Dasymetric disaggregation is an established approach for generating small-area population estimates while initially having that information on broader areas. It incorporates ancillary data to facilitate the areal interpolation process. A lot of work is done in the area of spatial population modelling. I wonder why I didn't found anything in R on it. Does anybody know if methods are available in R?
I've only read a little about small-area population estimates (Coulombe, 2009) and the interpolation was based on a linear model. This wasn't count data, but poverty. My spatial statistics professor, Daniel Griffith and Y. Chun (2013) recently published a book that includes a chapter on spatial interpolation using the spdep and gstat library in R. This covers kriging, co-kriging, EM, and spatial filtering. They developed an experimental spatial filtering method with eigenvectors that can be used with a Poisson regression. Perhaps you could adapt these for your small-area estimates.
I think you can try using the R function disaggregate. Hope it helps!
Here you can find both the methodology and the R code to implement it:
http://gsars.org/wp-content/uploads/2015/09/TR-Spatial-Disaggregation-and-Small-Area-Estimation-210915.pdf
may be you need to change your question. there are two difference disaggregation which is mean ,temporal and space disaggregation.
In my view to do disaggregation in both approach, we need some proxcy indicators which are show the behavior of latent variables
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