[R-sig-Geo] Imputation of aggregate data for spatial analysis

[Roger Bivand]

On Mon, 19 Oct 2020, Amitha Puranik wrote:

> Hello everyone!
>
> I have data on the proportion of screening of cervical cancer at
> district-level. I have about 530 data points out of the 545 districts. I
> want to get a complete picture of the data in order to obtain the spatial
> cluster map using LISA (local indicators of partial autocorrelation).
>
> Would it be alright if I impute the missing values of the proportion for
> the 15 districts using multiple imputation?

Please do not even consider imputation of this kind of data, especially if 
LISA is your tools. It makes no sense at all.

If, however, you model the screening proportions at district level, and 
have a model that fits well using multiple well-observed covariates, both 
INLA and other model fitting approaches can predict for unobserved 
responses. However, in such cases, the model will have used the spatial 
autocorrelation already (for example in a BYM model, see for example here: 
http://www.paulamoraga.com/book-geospatial-info/), so putting the 
posterior distributions at unobserved locations into a LISA makes no 
sense, because the imputation alrready uses spatial autocorrelation. 
Modelling, if you have relevant covariates, and model preferably using 
Poisson reression offset by log size of age-selected female population, 
makes much more sense than LISA.

Hope this helps,

Roger

>
> Or is there a better way for imputation in this case?
>
> Any suggestion/ comment is highly appreciated!
>
> Regards,
> Amitha
>
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>
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-- 
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
https://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en