I currently have a shape file with approximately 20 numeric variables. Several of these variables have missing values.

As this is a shapefile I do not think using the median or mean as a form of imputation would be effective.

Is there an approach I can take which would look at the specific polygon with a missing value, and impute that polygon with the mean/median of the surrounding polygons?

I have found the following paper:

Baker et al.: Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes. International Journal of Health Geographics 2014 13:47

which mentions a CAR prior distribution technique- but I am unsure how to implement this technique.

Is there a simplified approach to this spatial imputation technique in r?


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