  We introduce a method called neighbor-based bootstrapping (NB2) that can be
used to quantify the geospatial variation of a variable. We applied this method
to an analysis of the incidence rates of disease from electronic medical record
data (ICD-9 codes) for approximately 100 million individuals in the US over a
period of 8 years. We considered the incidence rate of disease in each county
and its geospatially contiguous neighbors and rank ordered diseases in terms of
their degree of geospatial variation as quantified by the NB2 method.
  We show that this method yields results in good agreement with established
methods for detecting spatial autocorrelation (Moran's I method and kriging).
Moreover, the NB2 method can be tuned to identify both large area and small
area geospatial variations. This method also applies more generally in any
parameter space that can be partitioned to consist of regions and their
neighbors.
