Why Spatial Autocorrelation Matters
{"Image DocumentName": "Why Spatial Autocorrelation Matters", "Image ImageDescription": "Spatial autocorrelation is of interest in its own right because it suggests the operation of a spatial process. Additionally, most statistical analyses are based on the assumption that the values of observations in each sample are independent of one another. Positive spatial autocorrelation violates this, because samples taken from nearby areas are related to each other and are not independent. In ordinary least squares regression (OLS), for example, the correlation coefficients will be biased and their precision exaggerated. Bias implies correlation coefficients may be higher than they really are. They are biased because the areas with higher concentrations of events will have a greater impact on the model estimate. Exaggerated precision (lower standard error) implies they are more likely to be found statistically significant they will overestimate precision because, since events tend to be concentrated, there are actually a fewer number of independent observations than is being assumed. Briggs UT-Dallas GISC 6382 Spring 2007.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}