A Statistical Method for Analyzing the Relative Location of Points in a Bounded Region

This paper develops a statistical method for analyzing the relative location of points in a bounded region. The location of points in relation to the center of the region in which they are located is discussed. Four spatial objects called reference objects are defined to represent the relative location: (1) the boundary, (2) skeleton, (3) nucleus, and (4) global center. The distribution of distance between points and a reference object yields a cumulative distribution function (CDF). Comparison of CDFs for a reference object allows us to analyze whether the points tend to be located close to the reference object or, for instance, whether the points are clustered around the center of the region. The significance of the CDF is statistically tested by Monte Carlo simulation. The method proposed is applied to the distribution of restaurants in retail clusters.

[1]  J. Brandt Convergence and continuity criteria for discrete approximations of the continuous planar skeleton , 1994 .

[2]  Peter J. Diggle,et al.  Statistical analysis of spatial point patterns , 1983 .

[3]  Yukio Sadahiro Statistical methods for analyzing the distribution of spatial objects in relation to a surface , 1999, J. Geogr. Syst..

[4]  D. T. Lee,et al.  Medial Axis Transformation of a Planar Shape , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  V. Ralph Algazi,et al.  Continuous skeleton computation by Voronoi diagram , 1991, CVGIP Image Underst..

[6]  Don de Savigny,et al.  GIS for health and the environment : proceedings of an international workshop held in Colombo, Sri Lanka, 5-10 Sept. 1994 , 1995 .

[7]  Stephen Brown,et al.  Retail Location: A Micro-Scale Perspective , 1992 .

[8]  L. King,et al.  Statistical Analysis In Geography , 1969 .

[9]  Atsuyuki Okabe,et al.  Qualitative Analysis of Two‐dimensional Urban Population Distributions in Japan , 2010 .

[10]  M. Bartlett The statistical analysis of spatial pattern , 1974, Advances in Applied Probability.

[11]  David Eppstein,et al.  The Crust and the beta-Skeleton: Combinatorial Curve Reconstruction , 1998, Graph. Model. Image Process..

[12]  Andrzej Lingas,et al.  Fast Skeleton Construction , 1995, ESA.

[13]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[14]  Brian D. Ripley,et al.  Spatial Statistics: Ripley/Spatial Statistics , 2005 .

[15]  Herbert Edelsbrunner,et al.  Algorithms in Combinatorial Geometry , 1987, EATCS Monographs in Theoretical Computer Science.

[16]  Gérard G. Medioni,et al.  Hierarchical Decomposition and Axial Shape Description , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Yuk Lee A Nearest-Neighbor Spatial-Association Measure for the Analysis of Firm Interdependence , 1979 .

[18]  Olaf Kübler,et al.  Hierarchic Voronoi skeletons , 1995, Pattern Recognit..

[19]  Atsuyuki Okabe,et al.  The Statistical Analysis through a Computational Method of a Distribution of Points in Relation to its Surrounding Network , 1984 .

[20]  W. Clark,et al.  The Spatial Structure of Retail Functions in a New Zealand City , 1967 .

[21]  A Statistical Method for Analyzing the Spatial Relationship between the Distribution of Activity Points and the Distribution of Activity Continuously Distributed over a Region , 2010 .

[22]  D. T. Lee,et al.  Euclidean shortest paths in the presence of rectilinear barriers , 1984, Networks.

[23]  E. C. Pielou Segregation and Symmetry in Two-Species Populations as Studied by Nearest- Neighbour Relationships , 1961 .

[24]  Method for Describing Spatial Hierarchical Structure of Urban Facilities , 1994 .

[25]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .