Reducing or removing autocorrelation in spatially correlated data

1. Why is spatial autocorrelation a problem?
2. How do you address spatial autocorrelation?
3. What is spatial autocorrelation between two variables?
4. What is spatial correlation Analysis?

Why is spatial autocorrelation a problem?

If spatial autocorrelation is present it will violate the assumption about the independence of residuals and call into question the validity of hypothesis testing. The main effect of such violations is that the Error SS (Sum of Squares) is underestimated (Davis, 1986 ) thus inflating the value of test statistic.

How do you address spatial autocorrelation?

In linear models of normally distributed data, spatial autocorrelation can be addressed by the related ap- proaches of generalised least squares (GLS) and auto- regressive models (conditional autoregressive models (CAR) and simultaneous autoregressive models (SAR)).

What is spatial autocorrelation between two variables?

The presence of spatial autocorrelation in both or either of two variables under investigation (i.e., bivariate spatial dependence) means that when the nature of a bivariate association at a location is known, one can guess the nature of bivariate associations at nearby locations.

What is spatial correlation Analysis?

Spatial correlation means that there is a correlation between the received average signal gain and the angle of arrival of a signal. Rich multipath propagation decreases the spatial correlation by spreading the signal such that multipath components are received from many different spatial directions.