Spatial autocorrelation can affect data analysis by creating patterns at different scales. To address this, researchers used the regionalized range to identify sub-regions with minimized large-scale autocorrelation impact. They applied this method to crime data in San Antonio, Texas, and found that it is crucial to consider non-stationarity of large-scale autocorrelation before detecting local patterns like crime hot spots and cold spots.