, 16(2), 23–47.

    Article  Google Scholar 

  • Polyzos, S. (2011). Regional development. Athens: Kritiki.

    Google Scholar 

  • Polyzos, S., & Niavis, S. (2013). Evaluating port efficiency in the Mediterranean. International Journal of Data Analysis Techniques and Strategies, 5(1), 84–100.

    Article  Google Scholar 

  • Polyzos, S., & Tsiotas, D. (2012). The evolution and spatial dynamics of coastal cities in Greece. In S. Polyzos (Ed.), Urban development (pp. 275–296). Rijeka: Intech. 2012.

    Chapter  Google Scholar 

  • Reggiani, A., Bucci, P., & Russo, G. (2011). Accessibility and network structures in the German commuting. Networks and Spatial Economics, 11, 621–641.

    Article  Google Scholar 

  • Rendl, F., & Wolkowicz, H. (1995). A projection technique for partitioning the nodes of a graph. Annals of Operations Research, 58, 155–179.

    Article  Google Scholar 

  • Serway, R. (1990). Physics for scientists & engineers, Vol. I (3rd ed.). London: Saunders College.

    Google Scholar 

  • Sgroi, D. (2008). Social network theory, broadband and the future of the World Wide Web. Telecommunications Policy, 32, 62–84.

    Article  Google Scholar 

  • Sharkey, W. (1992). Supportability of network cost functions. Annals of Operations Research, 36, 1–16.

    Article  Google Scholar 

  • Tabata, M., Eshimab, N., & Takagi, I. (2011). A mathematical modeling approach to the formation of urban and rural areas: convergence of global solutions of the mixed problem for the master equation in sociodynamics. Nonlinear Analysis: Real World Applications, 12, 3261–3293.

    Article  Google Scholar 

  • Ter Wal, A., & Boschma, R. (2009). Applying social network analysis in economic geography: framing some key analytic issues. The Annals of Regional Science, 43, 739–756.

    Article  Google Scholar 

  • Tsiotas, D., & Polyzos, S. (2011). Introducing an inequalities index from the simple perceptron pattern in ANN: an unemployment inequalities application in regional economics. Journal of Engineering Science and Technology Review, 4(3), 291–296.

    Google Scholar 

  • Tsiotas, D., & Polyzos, S. (2013). The contribution of ANN’s simple perceptron pattern to inequalities measurement in regional science. International Journal of Operational Research, 13(2), 289–301.

    Article  Google Scholar 

  • Tsiotas, D., Polyzos, S., & Alexiou, A. (2012). Analyzing the interregional transportation network in Greece by using graph theory. In Proceedings of the 3rd Pan-Hellenic conference in urban and regional planning and development, Volos, Greece, 27–30 Sep. 2012 (pp. 1100–1106). Thessaloniki: Graphima Publications. ISBN 978-960-6865-52-7.

    Google Scholar 

  • Wang, H., Mo, H., Wang, F., & Jin, F. (2010). Exploring the network structure and nodal centrality of China’s air transport network: a complex network approach. Journal of Transport Geography. doi:10.1016/j.jtrangeo.2010.08.012.

    Google Scholar 

  • Wang, F., Antipova, A., & Porta, S. (2011). Street centrality and land use intensity in Baton Rouge, Louisiana. Journal of Transport Geography, 19, 285–293.

    Article  Google Scholar 

  • Yu, M. (2010). Trade, democracy, and the gravity equation. Journal of Development Economics, 91, 289–300.

    Article  Google Scholar 

  • Zetterberg, A., Mortberg, U., & Balfors, B. (2010). Making graph theory operational for landscape ecological assessments, planning, and design. Landscape and Urban Planning, 95, 181–191.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38 334, Greece

    Dimitrios Tsiotas & Serafeim Polyzos

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  1. Dimitrios Tsiotas
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  2. Serafeim Polyzos
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Correspondence to Dimitrios Tsiotas.

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Tsiotas, D., Polyzos, S. Introducing a new centrality measure from the transportation network analysis in Greece. Ann Oper Res 227, 93–117 (2015). https://doi.org/10.1007/s10479-013-1434-0

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