How to do interpolation of missing data in grid cells?

Question

Hi.

I have created grid cells over Liberia on a shapefile. Then added data from a household survey, and joined the georeferencing data based on spatial location. The trouble is, that only around 100 of 1.300 cells are represented with data, the rest has the value 0.

How can I do an interpolation that fills the missing values in my attribute table? I would prefer an inverse distance weight interpolation in the geostatistical wizard that gives me the predicted values. But the huge amount of zeroes are too biased, and when I change the zeroes to <Null>, they are not included in the interpolation.

What am I doing wrong?

Thank you!

Frederik

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DanPatterson_Retired · Answer

Steve has given a suggestion which could be explored if you also take a look at whether there exists a correlation between the results of the previous study and your new variable

SteveLynch · Answer

If the zero values are changed to nulls they will be ignored, that is by design.

The pattern of your points tell me that you'll need covariates (explanatory) variables. Cokring or better still EBKRegressionKriging in Pro 1.2+

FrederikMadsen · Answer

Thank you for reply.

Yes, it's a pre-chosen fishnet with data, size, shape and alignment. It's a shapefile from another study that I'm trying to replicate, just with new data. I was thinking just to use that, and join the new data from another layer based on spatial location. If it would change anything, I could create my own fishnet, and not steal from the study, but dont know if it would solve the problem of not all cells being represented.

Citation from the study

"A potential challenge to our study is that the DHS surveys are only representative for that location and cannot inform us about nonsampled locations. We present below our method to interpolate measurements between locations. This method must necessarily underestimate variability within locations, and we do not see any obvious source of systematic bias. If so, our imperfect data should attenuate our results, leading to nonsignificant findings...() we interpolate data on wealth levels using a method designed for spatial data in GIS. The “Inverse Distance Weighted” (IDW) method".