  Comparing spatial data sets is a ubiquitous task in data analysis, however
the presence of spatial autocorrelation means that standard estimates of
variance will be wrong and tend to over-estimate the statistical significance
of correlations and other observations. While there are a number of existing
approaches to this problem, none are ideal, requiring detailed analytical
calculations, which are hard to generalise or detailed knowledge of the data
generating process, which may not be available. In this work we propose a
resampling approach based on Tobler's Law. By resampling the data with fixed
spatial autocorrelation, measured by Moran's I, we generate a more realistic
null model. Testing on real and synthetic data, we find that, as long as the
spatial autocorrelation is not too strong, this approach works just as well as
if we knew the data generating process.
