Zonal centrality measures and the neighborhood effect

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.

[1]  G R Leake,et al.  ACCESSIBILITY MEASURES AND THEIR SUITABILITY FOR USE IN TRIP GENERATION MODELS , 1979 .

[2]  Andrzej Maćkiewicz,et al.  TOWARDS A NEW DEFINITION OF TOPOLOGICAL ACCESSIBILITY , 1996 .

[3]  Qi Li,et al.  Nonparametric Econometrics: Theory and Practice , 2006 .

[4]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[5]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[6]  W. Anderson,et al.  Estimation of Trip Generation in Mexico City, Mexico, with Spatial Effects and Urban Densities , 2006 .

[7]  Massimo Marchiori,et al.  Vulnerability and protection of infrastructure networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Britta Ruhnau,et al.  Eigenvector-centrality - a node-centrality? , 2000, Soc. Networks.

[9]  J. Anthonisse.  The rush in a directed graph , 1971 .

[10]  A. Cuevas,et al.  A comparative study of several smoothing methods in density estimation , 1994 .

[11]  V. Latora,et al.  Centrality measures in spatial networks of urban streets. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  H. Mahmassani,et al.  GLOBAL MAXIMUM LIKELIHOOD ESTIMATION PROCEDURE FOR MULTINOMIAL PROBIT (MNP) MODEL PARAMETERS , 2000 .

[13]  R. Vickerman.  Accessibility, Attraction, and Potential: A Review of Some Concepts and Their Use in Determining Mobility , 1974 .

[14]  Tammam Zaki Nakkash,et al.  ACTIVITY-ACCESSIBILITY MODELS OF TRIP GENERATION , 1972 .

[15]  H. Mahmassani.  In Perpetual Motion: Travel Behaviour Research Opportunities and Application Challenges , 2002 .

[16]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[17]  J. G. Koenig,et al.  Indicators of urban accessibility: Theory and application , 1980 .

[18]  Licinio da Silva Portugal,et al.  Centrality indicators as an instrument to evaluate the integration of urban equipment in the area of influence of a rail corridor , 2009 .

[19]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[20]  W. G. Hansen.  How Accessibility Shapes Land Use , 1959 .

[21]  Robert E. Park,et al.  Physics and Society , 1940 .

[22]  Joan L. Walker,et al.  Integration of Choice and Latent Variable Models , 1999 .

[23]  Phillip Bonacich,et al.  Eigenvector-like measures of centrality for asymmetric relations , 2001, Soc. Networks.