This map is produced by CA-Markov module to predict the future land use map of an study area. Input land use maps were 2004 and 2008 with 9 land use types. 3 of them (housing, commercial and industrial) were with 10 class of probability of growth (created using Weight of evidence method) with class 1 least probability and class 10 highest probability of growth. And other 6 classes just extracted from land use map of 2008 ( as there were). So Markov chain transitional area matrix, basis land use map (land use map of 2008) and raster group file of 9 classes (as explained) were other input files, and asked to project for next 4 years (2012).
Why the result is like this?
Thank you
I'm not expert in CA-Markov, so let me first ask you whether you scan the image line by line to build your observation. If so, you may be should consider Markov fields or something like Hilbert-Peano scan.
I do not know anything about IDRISI software. Is it related to Markov Chains at all?
I have no idea if IDRISI has any relation to Markov Chains.
However, I have some papers on Markov chains for categorical variables I do not know if it can help, see the links below:
https://www.researchgate.net/profile/Amro_Elfeki/publications?sorting=newest&pubType=article&page=2
https://www.researchgate.net/publication/226498636_A_Markov_Chain_Model_for_Subsurface_Characterization_Theory_and_Applications
Article A Markov Chain Model for Subsurface Characterization: Theory...
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