Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz)
Abstract
:1. Introduction
2. Methodology
2.1. Study Area
2.2. Methods
Land-Use/Land-Cover Change Detection
2.3. Logistic Regression Model
Results
2.4. Land Transformation Model
2.5. TMA Ecological Development Planning
3. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data | Source | Resolution (m) | Date |
---|---|---|---|
Landsat 5 TM | US Geological Survey | 30 | 10 July 1984 |
Landsat 7 ETM+ | US Geological Survey | 30,15 (PAN) | 31 August 2000 |
Landsat 8 OLI | US Geological Survey | 30,15 (PAN) | 8 July 2018 |
Land Use | 1984 | 2000 | 2018 | 1984–2000 | 2000–2018 | 1984–2018 | |||
---|---|---|---|---|---|---|---|---|---|
Area | Total% | Area | Total% | Area | Total% | Variation | Variation | Variation | |
Barren lands | 151,962.57 | 68.85 | 149,223.51 | 67.60 | 147,051.99 | 66.62 | −1.80 | −1.46 | −3.23 |
Built-up lands | 7220.34 | 3.27 | 14,027.58 | 6.35 | 22,346.82 | 10.12 | 94.27 | 59.30 | 209.50 |
Agricultural lands | 25,369.83 | 11.49 | 23,259.42 | 10.53 | 22,489.02 | 10.18 | −8.32 | −3.31 | −11.36 |
Garden lands | 10,242.63 | 4.65 | 9094.86 | 4.12 | 6653.43 | 3.01 | −11.21 | −26.84 | −35.04 |
Pasture lands | 25,248.15 | 11.44 | 24,669.99 | 11.17 | 21,583.80 | 9.77 | −2.29 | −12.51 | −14.51 |
Water bodies | 669.24 | 0.30 | 437.40 | 0.19 | 587.700 | 0.26 | −34.64 | 34.36 | 12.18 |
1984 | 2000 | 2018 | |
---|---|---|---|
Overall Accuracy | 93.6 | 95.3 | 96.4 |
Kappa Coefficient | 0.89 | 0.91 | 0.94 |
Variable Description | Source and Description | Nature of Variable |
---|---|---|
Dependent variable Urban growth from 1984 to 2018 | Subtraction of the Boolean Urban areas for 1984 from 2018 (classified images); 0—no urban growth; 1—urban growth | Dichotomous |
Slope | Slope in percent | Continuous |
Population density | Population density (person/ha) | Continuous |
Distance from commercial centers | Euclidean distance from CBD(m) | Continuous |
Distance from roads | Distance to the nearest major road (m) | Continuous |
Distance from urban centers | Euclidean distance to the urban region (m) | Continuous |
Distance from power lines | Euclidean distance from power lines (m) | Continuous |
Distance from rivers | Euclidean distance from rivers (m) | Continuous |
Distance from faults | Euclidean distance from faults (m) | Continuous |
Urban CVN (center versus neighbor) | Number of urban cells within a 7 · 7 cell window (ranging from 0 to 8) | Ranging from 0 to 8 |
Geology | The degree of hardness for lithological structures | Continuous |
Barren lands | 1—bare land; 0—not bare land | Design |
Garden lands | 1—garden land; 0—not garden land | Design |
Agriculture lands | 1—agriculture land; 0—not agriculture land | Design |
Pasture lands | 1—pasture land; 0—not pasture land | Design |
Built up lands | 1—built-up lands; 0—not built-up lands | Design |
Distribution of land price | Spatial distribution of land price | Continuous |
1984–2000 | 2000–2018 | 1984–2018 | |||
---|---|---|---|---|---|
ROC | Pseudo-R2 | ROC | Pseudo-R2 | ROC | Pseudo-R2 |
0.86 | 0.78 | 0.82 | 0.74 | 0.89 | 0.79 |
Step 14 | −2 Log Likelihood | Cox & Snell R2 | Nagelkerke R2 | Pseudo R2 |
---|---|---|---|---|
26391.79 | 0.39 | 0.61 | 0.79 |
B a | S.E. b | Wald c | Df d | Sig. e | Exp(B) f | 95% C.I. for EXP(B) g | ||
---|---|---|---|---|---|---|---|---|
Lower | Upper | |||||||
Constant | −2.867 | 0.030 | 8861.936 | 1 | 0.000 | 0.507 | ||
Built-up lands | −0.26 | 0.011 | 6.178 | 1 | 0.000 | 0.974 | 0.954 | 0.994 |
Agriculture lands | −0.80 | 0.012 | 45.186 | 1 | 0.000 | 0.924 | 0.902 | 0.945 |
Land prices | −0.155 | 0.021 | 53.915 | 1 | 0.000 | 0.856 | 0.822 | 0.893 |
Pasture lands | −0.137 | 0.010 | 183.715 | 1 | 0.013 | 0.872 | 0.855 | 0.890 |
Garden lands | −0.131 | 0.010 | 180.805 | 1 | 0.000 | 0.877 | 0.861 | 0.894 |
Dist f roads | 0.328 | 0.010 | 1067.199 | 1 | 0.000 | 1.389 | 1.362 | 1.416 |
Dist f urban | 1.519− | 0.050 | 922.745 | 1 | 0.000 | 0.219 | 0.199 | 0.242 |
Dist f CBDs | −0.206 | 0.017 | 147.599 | 1 | 0.000 | 0.814 | 0.788 | 0.842 |
Dist f power lines | −0.39 | 0.012 | 10.334 | 1 | 0.001 | 0.962 | 0.940 | 0.985 |
Geology | −0.71 | 0.011 | 45.219 | 1 | 0.013 | 0.932 | 0.913 | 0.951 |
Dist f faults | −0.53 | 0.011 | 21.772 | 1 | 0.000 | 1.054 | 1.031 | 1.077 |
Urban CVN | 0.407 | 0.013 | 1028.951 | 1 | 0.000 | 0.665 | 0.649 | 0.682 |
Dist f rivers | −0.057 | 0.014 | 17.289 | 1 | 0.013 | 0.944 | 0.919 | 0.970 |
Slope | −0.380 | 0.029 | 171.274 | 1 | 0.000 | 0.684 | 0.646 | 0.724 |
Population density | 1.105 | 0.009 | 13548.603 | 1 | 0.000 | 3.019 | 2.964 | 3.076 |
1984–2000 | 2000–2018 | 1984–2018 | ||||||
---|---|---|---|---|---|---|---|---|
RMS | Kappa | PCM | RMS | Kappa | PCM | RMS | Kappa | PCM |
Cycle 9300 | Cycle 8600 | Cycle 8000 | ||||||
0.0197233 | 0.865437 | 85.871426 | 0.0186641 | 0.853461 | 86.842313 | 0.0189534 | 0.844434 | 87.831315 |
Advantages | Disadvantages |
---|---|
Logistic regression is easy to implement and interpret and very efficient to train (Terrset). | If the number of observations is less than the number of features, logistic regression should not be used and may lead to overfitting. |
It makes no assumptions about distributions of classes in feature the space (Table 4). | It constructs linear boundaries. |
It can easily extend to multiple classes (multinomial regression) and a natural probabilistic view of class predictions (lack of necessity). | The major limitation of logistic regression is the assumption of linearity between the dependent variable and the independent variables. |
It not only provides a measure of the appropriatenes of a predictor (coefficient size) but also its direction of association (positive or negative) (Table 6 and Table 7). | It can only be used to predict discrete functions. Hence, the dependent variable of logistic regression is bound to the discrete number set. |
It can rapidly classify unknown records. | Non-linear problems cannot be solved with logistic regression because it has a linear decision surface. Linearly separable data are rarely found in real-world scenarios. |
Accuracy on many simple datasets and performs well when the dataset is linearly separable. | Logistic regression requires average or absent multicollinearity between independent variables. |
It can interpret model coefficients as indicators of feature importance (Table 7). | It is difficult to obtain complex relationships using logistic regression. More powerful and compact algorithms such as neural networks can easily outperform logistic regression. |
Logistic regression is less inclined to overfit, but it can overfit in high dimensional datasets. Regularization (L1 and L2) techniques may be considered to avoid overfitting in such scenarios (Table 6). | In linear regression, independent and dependent variables are related linearly. However, logistic regression requires that independent variables be linearly related to the log odds (log(p/(1 − p)). |
Advantages | Disadvantages |
---|---|
Can be applied to complex non-linear problems. | It is not known to what extent each independent variable is affected by the dependent variable. Computations are difficult and time-consuming. |
Works well with large input data (TMA). | The proper functioning of the model depends on the quality of the training data. |
Provides quick predictions after training (Figure 9). | If the model does not work properly, generalization problems arise. |
Same accuracy ratio can be achieved, even with small datasets. |
City | Land-Use Type | Area (Ha) | Function |
---|---|---|---|
Tabriz | Incompatible land uses | 702 | Reducing environmental pollution |
Deteriorated textures | 420 | The revitalization of the city | |
One-story housing units | 2462 | Infill development (compact city strategy) | |
Vacant lands | 6043 | Limiting urban spatial polarization with the strengthening of new centers |
City | Type | Direction | Length (Km) | Function |
---|---|---|---|---|
Tabriz | Artificial Green Belt | Northern | 12.4 | Stabilization of the urban development and limiting it toward the Tabriz fault |
Artificial Green Belt | Southern | 21.3 | Stabilization of the urban area and reducing air pollutants from industrial land use sources | |
Natural Green Belt | Western | 16.1 | To preserve agricultural land and stop the spread of villages in the west of Tabriz | |
Basmenj | Natural Green Belt | Southern | 6.3 | Preservation of southern gardens of Basmenj |
Sardroud | Natural Green Belt | Southern | 4.4 | Protection of southern gardens of Sardroud from rural development |
Khosroshahr | Natural Green Belt | Eastern | 4 | Prevention of rural–urban integration in Khosrowshahr |
Natural green bow | Northern–Eastern | 7.3 | Preservation of garden lands in Khosrowshahr | |
Usko | Natural Green Belt | Eastern–Western–Southern | 13.4 | Prevention of rural–urban integration in Usko |
Natural green bow | Eastern–Western | 6.4 | Preservation of garden lands in Usko |
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Mahmoudzadeh, H.; Abedini, A.; Aram, F. Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz). Land 2022, 11, 2162. https://doi.org/10.3390/land11122162
Mahmoudzadeh H, Abedini A, Aram F. Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz). Land. 2022; 11(12):2162. https://doi.org/10.3390/land11122162
Chicago/Turabian StyleMahmoudzadeh, Hassan, Asghar Abedini, and Farshid Aram. 2022. "Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz)" Land 11, no. 12: 2162. https://doi.org/10.3390/land11122162
APA StyleMahmoudzadeh, H., Abedini, A., & Aram, F. (2022). Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz). Land, 11(12), 2162. https://doi.org/10.3390/land11122162