Scenario 4 Scenario-based land use modeling by using CLUE-S model
42 The selection of driving factors that has significant effect to land use change
was conducted by using logistic regression analysis. In case of forest, the quantitative analysis of driving factors of land use change by using logistic
regression showed that the driving factors that significantly influence forest area were population density, elevation, distance to main road, distance to public facility
and distance to education facility. Based on the calculation results, it can be concluded that the most significant driving factor of forest area was distance to
education facility and followed by distance to public facility, elevation, distance to main road, and population density. The relationship between driving factors and
land use change showed that distance to primary road, distance to public facility and education facility have positive effect to forest area to change which means that
the higher the value of these factors, the higher the probability of land use to change. In contrast, population density, and elevation have negative effect that implies that
the higher the value of these factors, the decrease the probability of land uses to change.
The goodness of the statistical measurement revealed that ROC values for urban water area, grassland area, estate area, settlement area and forest area were
0.903, 0.701, 0.780, 0.813 and 0.994, which indicated that the probability of land uses built from these models were capable to represent land use changes and
empirical analysis by using logistic regression method was satisfactory to examine the relationship between driving factors and land use change in study area.
In order to improve the utility of scenario regarding to the deficiency of data and methods, this research has conducted several approaches, includes reducing the
uncertainty of data classification and combining qualitative and quantitative approach in driving factors selection.
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