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--- tutorial:redd_case_study [2013/07/30 01:12]
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+++ tutorial:redd_case_study [2013/07/30 01:39]
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 === Developing an econometric projection model that predicts deforestation rates based on changes in the socioeconomic context of municipalities ===
-In this example, an econometric model is coupled to a spatially-explicit simulation model of deforestation. The econometric projection model predicts deforestation rates based on changes in the socioeconomic context of municipalities (Soares-Filho et. al, 2008, Soares-Filho et. al, 2010). A spatial lag regression is applied to compute the influence of five variables on the deforestation trajectory:  Crop area expansion, cattle herd growth, percent of protected areas, proximity to paved roads, and migration rates. A spatial neighborhood matrix allows the model to incorporate the influence of the socioeconomic context of neighboring municipalities in the prediction of deforestation rates within a certain municipality.
+In this example, an econometric model is coupled to a spatially-explicit simulation model of deforestation. The econometric projection model predicts deforestation rates based on changes in the socioeconomic context of municipalities [[http://www.csr.ufmg.br/dinamica/publications/cap6.pdf|(Soares-Filho et. al, 2008]][[http://www.pnas.org/cgi/doi/10.1073/pnas.0913048107|,Soares-Filho et. al, 2010)]]. A spatial lag regression is applied to compute the influence of five variables on the deforestation trajectory:  Crop area expansion, cattle herd growth, percent of protected areas, proximity to paved roads, and migration rates. A spatial neighborhood matrix allows the model to incorporate the influence of the socioeconomic context of neighboring municipalities in the prediction of deforestation rates within a certain municipality.
 Load the model “simulate_deforestation_under_socioeconomic_scenarios.xml” from \ Examples\REDD_case_study. This model is composed of three main parts: the input data, pre-calculation, and the simulation model itself.
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-In this simplified version of Soares-Filho et al. (2008), the user can modify the scenario by changing the annual rates of crop expansion and cattle herd growth, which are input to the model. Other variables could be also changed by editing the input lookup tables.
+In this simplified version of [[http://www.csr.ufmg.br/dinamica/publications/cap6.pdf|Soares-Filho et al. (2008)]], the user can modify the scenario by changing the annual rates of crop expansion and cattle herd growth, which are input to the model. Other variables could be also changed by editing the input lookup tables.
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 <note tip>**TIP**: //For Each// browses the elements of a table allowing its manipulation.</note>
-In addition to the lookup tables of the five independent variables, //Calc Spatial Lag// receives as input the lag coefficient, the neighborhood matrix, an initial x1 dependent variable table, the regression coefficients, and a random error term. This functor represents a spatial lag regression equation as follows (Anselin, 2002):
+In addition to the lookup tables of the five independent variables, //Calc Spatial Lag// receives as input the lag coefficient, the neighborhood matrix, an initial x1 dependent variable table, the regression coefficients, and a random error term. This functor represents a spatial lag regression equation as follows [[http://dx.doi.org/10.1177/0160017602250972|(Anselin, 2002)]]:
 y = pWy+XB+e
-Where "p" is the autoregressive coefficient, "W" is a first order neighborhood matrix, "y" the dependent variable, "X" the matrix of observations for the independent variables, "B"  the vector of regression coefficients and "e" a random error term. In this equation the term "pW" is calculated in an iterative way using moving averages of the "y" responses from the neighboring municipalities. In this case, instead of a classical linear model, a spatial lag regression was adopted since the regression model failed the autocorrelation tests (Anselin, 2002).
+Where "p" is the autoregressive coefficient, "W" is a first order neighborhood matrix, "y" the dependent variable, "X" the matrix of observations for the independent variables, "B"  the vector of regression coefficients and "e" a random error term. In this equation the term "pW" is calculated in an iterative way using moving averages of the "y" responses from the neighboring municipalities. In this case, instead of a classical linear model, a spatial lag regression was adopted since the regression model failed the autocorrelation tests [[http://dx.doi.org/10.1177/0160017602250972|(Anselin, 2002)]].
 Dinamica EGO does not provide a method to develop a spatial lag regression, but only to solve the equation, which was developed using Geoda (www.geoda.uiuc.edu).
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 === Developing a carbon bookkeeping model ===
-This model calculates annual carbon emissions by identifying annual deforestation and then overlaying these areas on a map of forest carbon biomass – figure below (Saatchi et al., 2007), and assuming that carbon content is 50% of wood biomass (Houghton et al., 2001) and that 85% of the carbon contained in trees is released to the atmosphere with deforestation (Houghton et al., 2000).
+This model calculates annual carbon emissions by identifying annual deforestation and then overlaying these areas on a map of forest carbon biomass – figure below [[http://dx.doi.org/10.1111/j.1365-2486.2007.01323.x|(Saatchi et al., 2007)]], and assuming that carbon content is 50% of wood biomass [[http://dx.doi.org/10.1111/j.1365-2486.2001.00426.x|(Houghton et al., 2001)]] and that 85% of the carbon contained in trees is released to the atmosphere with deforestation [[http://dx.doi.org/10.1038/35002062|(Houghton et al., 2000)]].
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