Quantifying resolution sensitivity of spatial autocorrelation: A resolution correlogram approach

Pradeep Mohan, Xun Zhou, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationGeographic Information Science - 7th International Conference, GIScience 2012, Proceedings
Pages132-145
Number of pages14
DOIs
StatePublished - Oct 23 2012
Event7th International Conference on Geographic Information Science, GIScience 2012 - Columbus, OH, United States
Duration: Sep 18 2012Sep 21 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7478 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Geographic Information Science, GIScience 2012
CountryUnited States
CityColumbus, OH
Period9/18/129/21/12

Keywords

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

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  • Cite this

    Mohan, P., Zhou, X., & Shekhar, S. (2012). Quantifying resolution sensitivity of spatial autocorrelation: A resolution correlogram approach. In Geographic Information Science - 7th International Conference, GIScience 2012, Proceedings (pp. 132-145). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7478 LNCS). https://doi.org/10.1007/978-3-642-33024-7_10