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Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data
Author(s) -
Jung Paul H.,
Thill JeanClaude,
Issel Michele
Publication year - 2019
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12177
Subject(s) - statistics , heteroscedasticity , spatial analysis , estimator , small area estimation , census , geography , autocorrelation , econometrics , bayes' theorem , sampling (signal processing) , mathematics , demography , bayesian probability , population , computer science , sociology , filter (signal processing) , computer vision
We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.