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Figure 4.33: Multi Criteria Evaluation Results for the Sustainable Scenario

Figure 4 33: Multi Criteria Evaluation Results for the Sustainable Scenario

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3. GEOGRAPHICAL INFORMATION SYSTEMS AND URBAN GROWTH
3. GEOGRAPHICAL INFORMATION SYSTEMS AND URBAN GROWTH
As given above, the research starts with the literature review of sustainable urban
As given above, the research starts with the literature review of sustainable urban
As given above, urban strategies are mainly based on sustainable development
As given above, urban strategies are mainly based on sustainable development
Figure 2.1: Sustainable Urban Form (Holden, 2004) the following fundamentals in Figure 2.1
Figure 2.1: Sustainable Urban Form (Holden, 2004) the following fundamentals in Figure 2.1
suitability values in numerical form as matrices in the computer(Fig 3.1).
suitability values in numerical form as matrices in the computer(Fig 3.1).
following sections
following sections
Table 3.3: Pairwise Comparison Values (Saaty, 1994) Pairwise comparison method is the process of comparing the relevance importance,
Table 3.3: Pairwise Comparison Values (Saaty, 1994) Pairwise comparison method is the process of comparing the relevance importance,
Table 3.5: Calculation of Priority Vector
Table 3.5: Calculation of Priority Vector
The upper row is the order of the random matrix, and the lower row is the derived from Saaty’s book (Saaty, 1980).
The upper row is the order of the random matrix, and the lower row is the derived from Saaty’s book (Saaty, 1980).
Figure 3.3: Probability surface of urban growth in Nanjing, 1988—2000 (Luo & Wei, 2009) probabilistic surface of urban growth (Figure 3.3).
Figure 3.3: Probability surface of urban growth in Nanjing, 1988—2000 (Luo & Wei, 2009) probabilistic surface of urban growth (Figure 3.3).
Table 3.7: Explanatory Variables for Nanjing Growth Probability(1988-2000) (Luo & Wei, 2009)
Table 3.7: Explanatory Variables for Nanjing Growth Probability(1988-2000) (Luo & Wei, 2009)
Figure 3.4: Linkage between growth rules and parameters (Ding & Zhang, 2007) controlled by five factors as given in Figure 3.4.
Figure 3.4: Linkage between growth rules and parameters (Ding & Zhang, 2007) controlled by five factors as given in Figure 3.4.
Figure 3.5: Artificial Neural Networks Model Framework (Pijanowski et al., 2009)
Figure 3.5: Artificial Neural Networks Model Framework (Pijanowski et al., 2009)
Figure 3.6: Tehran from 1980 -2020 Simulated by Artificial Neural Networks (Pijanowski et al., 2009)
Figure 3.6: Tehran from 1980 -2020 Simulated by Artificial Neural Networks (Pijanowski et al., 2009)
Figure 3.7: Von Neumann and the Moore neighborhood (Liu, 2009).
Figure 3.7: Von Neumann and the Moore neighborhood (Liu, 2009).
Figure 4.2: Famagusta Land Cover and Urban Growth 1963
Figure 4.2: Famagusta Land Cover and Urban Growth 1963
Table 4.1: Selected Departments and Experts for Interviews problems. These are given as follows;
Table 4.1: Selected Departments and Experts for Interviews problems. These are given as follows;
the main objectives have been set for Sustainable Development scenario (Figure 4.4). achieved by following the policies indicated in the following Table 4.2 would make them sustainable. In order to create Sustainable Urban Growth for Famagusta
the main objectives have been set for Sustainable Development scenario (Figure 4.4). achieved by following the policies indicated in the following Table 4.2 would make them sustainable. In order to create Sustainable Urban Growth for Famagusta
calculating transition probability between land use types.
calculating transition probability between land use types.
After the decision of factors and constraints to each land use type, general constraint
After the decision of factors and constraints to each land use type, general constraint
Figure 4.10: General Constraints for growth
Figure 4.10: General Constraints for growth
Figure 4.11: Constraint Map Development
Figure 4.11: Constraint Map Development
Figure 4.12: Distance to Road Map cellular automata modeling (see Figure 4.13).
Figure 4.12: Distance to Road Map cellular automata modeling (see Figure 4.13).
Figure 4.13: Distance to Road Fuzzy Transformed Map
Figure 4.13: Distance to Road Fuzzy Transformed Map
Figure 4.14: Combination of Constraints and Factors for Suitability Map combined for MCE suitability maps in IDRISI with Decision Support Tool (Figure 4.14).
Figure 4.14: Combination of Constraints and Factors for Suitability Map combined for MCE suitability maps in IDRISI with Decision Support Tool (Figure 4.14).
Figure 4.15: Visual Comparison of Actual and Simulated Land use for 2011
Figure 4.15: Visual Comparison of Actual and Simulated Land use for 2011
Figure 4.16: Visual Comparison of Actual and Simulated Land use for 2011
Figure 4.16: Visual Comparison of Actual and Simulated Land use for 2011
Figure 4.17: Visual Comparisons of Actual and Simulated Land covers for 2011
Figure 4.17: Visual Comparisons of Actual and Simulated Land covers for 2011
Figure 4.18: Comparison of Simulated and Actual Land Uses
Figure 4.18: Comparison of Simulated and Actual Land Uses
Figure 4.19: Simulated Land use 2020 under the Do Nothing Scenario
Figure 4.19: Simulated Land use 2020 under the Do Nothing Scenario
local experts’ interview as that is explained in the previous section (Table 4.2.)
local experts’ interview as that is explained in the previous section (Table 4.2.)
Table 4.7.
Table 4.7.
Table 4.8: Soil Characteristics in Famagusta
Table 4.8: Soil Characteristics in Famagusta
Figure 4.22: Brownfield Areas
Figure 4.22: Brownfield Areas
Figure 4.23: Distance to City Center
Figure 4.23: Distance to City Center
Figure 4.24: Soil Characteristics
Figure 4.24: Soil Characteristics
Figure 4.25: Soil Productivity
Figure 4.25: Soil Productivity
Figure 4.26: Vegetation
Figure 4.26: Vegetation
Figure 4.27: Distance from Natura 2000 Areas
Figure 4.27: Distance from Natura 2000 Areas
Figure 4.28: Distance to Open Space
Figure 4.28: Distance to Open Space
Figure 4.29: Distance to Local Services 4.2.2.2 Pairwise Comparison
Figure 4.29: Distance to Local Services 4.2.2.2 Pairwise Comparison
Table 4.9: Main Criteria Pairwise Comparison Matrix Table 4.10: Physical Compactness Criteria Pairwise Comparison Matrix
Table 4.9: Main Criteria Pairwise Comparison Matrix Table 4.10: Physical Compactness Criteria Pairwise Comparison Matrix
Table 4.11: Environmental Protection Criteria Pairwise Comparison Matrix
Table 4.11: Environmental Protection Criteria Pairwise Comparison Matrix
Table 4.12: Social Equity Criteria Pairwise Comparison Matrix Table 4.13: Brownfield Sub-Criteria Pairwise Comparison Matrix
Table 4.12: Social Equity Criteria Pairwise Comparison Matrix Table 4.13: Brownfield Sub-Criteria Pairwise Comparison Matrix
Table 4.14: Distance to City Center Pairwise Comparison Matrix
Table 4.14: Distance to City Center Pairwise Comparison Matrix
Table 4.15: Soil Characteristics Pairwise Comparison Matrix Table 4.16: Soil Productivity Pairwise Comparison Matrix
Table 4.15: Soil Characteristics Pairwise Comparison Matrix Table 4.16: Soil Productivity Pairwise Comparison Matrix
Table 4.17: Vegetation Pairwise Comparison Matrix
Table 4.17: Vegetation Pairwise Comparison Matrix
Table 4.18: Distance to Natura2000 Sites Pairwise Comparison Matrix Table 4.19: Distance to Open Spaces Pairwise Comparison Matrix
Table 4.18: Distance to Natura2000 Sites Pairwise Comparison Matrix Table 4.19: Distance to Open Spaces Pairwise Comparison Matrix
Table 4.20: Distance to Local Services Pairwise Comparison Matrix
Table 4.20: Distance to Local Services Pairwise Comparison Matrix
Table 4.21: Summary of the Criteria Sets
Table 4.21: Summary of the Criteria Sets
Figure 4.31: General Constraints for Sustainable Scenario
Figure 4.31: General Constraints for Sustainable Scenario
Figure 4.32: General Constraint Map Development for Sustainable Scenario
Figure 4.32: General Constraint Map Development for Sustainable Scenario
Figure 4.34: Simulation of Sustainable Scenario
Figure 4.34: Simulation of Sustainable Scenario
Step One: Opening Idrisi Shape to Idrisi Module an define the layer to be converted for values to be converted.
Step One: Opening Idrisi Shape to Idrisi Module an define the layer to be converted for values to be converted.
Step One: Defining Suitability Layers and Criteria Zones in ArcGIS Step Two: Updating Attributes of Criteria Layers and Normalize Them for 0-255
Step One: Defining Suitability Layers and Criteria Zones in ArcGIS Step Two: Updating Attributes of Criteria Layers and Normalize Them for 0-255
Step Three: Converting Shapefile to IDRISI Raster Format by “Shapetoldr” tool
Step Three: Converting Shapefile to IDRISI Raster Format by “Shapetoldr” tool
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