Model Validation 2 MLR Model using Observed Variables 2
88 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
probability value range from 0 to 1.
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
Figure 36. Conditional Probability Maps of Land Use Transitions 2
nd
Scenario
89
Probability Map for GF PY11 Probability Map for GC PY12
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
Figure 36. Conditional Probability Maps of Land Use Transitions 2
nd
Scenario Continue
90
Probability Map for OC PY23 Probability Map for OG PY24
Probability Map for OS PY25 Probability Map for OO PY26
Figure 36. Conditional Probability Maps of Land Use Transitions 2
nd
Scenario Continue
The conditional probability maps of land use transitions 2005 – 2008 using observed variables have been produced are shown in Figure 36 above. Compare to
the result of MLR model simulation using all significant variables as the parameters which only 63.45 of the total area of Siak District could be
simulated, the conditional probability maps of land use transitions using observed variables show that the result of this MLR model simulation could cover the
whole area of Siak District. This condition could be happen because of this model simulation involved only 5 variables into the model which means its MLR model
only forces fewer variables easily rather than the MLR model developed in the 1
st
scenario of MLR model analysis. This result strengthens the research findings on land use change modeling using MLR model which the MLR model is a
generalized logistic regression model which conditioned all response categories having the same parameters, and forces every land use transitions to be driven by
the significant parameters which have been determined. The next procedure of model validation which is the same with the 1
st
scenario is comparing the conditional probability maps of land use transitions 2005 – 2008 and 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 in order to examine the statistical properties
91 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 research found that the statistical properties of MLR conditional
probability values for each land use transition in actual condition 2005 - 2008 are various. However, the model validation done using MLR Model which developed
from observed variables show that only 3 land use transitions CC, GG, and WW which have Max value higher than 0.5, and the rest 23 land use transitions have
Max value less than 0.5. Furthermore, the land use transition CC cropland in stable condition has a good distribution of probability values which range from
0.42 to 0.72, while other land use transitions that have large range of values and they are distributed under the threshold of 0.5. Similar with the 1
st
scenario, these statistical properties show that the final model could not fit the actual spatial data
layers into the actual condition of land use change 2005 – 2008.
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 ilit
y
Min Max
Mean Std
Dev
Figure 37. The Statistical Properties of MLR Conditional Probability Values in Actual Land Use Change 2005 – 2008 2
nd
Scenario
92
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 38. The Distribution of MLR Conditional Probability Values in Actual Land Use Change 2005 – 2008 2
nd
Scenario The model validations have been conducted in two scenarios regarding to
the two models developed using all significant variables 24 variables and observed variables. These two scenarios of land use change modeling using MLR
model could give better understandings on advantages and disadvantages of MLR model for modeling land use change. These research findings on land use change
modeling using MLR model would be concluded in the next chapter.