A global spatial autocorrelation measure with data reliability information
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.
The reference for SBC appears in Koo et al. (2019).
- Koo, H., Wong, D. W. S., and Chun, Y., D. A. (2019). Measuring Global Spatial Autocorrelation with Data Reliability Information. The Professional Geographer, 71(3), 551-565.
This code will be replaced by the new version including both global and local SBC with their significance tests.