Measuring Global Spatial Autocorrelation with Data Reliability Information

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dc.contributor.authorKoo, Hyeongmo
dc.contributor.authorWong, D. W. S.
dc.contributor.authorChun, Yongwan
dc.contributor.utdAuthorKoo, Hyeongmo
dc.contributor.utdAuthorChun, Yongwan
dc.date.accessioned2019-11-18T21:34:12Z
dc.date.available2019-11-18T21:34:12Z
dc.date.created2019-03-29
dc.description.abstractAssessing 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.
dc.description.departmentSchool of Economic, Political and Policy Studies
dc.description.sponsorshipThis research was supported by the National Institutes of Health, Grant 1R01HD076020‐01A1.
dc.identifier.bibliographicCitationKoo, H., D. W. S. Wong, and Y. Chun. 2019. "Measuring Global Spatial Autocorrelation with Data Reliability Information." The Professional Geographer 71(3): 551-565, doi: 10.1080/00330124.2018.1559652
dc.identifier.issn0033-0124
dc.identifier.issue3
dc.identifier.urihttps://hdl.handle.net/10735.1/7113
dc.identifier.volume71
dc.language.isoen
dc.publisherRoutledge
dc.relation.urihttps://dx.doi.org/10.1080/00330124.2018.1559652
dc.rights©2019 American Association of Geographers.
dc.source.journalThe Professional Geographer
dc.subjectAmerican Community Survey
dc.subjectAutocorrelation (Statistics)
dc.subjectSpatial analysis (Statistics)
dc.titleMeasuring Global Spatial Autocorrelation with Data Reliability Information
dc.type.genrearticle

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