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3.3.3. Population Projection
Population projection is the prediction of future populations based on the present population, and with the present urban growth rates. Population projection
can be estimated using Geometric method, the equation used is:
1
n t
P P
r
2.4
Where ; Estimated population; P denotes Base year population, r denotes
Population Growth Ratio ; N difference year between P
t
and P .
3.3.4. Relation Between Amount of Population and Built-up Area
Regression and correlation analysis was applied to find relation between urban land use and population projection. The result from this step is relation
between increases of population and built-up area. Linier regression equation used to predict land demand of urban built up area in the feature year’s base on increase
of population.
Geometric and Atmospheric Correction Population Data in
1991, 1997, 2001 and 2007
Satellite Imagery in 1991 1997
Image Classification using Visual Interpretation Methods
Classification of Landcover and Landuse type Built-up Area
Reference Maps in
2000 and 2007
Relation between Amount of population and Built-up Area Prediction of demand for Built-up Area using
Regression and Correlation Analysis Demand of Built-up Area as the effect of population increase in year t+n
Population Projection in year t+n
Figure 3.5 Regression and correlation analysis to get demand for prediction model
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3.3.5. Configuration of Model, Calibration and Validation
Model calibration is the process of modifying the input parameters in an attempt to match field conditions within some acceptable criteria until the output from
the model matches an observed set of data. In this study, two scenarios were applied to calibrate the model. In the first scenario, driving forces and constrained factors
slope 15 , protected forest, open green space and water body as an input parameter in the model simulation. Whereas, the second scenario is the simulation
model will be run without slope as an input parameter. Meanwhile, Validation is the evaluation of the predicted modelling results. In
this research, Model simulation was running from 1991 – 2000 and 2000 – 2007 to
calibrate and validate the model result.
Start model from year-t Configure Scenario Rules
Start model until t+n Compute Simulation Accuracies
Validation K
hat
= 75 Actual Built-up Area in
year t+n
Model Calibration Validation Complete
Yes No
Calibration Rule Setting
Figure 3.6 Model Calibration and Validation Process Simulation accuracy assessments base on the error matrix and kappa coefficient
were applied in model validation process. Kappa analysis yields a K
hat
coefficient that measures the difference between the observed agreement of the two maps and the
agreement that might be attained by chance matching Campbell 1996. The K
hat
coefficient was computed as follows:
52
1 1
2 1
r r
ii i
i i
i hat
r i
i i
N x
x x
K N
x x
2.5
Where :
hat
K :
The number of categories in the error matrix
ii
x
: The number of cells in row i and column i
i
x
: The total number of cells in row i shown as the row total in the
matrix
i
x
: The total number of cells in column i shown as the column total in
the matrix
N
: The total number of observations included in the matrix
Landis and Koch 1977 characterize agreement as follows: values 0.75 are very good to excellent, values between 0.4 and 0.75 are fair to good and values of 0.4
or less indicate poor agreement.
3.4. Result and Discussion
Kategori