86 order to convince that the observed variables have significant contribution to the
land use change happen in Siak District, the procedure of likelihood ratio tests have been done in order to check the contribution of each effect to the land use
change model. Table 20. Likelihood Ratio Tests for Observed Variables
Likelihood Ratio Tests
43614.131 1788.275
25 .000
49206.686 7380.831
25 .000
42221.697 395.841
25 .000
42168.217 342.362
25 .000
42353.022 527.167
25 .000
42027.977 202.121
25 .000
Effect Intercept
CROPDIST02 ROADDIST
KWSID RTRWPID
CONCESSID -2 Log
Likelihood of Reduced
Model Model Fitting
Criteria Chi-Square
df Sig.
Likelihood Ratio Tests
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed
by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.
The result of likelihood ratio tests for each observed variable done in MLR model analysis shows that the significance levels of the observed variables tested
are less than 0.05, and in other word all observed variables may be considered as significant variables of land use change in Siak District and would be included
into the final model. The list of significant variables would be included into the model is shown in Table 20.
4.4.3.3 Significance Test for Final Model 2
nd
Scenario
There were two tests which have been done in order to determine whether the final model were adequate to explain the land use change happen in Siak
District or not. The tests done were Likelihood Ratio Test of the final model and Pseudo R-Square statistics. Based on the likelihood ratio test of the final model,
the significance level produced is less than 0.05 Sig.0.05 which may be concluded that the final model which has been produced was outperforming the
Null. In other words, the land use change model of Siak District that has been developed by considering the observed variables could be concluded as a good
fit model.
87 Table 21. Likelihood Ratio Test of the Final Model 2
nd
Scenario
Model Fitting Information
53960.379 41825.856
12134.524 125
.000 Model
Intercept Only Final
-2 Log Likelihood
Model Fitting
Criteria Chi-Square
df Sig.
Likelihood Ratio Tests
Table 22. Pseudo R-Square Statistics 2
nd
Scenario
Pseudo R-Square
.703 .706
.225 Cox and Snell
Nagelkerke McFadden
Furthermore, the pseudo r-squared statistics, either Cox and Snell or Nagelkerke, indicate that the variability of land use change in Siak District can be
explained more than 70 by the observed variables, whereas according to McFadden, it is only 22.5. However, in general the pseudo r-squared statistics
that have been produced shows that most of variability in land use change happen in Siak District could be explained by the final model which was developed by
using the observed variables in the research site. The two tests for final model which have been conducted above indicate
that the final model of land use change in Siak District which has been developed by considering the observed variables in MLR model is a good model that can
explain most of the variability of land use change in the research site. The observed variables that have been determined can be concluded as driving factors
of land use transitions in Siak District.
4.4.3.4 Model Validation 2
nd
Scenario
The coefficient of the observed variables Appendix 6 and spatial data layersparameters 2005 x
i
have been simulated on MLR model in order to produce the conditional probability maps of land use transitions during 2005 –
2008. The computation of coefficient of the observed variables β
i
and spatial data layersparameters 2005 x
i
on MLR model equation produced 26 conditional
