Model Validation 1 MLR Model using All Significant Variables 1

75 parameter estimates produced in MLR model analysis and the actual spatial data layers in 2005 into MLR model equation Equation 1 and 2. The parameter estimates in logistic regression contain the coefficients of the parametersvariables β included in the final model, and it summarizes the effect of each parameter. The spatial data layers in 2005 see Appendix 4 which would be functioned as parametersvariables x in the MLR model equation have been prepared by the same procedures when preparing the spatial data layers in 2002 for MLR model analysis. Table 18. MLR Model Equation: Logit Functions and Conditional Probability of Each Land Use Transition Equation 1. Logit Functions Equation 2. Conditional Probability of Land Use Transition The coefficient of the parameters β i in Parameter Estimates and spatial data layersparameters 2005 x i have been simulated on MLR model equation by using raster calculator, so it would produce the conditional probability maps of land use transitions during 2005 – 2008. Two steps of computations have been done in order to simplify the conditional probability maps simulations that were Logit Functions Equation 1 and Conditional Probability of Land Use Transition Equation 2. The computation of coefficient of the parameters β i and spatial data layersparameters 2005 x i on MLR model equation produced 26 conditional probability maps of outcome categories in accordance with number of land use transitions in Siak District. These conditional probability maps show the probability of each land use transition may occur in the research area, with 76 probability value range from 0 to 1. The conditional probability maps of land use transitions 2005 – 2008 which have been produced are shown in Figure 28. Probability Map for FF PY01 Probability Map for FC PY02 Probability Map for FG PY03 Probability Map for FS PY04 Probability Map for FO PY05 Probability Map for CF PY06 Probability Map for CC PY07 Probability Map for CG PY08 Probability Map for CS PY09 Probability Map for CO PY10 Probability Map for GF PY011 Probability Map for GC PY12 Figure 28. Conditional Probability Maps of Land Use Transitions 1 st Scenario 77 Probability Map for GG PY13 Probability Map for GS PY14 Probability Map for GO PY15 Probability Map for WW PY16 Probability Map for SF PY17 Probability Map for SC PY18 Probability Map for SG PY19 Probability Map for SS PY20 Probability Map for SO PY21 Probability Map for OF PY22 Probability Map for OC PY23 Probability Map for OG PY24 Figure 28. Conditional Probability Maps of Land Use Transitions 1 st Scenario Continue 78 Probability Map for OS PY25 Probability Map for OO PY26 Figure 28. Conditional Probability Maps of Land Use Transitions 1 st Scenario Continue The conditional probability maps of land use transitions show that the result of the MLR model simulation could not cover the whole area of Siak District. Only 63.45 of the total area of Siak District could be simulated by the MLR model, and the rest of the area which is 36.55 of Siak District could not be simulated. This situation revealed after the MLR model computation was conducted in spatial manner. It may be caused by the nature of MLR model which is a generalized logistic regression model which conditioned all response categories having the same parameters. The MLR model forced every land use transitions to be driven by all significant parameters which have been determined, while in the real condition each land use transition probably has unique combination of parameters which drive its land use transition. Eventually, the MLR simulation in this research produced the generalized conditional probability maps of land use transitions which could not cover the whole area which has been simulated. Figure 29. The Aggregation of Conditional Probability Maps of Land Use Transitions 79 The conditional probability maps of land use transitions which have been produced in this research show that the range of probability values for a land use transition varies if it is compared with other land use transitions. In general, the probability values of all land use transitions range from 0 to 0.999. The ranges of probability values covered only the area which could be simulated by the MLR model simulation covered 63.45 of the total area of Siak District. In order to examine the performance of final model of land use change in Siak District that has been developed, this research compares the conditional probability maps of land use transitions 2005 – 2008 with the actual land use transitions 2005 – 2008. The comparison between the conditional probability maps of land use transitions 2005 – 2008 and the actual land use transitions 2005 – 2008 have been done by overlaying intersecting the maps individually; the conditional probability map of a land use transition with its actual land use transition map. Then, the basic statistical properties Min, Max, Mean, and Standard Deviation were derived from the intersected maps in order to examine the statistical properties of the probability values in actual condition. Furthermore, the distributions of MLR conditional probability values in actual condition were also derived by subtracting and adding the mean value with standard deviation value which would produce lower bound and upper bound of data distribution respectively. The statistical properties of MLR conditional probability values in actual condition 2005 – 2008 is shown in the Figure 30, whereas Figure 31 show the distribution of the most probability values about 68 by assuming its values distributed in normal distribution. The research found that the statistical properties of MLR conditional probability values for each land use transition in actual condition 2005 - 2008 are various. The minimum values of every land use transitions are close to 0, but the maximum values vary. There are 14 land use transitions which have Max value higher than 0.5, and the rest 12 land use transitions have Max value less than 0.5. These conditions have caused the range Min-Max of probability values are also various for every land use transitions. Furthermore, the distribution Lower-Upper bound of probability values that have been examined show that the data distributions vary for every land use transitions. There are some land use 80 transitions that have narrow range of data distribution, and some other land use transitions that have large range of values. The Statistical Properties of MLR Conditional Probability Values in Actual Land Use Change 2005 ‐ 2008 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Land Use Transition P ro b a b ili ty Min Max Mean Std Dev Figure 30. The Statistical Properties of MLR Conditional Probability Values in Actual Land Use Change 2005 – 2008 1 st Scenario The Distribution of MLR Conditional Probability Values in Actual Land Use Change 2005 ‐ 2008 ‐0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Land Use Transition P ro b a b ilit y Mean Lower Bound Upper Bound Figure 31. The Distribution of MLR Conditional Probability Values in Actual Land Use Change 2005 – 2008 1 st Scenario Actually, the examined Min-Max values and data distribution of probability values for every land use transitions were expected to have narrow range that is located between 0.5 – 1.0 in order to conclude that the land use 81 change model for Siak District could fit the actual spatial data layers correctly. However, the result of this model validation can be used as consideration for the future research of land use change modeling using logistic regression model in order to develop the adequate land use change model which is good in statistical and spatial manners.

4.4.3 MLR Model using Observed Variables 2

nd Scenario In the previous chapter, the driving factors which may drive the land use change in Siak District have been determined by MLR model analysis, which shows 24 significant variables contribute in driving land use change in Siak District. In this chapter, the research will describe the land use change driving factors based on the direct observation in the research site during field data collection activities were done. Based on the observation in the research site, there are three major driving factors of land use change in Siak District that are the existences of crop and timber plantation, the existences of road network, and the spatial plans. These observed driving factors are in accordance with the significant variables of Siak District’s land use change model which have been resulted by MLR model analysis. Some evidences which support those driving factors the existences of crop and timber plantation, the existences of road network, and the spatial plans become the major driving factors of land use change in Siak District will be delivered in the following discussions.

4.4.3.1 Observed Land Use Change Driving Factors

Based on the Forestry and Crop Spatial Plan at district level, Siak District allocates for about 59 of their district area for crop and timber plantation. The existences of crop and timber plantation have stimulated the allocated areas for crop and timber plantation and many areas near the existing plantations in Siak District to transform into new plantation area, and in many cases natural forest were logged in order to prepare the land for new crop and timber plantation area. The land preparation for new plantation in Siak District usually involves the transformation from Forest land to bare land Other lands category or shrub land Grassland category as transition phases before they are developed to crop and 82 timber plantation completely, since the land preparation of its plantations involving the land clearing activity in the initial land uses. Forest which are close to existing crop and timber plantation tends to be transformed into new plantation rather than forest which are far from existing plantation area. Furthermore, the establishment of crop and timber plantation in Siak District also stimulates new development of settlements and market place which are usually developed around the plantation area. Figure 32. Development of new crop plantation and settlements stimulated by existing Cropland During 2002 – 2008, Siak District has developed new road for about 562 km with total length of road in 2008 is 2,064 km Siak Government 2008. The road development in Siak District will be continued until next couple years in order to open the accessibility of the remote areas to the central area, and hopefully in the future new central areas will also be developed. The road development may open the accessibility of socio-economic activities in remote areas, but in the other hand the establishment of new road will also stimulate the land use change on either side of the road. In fact, the developments of new road itself have altered the initial land uses to be Built up area road. The establishment of new roads which are close to forest is the starting point for Forest land to be deforested, and also motivates the conversion from the initial land uses on side of the road for other land uses, such as settlements area and agriculture area. A simple spatial analysis, which overlaid the major land use transitions and the distance from road, show that most of major land use transitions are located in the area which are close to road 1 km, and only Forest land in stable condition are mostly located far from the road 1 km.