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Quantifying Resolution Sensitivity of Spatial Autocorrelation: A Resolution Correlogram Approach

  • Pradeep Mohan
  • Xun Zhou
  • Shashi Shekhar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7478)

Abstract

Raster spatial datasets are often analyzed at multiple spatial resolutions to understand natural phenomena such as global climate and land cover patterns. Given such datasets, a collection of user defined resolutions and a neighborhood definition, resolution sensitivity analysis (RSA) quantifies the sensitivity of spatial autocorrelation across different resolutions. RSA is important due to applications such as land cover assessment where it may help to identify appropriate aggregations levels to detect patch sizes of different land cover types. However, Quantifying resolution sensitivity of spatial autocorrelation is challenging for two important reasons: (a) absence of a multi-resolution definition for spatial autocorrelation and (b) possible non-monotone sensitivity of spatial autocorrelation across resolutions. Existing work in spatial analysis (e.g. distance based correlograms) focuses on purely graphical methods and analyzes the distance-sensitivity of spatial autocorrelation. In contrast, this paper explores quantitative methods in addition to graphical methods for RSA. Specifically, we formalize the notion of resolution correlograms(RCs) and present new tools for RSA, namely, rapid change resolution (RCR) detection and stable resolution interval (SRI) detection. We propose a new RSA algorithm that computes RCs, discovers interesting RCRs and SRIs. A case study using a vegetation cover dataset from Africa demonstrates the real world applicability of the proposed algorithm.

Keywords

Resolution correlograms descriptive correlogram statistics rapid change resolution stable resolution intervals resolution sensitivity analysis 

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References

  1. 1.
    Anselin, L.: Under the hood issues in the specification and interpretation of spatial regression models. Agricultural Economics 27(3), 247–267 (2002)CrossRefGoogle Scholar
  2. 2.
    Atkinson, P.M., Tate, N.J.: Spatial scale problems and geostatistical solutions: A review. The Professional Geographer 52(4), 607–623 (2000)CrossRefGoogle Scholar
  3. 3.
    de Koning, G., Veldkamp, A., Fresco, L.: Land use in ecuador: a statistical analysis at different aggregation levels. Agriculture, Ecosystems and Environment 70(2-3), 231–247 (1998)CrossRefGoogle Scholar
  4. 4.
    de Koning, G., Verburg, P., Veldkamp, A., Fresco, L.: Multi-scale modelling of land use change dynamics in ecuador. Agricultural Systems 61(2), 77–93 (1999)CrossRefGoogle Scholar
  5. 5.
    Ebdon, D.: Statistics in geography. Blackwell Publisher (1985)Google Scholar
  6. 6.
    Fischer, M., Getis, A.: Handbook of applied spatial analysis: software tools, methods and applications. Springer (2010)Google Scholar
  7. 7.
    Foufoula-Georgiou, E., Kumar, P.: Wavelets in geophysics. Wavelet analysis and its applications, vol. 4. Academic Press (1994)Google Scholar
  8. 8.
    Geary, R.C.: The contiguity ratio and statistical mapping. The Incorporated Statistician 5(3), 115–127, 129–146 (1954)CrossRefGoogle Scholar
  9. 9.
    Goodchild, M.F.: The validity and usefulness of laws in geographic information science and geography. Annals of the Association of American Geographers 94(2), 300–303 (2004)CrossRefGoogle Scholar
  10. 10.
    Ju, J., Gopal, S., Kolaczyk, E.: On the choice of spatial and categorical scale in remote sensing land cover classification. Remote Sensing of Environment 96(1), 62–77 (2005)CrossRefGoogle Scholar
  11. 11.
    Moran, P.A.P.: The interpretation of statistical maps. Journal of the Royal Statistical Society. Series B (Methodological) 10(2), 243–251 (1948)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Overmars, K., de Koning, G., Veldkamp, A.: Spatial autocorrelation in multi-scale land use models. Ecological Modelling 164(2-3), 257–270 (2003)CrossRefGoogle Scholar
  13. 13.
    Page, E.: Continuous inspection schemes. Biometrika 41(1-2), 100–115 (1954)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Quattrochi, D., Goodchild, M.: Scale in remote sensing and GIS. Mapping Sciences Series. Lewis Publishers (1997)Google Scholar
  15. 15.
    Shekhar, S., Xiong, H.: Encyclopedia of GIS. Springer Reference. Springer (2008)Google Scholar
  16. 16.
    Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Economic Geography 46, 234–240 (1970)CrossRefGoogle Scholar
  17. 17.
    Tucker, C., Pinzon, J., Brown, M.: Global inventory modeling and mapping studies (gimms) satellite drift corrected and noaa-16 incorporated normalized difference vegetation index (ndvi), monthly 1981-2002. Global Land Cover Facility, University of Maryland (2004)Google Scholar
  18. 18.
    Willsky, A.: Multiresolution markov models for signal and image processing. Proceedings of the IEEE 90(8), 1396–1458 (2002)CrossRefGoogle Scholar
  19. 19.
    Woodcock, C.E., Strahler, A.H.: The factor of scale in remote sensing. Remote Sens. Environ. 21(3), 311–332 (1987)CrossRefGoogle Scholar
  20. 20.
    Zhou, X., Shekhar, S., Mohan, P., Liess, S., Snyder, P.K.: Discovering interesting sub-paths in spatiotemporal datasets: a summary of results. In: GIS, pp. 44–53 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pradeep Mohan
    • 1
  • Xun Zhou
    • 1
  • Shashi Shekhar
    • 1
  1. 1.University of MinnesotaMinneapolisUSA

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