[ai-geostats] kriging proportions
Hi everyone, Lets say we have measured three soil particule size values for clay, silt and sand, all adding to one. cl + s i + sa = 1 What would be the best way to take into account each particule size, so the interpolated values still add up to one? Is there any geostatistical process that can handle this? I have tried interpolating parameters caracterising different particle size distribution functions (in the case where there are more than 10 particule sizes) but this adds errors to the modelling and some parameters don't necessarily exhibit spatial correlation. Maximum autocorrelation factor kriging has been suggested such as in: A. J. Desbarats and R. Dimitrakopoulos. Geostatistical Simulation of Regionalized Pore-Size Distributions Using Min/Max Autocorrelation Factors. Mathematical Geology, Vol. 32, No. 8, 2000. but I haven't found many statistical packages implementig this procedure. What other possibilities are there? Best regards, Marc-Olivier * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] kriging proportions
Hi Marc, You may want to look at the following paper: de Gruijter, J.J., Walvoort, D.J.J., van Gaans, P.F.M., 1997. Continuous soil maps --- a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models. Geoderma 77, 169--195. The authors describe compositional kriging to interpolate class memberships, and they have incorporated additional constraints into the kriging system to ensure that all estimates are positive and add up to a constant (1 in this case). Cheers, Pierre Goovaerts Dr. Pierre Goovaerts President of PGeostat, LLC Chief Scientist with Biomedware Inc. 710 Ridgemont Lane Ann Arbor, Michigan, 48103-1535, U.S.A. E-mail: [EMAIL PROTECTED] Phone: (734) 668-9900 Fax: (734) 668-7788 http://alumni.engin.umich.edu/~goovaert/ On Thu, 10 Jun 2004, Marc-Olivier Gasser wrote: Hi everyone, Lets say we have measured three soil particule size values for clay, silt and sand, all adding to one. cl + s i + sa = 1 What would be the best way to take into account each particule size, so the interpolated values still add up to one? Is there any geostatistical process that can handle this? I have tried interpolating parameters caracterising different particle size distribution functions (in the case where there are more than 10 particule sizes) but this adds errors to the modelling and some parameters don't necessarily exhibit spatial correlation. Maximum autocorrelation factor kriging has been suggested such as in: A. J. Desbarats and R. Dimitrakopoulos. Geostatistical Simulation of Regionalized Pore-Size Distributions Using Min/Max Autocorrelation Factors. Mathematical Geology, Vol. 32, No. 8, 2000. but I haven't found many statistical packages implementig this procedure. What other possibilities are there? Best regards, Marc-Olivier * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
[ai-geostats] Re: kriging proportions
Marc-Olivier The simplest solution - in the sense that most packages could handle it - is to carry out a 'nested' indicator analysis. That is: (i) code one of your particle classes as '1' and all other as '0', produce a map of proportion of this class. (ii) remove this particle class from your data. Code the next class as 1 and all others as 0. produce a map of the proportion of this class, given that it is not in the first class. The 'actual' proportion is then P(ii)*(1-P(i)). (iii) If you have more than three classes, you can keep nesting although you tend to run out of data pretty fast. The last class has whatever proportion is left. Proportions such as this which have to add up to 1 or 100% have been the subject of a lot of study, particularly by people such as Vera Pawlowsky-Glahn under the title 'compositional data'. Isobel http://geoecosse.bizland.com/BYOGeostats.htm ___ALL-NEW Yahoo! Messenger - so many all-new ways to express yourself http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] kriging proportions
Marc-Olivier, You can try our recent publication in the Journal Soil Science which compared several geostatistical techniques for simultaneously interpolating soil particle-size fractions while ensuring summation to a constant. The reference is: Odeh IOA Todd AJ and Triantafilis J 2003. Spatial prediction of particle size fractions as compositional data. Soil Science 168, 501-515. Odeh At 01:01 PM 10/06/2004 -0400, Marc-Olivier Gasser wrote: Hi everyone, Lets say we have measured three soil particule size values for clay, silt and sand, all adding to one. cl + s i + sa = 1 What would be the best way to take into account each particule size, so the interpolated values still add up to one? Is there any geostatistical process that can handle this? I have tried interpolating parameters caracterising different particle size distribution functions (in the case where there are more than 10 particule sizes) but this adds errors to the modelling and some parameters don't necessarily exhibit spatial correlation. Maximum autocorrelation factor kriging has been suggested such as in: A. J. Desbarats and R. Dimitrakopoulos. Geostatistical Simulation of Regionalized Pore-Size Distributions Using Min/Max Autocorrelation Factors. Mathematical Geology, Vol. 32, No. 8, 2000. but I haven't found many statistical packages implementig this procedure. What other possibilities are there? Best regards, Marc-Olivier * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
