Measuring Global Spatial Autocorrelation with Data Reliability Information

Hyeongmo Koo, David W.S. Wong, Yongwan Chun

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular SA statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, one is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation. Key Words: American Community Survey, Geary ratio, Moran’s I, permutation test, spatial Bhattacharyya coefficient.

Original languageEnglish
Pages (from-to)551-565
Number of pages15
JournalProfessional Geographer
Volume71
Issue number3
DOIs
StatePublished - 3 Jul 2019

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