The codispersion coefficient quantifies the association between two spatial processes for a particular direction (spatial lag) on a two-dimensional space. When this coefficient is computed for many directions, it is useful to display those values on a single graph. In this article, we suggest a graphical tool called a codispersion map to visualize the spatial correlation between two sequences on a plane. We describe how to construct a codispersion map for regular and non-regular lattices, providing algorithms in both cases. Three numerical examples are given to illustrate how useful this map can be to detect those directions for which the codispersion coefficient attains its maximum and minimum values. We also provide the R code to construct the codispersion map in practice."> The codispersion coefficient quantifies the association between two spatial processes for a particular direction (spatial lag) on a two-dimensional space. When this coefficient is computed for many directions, it is useful to display those values on a single graph. In this article, we suggest a graphical tool called a codispersion map to visualize the spatial correlation between two sequences on a plane. We describe how to construct a codispersion map for regular and non-regular lattices, providing algorithms in both cases. Three numerical examples are given to illustrate how useful this map can be to detect those directions for which the codispersion coefficient attains its maximum and minimum values. We also provide the R code to construct the codispersion map in practice."> The codispersion coefficient quantifies the association between two spatial processes for a particular direction (spatial lag) on a two-di">
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The codispersion map: a graphical tool to visualize the association between two spatial variables

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  • Ronny Vallejos
  • Felipe Osorio
  • Diego Mancilla
Abstract
type="main" xml:id="stan12060-abs-0001"> The codispersion coefficient quantifies the association between two spatial processes for a particular direction (spatial lag) on a two-dimensional space. When this coefficient is computed for many directions, it is useful to display those values on a single graph. In this article, we suggest a graphical tool called a codispersion map to visualize the spatial correlation between two sequences on a plane. We describe how to construct a codispersion map for regular and non-regular lattices, providing algorithms in both cases. Three numerical examples are given to illustrate how useful this map can be to detect those directions for which the codispersion coefficient attains its maximum and minimum values. We also provide the R code to construct the codispersion map in practice.

Suggested Citation

  • Ronny Vallejos & Felipe Osorio & Diego Mancilla, 2015. "The codispersion map: a graphical tool to visualize the association between two spatial variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 298-314, August.
    • Handle: RePEc:bla:stanee:v:69:y:2015:i:3:p:298-314
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    References listed on IDEAS

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    1. Júlia Viladomat & Rahul Mazumder & Alex McInturff & Douglas J. McCauley & Trevor Hastie, 2014. "Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach," Biometrics, The International Biometric Society, vol. 70(2), pages 409-418, June.
    2. Genton, Mark G. & Ruiz-Gazen, Anne, 2009. "Visualizing Influential Observations in Dependent Data," TSE Working Papers 09-051, Toulouse School of Economics (TSE).
    3. Rukhin, Andrew L. & Vallejos, Ronny, 2008. "Codispersion coefficients for spatial and temporal series," Statistics & Probability Letters, Elsevier, vol. 78(11), pages 1290-1300, August.
    4. Ronny Vallejos, 2008. "Assessing the association between two spatial or temporal sequences," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(12), pages 1323-1343.
    5. Francisco Cuevas & Emilio Porcu & Ronny Vallejos, 2013. "Study of spatial relationships between two sets of variables: a nonparametric approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 695-714, September.
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