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Zonal centrality measures and the neighborhood effect

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  • Sohn, Keemin
  • Kim, Daehyun
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    Abstract

    The goal of this study is to develop a robust methodology for computing zone centrality measures in an urban area. Centrality refers to the relative importance of a zone in terms of network efficiency and utility for both transportation and urban study. Centrality indices that were developed to describe human relationships in the field of structural sociology were adopted. It is important to accommodate the neighborhood effect in dealing with centrality. The neighborhood effect describes the phenomenon whereby the attractiveness of a specific zone is affected by its neighbor zones. Kernel functions were employed to accommodate the neighborhood effect. The optimal bandwidth parameters were derived indirectly within the framework of trip attraction estimation under the assumption that the trip attraction of a zone is influenced by the integrated centrality, which includes the neighborhood effect. The well-known estimation tool of maximum likelihood estimation (MLE) was adopted to find the optimal bandwidth. As a byproduct of accommodating the neighborhood effect in centralities, a considerable advantage of the present study is an enhancement of the performance of trip attraction model. Another meaningful contribution of this study is a solution to the question of an acceptable delineation of the two city centers in Seoul. The boundaries of the two city centers were derived based on both the kernel function and its bandwidth.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 44 (2010)
    Issue (Month): 9 (November)
    Pages: 733-743

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    Handle: RePEc:eee:transa:v:44:y:2010:i:9:p:733-743

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    Related research

    Keywords: Centrality Closeness Betweenness Straightness Neighborhood effect Kernel function;

    References

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    1. Mackiewicz, Andrzej & Ratajczak, Waldemar, 1996. "Towards a new definition of topological accessibility," Transportation Research Part B: Methodological, Elsevier, vol. 30(1), pages 47-79, February.
    2. Liu, Yu-Hsin & Mahmassani, Hani S., 2000. "Global maximum likelihood estimation procedure for multinomial probit (MNP) model parameters," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 419-449, June.
    3. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    4. R W Vickerman, 1974. "Accessibility, attraction, and potential: a review of some concepts and their use in determining mobility," Environment and Planning A, Pion Ltd, London, vol. 6(6), pages 675-691, June.
    5. Cao, Ricardo & Cuevas, Antonio & Gonzalez Manteiga, Wensceslao, 1994. "A comparative study of several smoothing methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 153-176, February.
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    Cited by:
    1. Moonsoo Ko & Taewan Kim & Keemin Sohn, 2013. "Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation," Transportation, Springer, vol. 40(1), pages 91-107, January.
    2. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    3. Gómez, Daniel & Figueira, José Rui & Eusébio, Augusto, 2013. "Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems," European Journal of Operational Research, Elsevier, vol. 226(2), pages 354-365.

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