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Measuring global spatial autocorrelation with data reliability information.

Hyeongmo Koo1, David W S Wong2, Yongwan Chun3

  • 1School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, China, hmo.koo@gmail.com.

The Professional Geographer : the Journal of the Association of American Geographers
|December 3, 2019
PubMed
Summary
This summary is machine-generated.

Traditional spatial autocorrelation (SA) statistics overestimate spatial patterns when estimate errors are ignored. The new Spatial Bhattacharyya coefficient (SBC) accurately assesses SA by including estimate reliability, offering a robust alternative.

Keywords:
American Community SurveyGeary RatioMoran’s ISpatial Bhattacharyya coefficientpermutation test

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Area of Science:

  • Spatial statistics
  • Geographic Information Systems (GIS)
  • Geostatistics

Background:

  • Spatial autocorrelation (SA) analysis is crucial in spatial statistics.
  • Existing SA statistics often disregard the reliability or error associated with statistical estimates.
  • This oversight can lead to inaccurate assessments of spatial patterns.

Purpose of the Study:

  • To investigate the impact of estimate errors on spatial autocorrelation assessments.
  • To propose a novel statistic that accounts for estimate reliability.
  • To introduce a more accurate method for evaluating spatial autocorrelation in the presence of error.

Main Methods:

  • Empirical and simulated data were used to demonstrate the overestimation of SA by traditional methods.
  • The concept of the Bhattacharyya coefficient was adapted to develop the Spatial Bhattacharyya coefficient (SBC).
  • A permutation test was employed to assess the statistical significance of the SBC.

Main Results:

  • Traditional SA statistics tend to overestimate spatial autocorrelation when estimate errors are not considered.
  • The proposed Spatial Bhattacharyya coefficient (SBC) effectively incorporates errors of estimates.
  • The SBC demonstrated a more accurate and robust reflection of SA magnitude compared to traditional measures.

Conclusions:

  • The SBC provides a more reliable evaluation of spatial autocorrelation for estimates with associated errors.
  • Incorporating estimate reliability enhances the accuracy of spatial pattern assessment.
  • The SBC represents a significant advancement in spatial statistical analysis for error-prone data.