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tutorial:building_a_land-use_and_land-cover_change_simulation_model [2017/01/27 17:06]
francisco [Second step: Calculating ranges to categorize gray-tone variables]
tutorial:building_a_land-use_and_land-cover_change_simulation_model [2017/10/17 03:20]
admin
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 ==== Second step: Calculating ranges to categorize gray-tone variables ==== ==== Second step: Calculating ranges to categorize gray-tone variables ====
  
-The Weights of Evidence method [[http://​dx.doi.org/​10.1016/​0040-1951(93)90011-8|(Goodacre et al. 1993]] +The Weights of Evidence method [[http://​dx.doi.org/​10.1016/​0040-1951(93)90011-8|(Goodacre et al. 1993]][[https://books.google.com/books?​printsec=frontcover&​vid=ISBN0080424201&​vid=ISBN0080418678&​vid=LCCN94028315#​v=onepage&​q&​f=false|Bonham-Carter 1994)]] is applied in Dinamica EGO to produce a transition probability map (fig. 3), which depicts the most favourable areas for a change [[http://​dx.doi.org/​10.1016/​S0304-3800(02)00059-5|(Soares-Filho et al. 2002]][[http://​dx.doi.org/​ 10.1111/​j.1529-8817.2003.00769.x|,​ 2004)]]. ​
-[[http://www.rc.unesp.br/igce/​geologia/​GAA01048/​papers/​Bonham-Carter_Cap9.pdf|Bonham-Carter1994)]] is applied in Dinamica EGO to produce a transition probability map (fig. 3), which depicts the most favourable areas for a change [[http://​dx.doi.org/​10.1016/​S0304-3800(02)00059-5|(Soares-Filho et al. 2002]][[http://​dx.doi.org/​ 10.1111/​j.1529-8817.2003.00769.x|,​ 2004)]]. ​+
  
 Weights of Evidence consists of a Bayesian method, in which the effect of a spatial variable on a transition is calculated independently of a combined solution. The Weights of Evidence represent each variable’s influence on the spatial probability of a transition i-j and are calculated as follows. Weights of Evidence consists of a Bayesian method, in which the effect of a spatial variable on a transition is calculated independently of a combined solution. The Weights of Evidence represent each variable’s influence on the spatial probability of a transition i-j and are calculated as follows.
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-This model performs pairwise tests for categorical maps in order to test the independence assumption. Methods employed are the Chi^2, ​Crammers, the Contingency,​ the Entropy and the Uncertainty Joint Information [[http://​www.rc.unesp.br/​igce/​geologia/​GAA01048/​papers/​Bonham-Carter_Cap9.pdf|( Bonham-Carter,​ 1994)]]. In addition to the links to be connected, the only parameter to be set in the Determine Weights of Evidence Correlation is the transition as follows:+This model performs pairwise tests for categorical maps in order to test the independence assumption. Methods employed are the Chi^2, ​Cramer, the Contingency,​ the Entropy and the Uncertainty Joint Information [[http://​www.rc.unesp.br/​igce/​geologia/​GAA01048/​papers/​Bonham-Carter_Cap9.pdf|( Bonham-Carter,​ 1994)]]. In addition to the links to be connected, the only parameter to be set in the Determine Weights of Evidence Correlation is the transition as follows:
  
 {{ :​tutorial:​lucc_26.2.jpg |}}\\ {{ :​tutorial:​lucc_26.2.jpg |}}\\
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 ==== Fifth step: Setting up and running a LUCC simulation model ==== ==== Fifth step: Setting up and running a LUCC simulation model ====
  
-Let’s start setting up the deforestation simulation model by loading the input data. You will need //[[:Load Categorical Map]]// to load the initial landscape: ''​original/​23267_1997.ers'',​ //[[:Load Map]]// for ''​originals/​23267statics.ers'',​ //[[:Load Weights]]// for ''​new_weights.dcf'',​ and //[[:Load Lookup Table]]// for the multi-step transition matrix: ''​originals/​multiple_steps.csv''​ because you will run the model in annual time-steps. Add the following comments to each functor:+Let’s start setting up the deforestation simulation model by loading the input data. You will need //[[:Load Categorical Map]]// to load the initial landscape: ''​originals/​23267_1997.ers'',​ //[[:Load Map]]// for ''​originals/​23267statics.ers'',​ //[[:Load Weights]]// for ''​new_weights.dcf'',​ and //[[:Load Lookup Table]]// for the multi-step transition matrix: ''​originals/​multiple_steps.csv''​ because you will run the model in annual time-steps. Add the following comments to each functor:
  
 {{ :​tutorial:​lucc_28.jpg |}} {{ :​tutorial:​lucc_28.jpg |}}