Spatial Prominence and Spatial Weights Matrix in Geospatial Analysis

  • Changping Zhang


In spatial statistical analyses of geographical phenomena, a region or city under study might be divided into some small areal units such as a regular square tessellation, or into irregular shaped administrative units which have different spatial characteristics. If we are using geographical information science (GIS) to support the analysis, irregular areal units such as cho in Japan are usually represented as one such polygon with geometric attributes. In spatial statistics, an areal unit that has special geometric attributes and maintains significant spatial correlation and spatial interaction close to adjacent units is called a prominent areal unit or an important areal unit. The prominence of areal units can be measured by a prominence or influence-centrality index, which is obtained by using eigenfunctions or the Markov chains method from a spatial weights matrix (Tinkler 1972; Griffith and Jones 1980; Boots 1982; Bavaud 1998; Zhang and Murayama 2003).


Spatial Weight Distance Decay Areal Unit Spatial Weight Matrix Triangulate Irregular Network 
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  1. Anselin L (1988) Spatial econometrics: methods and models. Kluwer, DordrechtGoogle Scholar
  2. Bavaud F (1998) Models for spatial weights: a systematic look. Geogr Anal 30:153–171CrossRefGoogle Scholar
  3. Boots BN (1982) Comments on the use of eigenfunctions to measure structural properties of geographic networks. Environ Plann A 14:1063–1072CrossRefGoogle Scholar
  4. Can A (1996) Weight matrices and spatial autocorrelation statistics using a topological vector data model. Int J Geogr Inform Syst 10:1009–1017Google Scholar
  5. Cliff AD, Ord JK (1969) The problem of spatial autocorrelation. In: Scott AJ (ed) London papers in regional science, vol 1, Studies in regional science. Pion, London, pp 25–55Google Scholar
  6. Cliff AD, Ord JK (1981) Spatial processes: models and applications. Pion, LondonGoogle Scholar
  7. Getis A (2009) Spatial weights matrices. Geogr Anal 41:404–410CrossRefGoogle Scholar
  8. Getis A, Aldstadt J (2004) Constructing the spatial weights matrix using a local statistic. Geogr Anal 36:90–104CrossRefGoogle Scholar
  9. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206CrossRefGoogle Scholar
  10. Griffith DA (1996) Some guidelines for specifying the geographic weights matrix contained in spatial statistical models. In: Arlinghaus SL et al (eds) Practical handbook of spatial statistics. CRC, Boca Raton, pp 65–82Google Scholar
  11. Griffith DA, Jones KG (1980) Explorations of the relationship between spatial structure and spatial interaction. Environ Plann A 12:187–201CrossRefGoogle Scholar
  12. Ord J, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27:286–306CrossRefGoogle Scholar
  13. Tinkler KJ (1972) The physical interpretation of eigenfunctions of dichotomous matrices. Trans Inst Br Geogr 55:17–46CrossRefGoogle Scholar
  14. Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240, SupplementCrossRefGoogle Scholar
  15. Zhang C (1999) Development of a spatial analysis tool for irregular zones using the spatial data framework. Geogr Rev Jpn 72:166–177 (in Japanese with English abstract)Google Scholar
  16. Zhang C, Murayama Y (2000) Testing local spatial autocorrelation using k-order neighbors. Int J Geogr Inform Sci 14:681–692CrossRefGoogle Scholar
  17. Zhang C, Murayama Y (2003) Evaluation on the prominences of irregular areas based on spatial weight matrices. Geogr Rev Jpn 76A:777–787 (in Japanese with English abstract)CrossRefGoogle Scholar

Copyright information

© Springer Japan 2012

Authors and Affiliations

  1. 1.Faculty of Regional Development StudiesToyo UniversityTokyoJapan
  2. 2.Graduate School of GeoscienceUniversity of TsukubaTsukubaJapan

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