Hello, sorry for any cross postings - I posted this question yesterday but don't think I posted it in the right space. I am using cokriging to model a continuous variable across my study unit (soil carbon). I have one continuous covariable (soil moisture) but also a categorical covariable (vegetation community). I am using the Geostatistical Analyst Wizard to do this. (I ran linear models in R to establish the significant covariables and any transformations required). Am I right in thinking that I can't include the categorical covariable directly into the model? If so, can anyone please suggest a way that I can include this information within a cokriging model? I am using cokriging as it is enabling me to compare the cross validation statistics with and without the covariables. But open to suggestions of other methods which allow inclusion of covariables and provide some idea of model fit, that I can apply in ArcMap. Many thanks |
Beth,
As you are open to new way, the advice is as follows, which use ordinary Kriging with regression:
pls keep posting your findings and share your experience
Dear Larry,
This is very helpful, thank you.
I assume that regression-kriging will need to be carried out outside of the ArcMap Geostatistical Analyst? In a programme such as R?
I will certainly post my experiencesof trialing this - in case they help others.
Best wishes, Beth
With R, pls refer to 'Regression on categorical variables' at http://www.r-bloggers.com/regression-on-categorical-variables/ and open the attachment, which might be meaningful to your practice...
On your question 'cokriging continuous variable - soil carbon with categorical variable - vegetation', you can try GoCAD (if available to you).
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Generally speaking, geostatistical analysis of categorical variables is by many referred to as the indicator geostatistics. One of those is Regression-kriging of indicators.
With Regression-kriging of indicators, one approach to interpolate vegetation categorical variables is to first assign memberships to point observations and then to interpolate each membership separately. This approach was first elaborated by de Gruijter et al. (1997) and then applied by Bragato (2004) and Triantafilis et al. (2001). An alternative is to first map cheap, yet descriptive, diagnostic distances and then classify these per pixel in a GIS (Carr´e and Girard, 2002).
Dear Larry,
Thank you. I have not come across GoCAD - but will investigate. Is this software I'd need to carry out the rest of your suggestion?
Thanks for clarifying indicator kriging - I assumed it was just used for categorical dependent variables. I will read through your suggested references and see if I can apply this in ArcMap Geostatistical Analyst. I am not very expert at this - so I'm afraid I'll need to do a bit of reading around before I totally understand your second paragraph.
Thanks again, your help is much appreciated. Beth