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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Anselin, L.: Under the hood issues in the specification and interpretation of spatial regression models. Agricultural Economics 27(3), 247–267 (2002)
Atkinson, P.M., Tate, N.J.: Spatial scale problems and geostatistical solutions: A review. The Professional Geographer 52(4), 607–623 (2000)
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)
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)
Ebdon, D.: Statistics in geography. Blackwell Publisher (1985)
Fischer, M., Getis, A.: Handbook of applied spatial analysis: software tools, methods and applications. Springer (2010)
Foufoula-Georgiou, E., Kumar, P.: Wavelets in geophysics. Wavelet analysis and its applications, vol. 4. Academic Press (1994)
Geary, R.C.: The contiguity ratio and statistical mapping. The Incorporated Statistician 5(3), 115–127, 129–146 (1954)
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)
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)
Moran, P.A.P.: The interpretation of statistical maps. Journal of the Royal Statistical Society. Series B (Methodological) 10(2), 243–251 (1948)
Overmars, K., de Koning, G., Veldkamp, A.: Spatial autocorrelation in multi-scale land use models. Ecological Modelling 164(2-3), 257–270 (2003)
Page, E.: Continuous inspection schemes. Biometrika 41(1-2), 100–115 (1954)
Quattrochi, D., Goodchild, M.: Scale in remote sensing and GIS. Mapping Sciences Series. Lewis Publishers (1997)
Shekhar, S., Xiong, H.: Encyclopedia of GIS. Springer Reference. Springer (2008)
Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Economic Geography 46, 234–240 (1970)
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)
Willsky, A.: Multiresolution markov models for signal and image processing. Proceedings of the IEEE 90(8), 1396–1458 (2002)
Woodcock, C.E., Strahler, A.H.: The factor of scale in remote sensing. Remote Sens. Environ. 21(3), 311–332 (1987)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohan, P., Zhou, X., Shekhar, S. (2012). Quantifying Resolution Sensitivity of Spatial Autocorrelation: A Resolution Correlogram Approach. In: Xiao, N., Kwan, MP., Goodchild, M.F., Shekhar, S. (eds) Geographic Information Science. GIScience 2012. Lecture Notes in Computer Science, vol 7478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33024-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-33024-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33023-0
Online ISBN: 978-3-642-33024-7
eBook Packages: Computer ScienceComputer Science (R0)