AI-GEOSTATS Hi Ashton, Sequential Indicator simulation (SIS) is based on the local estimation (i.e. kriging) of the probabilities of occurrence of each of the 7 categories, in your case. Thus, the local mean refers to the local (a priori) probability of occurrence of each of the seven classes based on the calibration of your "map". The vectors of local means correspond to the row of your confusion, or error matrix. At the locations of ground-thruthed data, you have non only this vector of local means but also a vector of indicators of occurrence which should include 6 zeros and a one for the category that is observed on the ground. Indicator residuals are computed by subtracting these two vectors. SIS with varying local means is implemented in Gslib program sisim. Cheers, Pierre Pierre Goovaerts Chief Scientist at BioMedware Inc. Courtesy Associate Professor, University of Florida President of PGeostat LLC Office address: 516 North State Street Ann Arbor, MI 48104 Voice: (734) 913-1098 (ext. 8) Fax: (734) 913-2201 http://home.comcast.net/~goovaerts/
________________________________ From: [EMAIL PROTECTED] on behalf of Ashton Shortridge Sent: Thu 5/25/2006 10:24 AM To: [email protected] Subject: AI-GEOSTATS: multicategory indicator simulation AI-GEOSTATS Hello all, I have a land cover dataset with codes 1-7 representing different land cover categories. This data is not too good, but might be better than nothing. Let's call this the map. I have a second dataset - a bunch of point locations at which land cover for the area has been ground-truthed. This is essentially my reference data. I can use these things to construct a confusion, or error matrix, like this: [1,] 0.11111111 0.02222222 0.00000000 0.8666667 0.00000000 0.000 0.00000000 [2,] 0.07777778 0.18888889 0.00000000 0.7222222 0.00000000 0.000 0.01111111 [3,] 0.00000000 0.24166667 0.49166667 0.2500000 0.01666667 0.000 0.00000000 [4,] 0.03333333 0.26666667 0.06666667 0.6333333 0.00000000 0.000 0.00000000 [5,] 0.00000000 0.75000000 0.00000000 0.1250000 0.00000000 0.125 0.00000000 [6,] 0.00000000 0.00000000 0.90000000 0.0000000 0.10000000 0.000 0.00000000 [7,] 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000 0.000 1.00000000 where cell i,j corresponds to the observed probability of observing class j on the ground, where class i was present in the map. For example, a cell with class 3 on the map is actually class 3 about 49% of the time. About 24% of the time it's class 2, and 25% of the time it is class 4. Very rarely (1.7%) it's actually class 5. I would like to employ indicator simulation on this data using simple kriging with locally varying means. I want to generate realizations of reference land cover, using the map landcover data to improve the prediction by serving as the mean estimate. This approach is documented in Goovaerts' book and in a paper by Kyriakidis and Dungan (2001). However, several points are unclear to me. First, simple kriging is employed on residuals from the mean. For multicategorical data of the sort I am investigating here, how would one calculate the mean at a particular location? Second and more practically, I've struggled to discover how to implement this in gstat (R version or standalone), and am wondering if anyone has had success with another software package. Thanks in advance for any assistance you can provide. Ashton -- Ashton Shortridge Assistant Professor [EMAIL PROTECTED] Dept of Geography http://www.msu.edu/~ashton 235 Geography Building ph (517) 432-3561 Michigan State University fx (517) 432-1671 Geography Has moved! Map: http://www.rsgis.msu.edu/images/parking-map.gif + To post a message to the list, send it to [email protected] + To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ + To post a message to the list, send it to [email protected] + To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
