Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models
<p>Study Area.</p> "> Figure 2
<p>Overall Methodology.</p> "> Figure 3
<p>Urban LULC Modeling.</p> "> Figure 4
<p>LULC classifications in: (<b>a</b>) 1988; (<b>b</b>) 1993; (<b>c</b>) 1998; (<b>d</b>) 2003; (<b>e</b>) 2008; and (<b>f</b>) 2011.</p> "> Figure 4 Cont.
<p>LULC classifications in: (<b>a</b>) 1988; (<b>b</b>) 1993; (<b>c</b>) 1998; (<b>d</b>) 2003; (<b>e</b>) 2008; and (<b>f</b>) 2011.</p> "> Figure 5
<p>LULC change during: (<b>a</b>) 1988–1993; (<b>b</b>) 1993–1998; (<b>c</b>) 1993–2003; (<b>d</b>) 2003–2008; and (<b>e</b>) 2008–2011; (<b>f</b>) The amount of change in each period.</p> "> Figure 6
<p>Comparison of LULC prediction and urban extend between Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC): (<b>a</b>) CA-MC 1998; (<b>b</b>) MLP-MC 1998; (<b>c</b>) CA-MC 2003; (<b>d</b>) MLP-MC 2003; (<b>e</b>) CA-MC 2008; (<b>f</b>) MLP-MC 2008; (<b>g</b>) CA-MC 2011; and (<b>h</b>) MLP-MC 2011.</p> "> Figure 7
<p>Comparison of LULC simulation maps in 2014 from four schemes: (<b>a</b>) CA-MC; (<b>b</b>) MLP-MC; (<b>c</b>) Extrapolation; and (<b>d</b>) SD.</p> "> Figure 7 Cont.
<p>Comparison of LULC simulation maps in 2014 from four schemes: (<b>a</b>) CA-MC; (<b>b</b>) MLP-MC; (<b>c</b>) Extrapolation; and (<b>d</b>) SD.</p> "> Figure 8
<p>Comparison of LULC simulation maps in 2035 from the transition probability calculated by: (<b>a</b>) extrapolation; and (<b>b</b>) SD.</p> "> Figure 9
<p>Urban extend overlay with the land transportation system of the BMR in 2035.</p> ">
Abstract
:1. Introduction
2. Study Area
3. Data and Methodology
3.1. Preparation of Satellite Image and LULC Classification
3.2. Data Input for the Urban LULC Modeling
3.3. Change Analysis
3.4. Urban LULC Modeling
3.4.1. Cellular Automata-Markov Chain (CA-MC)
3.4.2. Multi-Layer Perceptron-Markov Chain (MLP-MC)
- Change Analysis
- Transition Potential Determination
- LULC Change Prediction
3.4.3. Model Validation
3.4.4. Model Application
4. Results and Discussion
4.1. LULC Classification and Accuracy Assessment
4.2. Change Analysis
4.3. Urban LULC Prediction
4.3.1. Identification of Driving Factors
4.3.2. Transition Probability Analysis
- Transition probability calculation from the MC
- Integration of the future population and economic demand for the transition probability of urban expansion in 2014 and 2035
4.3.3. LULC Prediction and Validation
- LULC Prediction from CA-MC and MLP-MC
4.3.4. LULC Model Application
- Comparative study on the LULC prediction from the conventional MC and demographic–economics data to estimate the transition probability
- Simulation of the future urban LULC and urban expansion in 2035.
5. Conclusions
Author Contributions
Conflicts of Interest
References
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Data | Year | Scale/Resolution | Sources |
---|---|---|---|
Demographic data: density of population and housing, growth rate of population and housing | 1993, 1998, 2003, 2008, 2011, 2014 | 1:50,000 | Department of Provincial Administration |
Economic data: Gross regional and provincial product | 1988, 1993, 1998, 2003, 2008, 2011, 2014 | 1:50,000 | Office of the National Economic and Social Development Board |
Geo-data base of physical and environment related to urban features | n.d. | 1:25,000 to 1:50,000 | Bangkok Metropolis Administration GIS center |
Digital Elevation Model (DEM) | n.d. | 30 m | Bangkok Metropolis Administration GIS center |
Data | Detailed Description |
---|---|
Distance to agriculture | Distance to any agricultural activity |
Distance to BTS and MRT | Distance to BTS and MRT line |
Distance to built-up area | Distance to any man-made construction |
Distance to forest | Distance to any forest |
Distance to main road | Distance to any permanent road |
Distance to miscellaneous | Distance to any open land and grassland |
Distance to river | Distance to permanent flowing water and surface sources |
Distance to urban center | Distance to city center |
Growth rate of economics | Growth rate of GPP |
Growth rate of housing | Annual growth rate of housing |
Growth rate of population | Annual growth rate of population |
Housing density | Housing unit density per square kilometer |
Population density | Persons per square kilometer |
Slope | Mean slope in degrees |
Years | Overall Accuracy | Kappa |
---|---|---|
1988 | 85.19% | 0.80 |
1993 | 86.25% | 0.82 |
1998 | 90.12% | 0.87 |
2003 | 86.95% | 0.83 |
2008 | 87.65% | 0.83 |
2011 | 89.11% | 0.86 |
Year | Factor | Overall V | A | W | U | F | M |
---|---|---|---|---|---|---|---|
1998 | Distance to agriculture | 0.4533 | 0.6302 | 0.9938 | 0.2662 | 0.9176 | 0.1941 |
Distance to built-up area | 0.3843 | 0.8002 | 0.7752 | 0.2400 | 0.8207 | 0.0654 | |
Distance to center of city | 0.1725 | 0.2229 | 0.3752 | 0.1268 | 0.3429 | 0.0918 | |
Distance to forest | 0.1863 | 0.3178 | 0.2590 | 0.1170 | 0.2409 | 0.3127 | |
Distance to miscellaneous | 0.1514 | 0.1373 | 0.1381 | 0.0618 | 0.1085 | 0.0806 | |
Distance to road | 0.1349 | 0.1758 | 0.2737 | 0.0717 | 0.2902 | 0.0338 | |
1998 | Distance to water | 0.2699 | 0.3649 | 0.3191 | 0.5405 | 0.2517 | 0.0658 |
Economic growth rate | 0.1582 | 0.5003 | 0.3029 | 0.0994 | 0.3112 | 0.1283 | |
Elevation | 0.2913 | 0.1914 | 0.1896 | 0.6409 | 0.0989 | 0.0349 | |
Slope | 0.0652 | 0.0151 | 0.0532 | 0.1273 | 0.0303 | 0.0216 | |
2003 | Distance to agriculture | 0.4498 | 0.7677 | 0.9949 | 0.2285 | 0.9328 | 0.1356 |
Distance to built-up area | 0.4526 | 0.4701 | 0.9383 | 0.1378 | 1.0000 | 0.0389 | |
Distance to center of city | 0.1698 | 0.2121 | 0.3648 | 0.1209 | 0.3362 | 0.0958 | |
Distance to forest | 0.1790 | 0.9365 | 0.2334 | 0.1086 | 0.2193 | 0.3051 | |
Distance to miscellaneous | 0.4520 | 0.3931 | 0.1998 | 0.0646 | 0.1342 | 0.0283 | |
Distance to road | 0.1462 | 0.2646 | 0.3036 | 0.0633 | 0.3181 | 0.0284 | |
2003 | Distance to water | 0.2481 | 0.7880 | 0.2448 | 0.5162 | 0.1779 | 0.0759 |
Economic growth rate | 0.1547 | 0.8363 | 0.3090 | 0.0788 | 0.3102 | 0.1182 | |
Elevation | 0.2722 | 0.3918 | 0.1625 | 0.6009 | 0.0945 | 0.0247 | |
Housing density | 0.2401 | 0.6925 | 0.5083 | 0.1114 | 0.5019 | 0.0555 | |
Housing growth rate | 0.1643 | 0.9972 | 0.3150 | 0.0954 | 0.3137 | 0.1450 | |
Population density | 0.2337 | 0.9773 | 0.4975 | 0.0964 | 0.4939 | 0.0362 | |
Population growth rate | 0.1997 | 0.6992 | 0.4047 | 0.0927 | 0.3997 | 0.1077 | |
Slope | 0.0776 | 0.1496 | 0.0582 | 0.1694 | 0.0297 | 0.0180 | |
2008 | Distance to agriculture | 0.3668 | 0.5380 | 0.7965 | 0.1635 | 0.7814 | 0.0837 |
Distance to BTS and MRT | 0.2466 | 0.3331 | 0.5356 | 0.0902 | 0.5235 | 0.0696 | |
Distance to built-up area | 0.3918 | 0.7218 | 0.8035 | 0.2060 | 0.8427 | 0.0534 | |
Distance to center of city | 0.1734 | 0.1824 | 0.3712 | 0.1161 | 0.3462 | 0.0753 | |
Distance to forest | 0.2033 | 0.5839 | 0.3141 | 0.2883 | 0.2908 | 0.2760 | |
Distance to miscellaneous | 0.1347 | 0.6038 | 0.2719 | 0.0159 | 0.2650 | 0.0557 | |
Distance to road | 0.1651 | 0.0806 | 0.3463 | 0.0751 | 0.3564 | 0.0322 | |
Distance to water | 0.2943 | 0.6357 | 0.2366 | 0.6190 | 0.1979 | 0.0859 | |
Economic growth rate | 0.1802 | 0.3877 | 0.3675 | 0.1105 | 0.3495 | 0.1177 | |
Elevation | 0.2585 | 0.3879 | 0.1397 | 0.5676 | 0.1075 | 0.0219 | |
Housing density | 0.2430 | 0.6503 | 0.5161 | 0.1292 | 0.5009 | 0.0691 | |
Housing growth rate | 0.2061 | 0.3140 | 0.4045 | 0.1720 | 0.3869 | 0.1101 | |
Population density | 0.2384 | 0.4323 | 0.5101 | 0.1147 | 0.4940 | 0.0687 | |
Population growth rate | 0.2079 | 0.3740 | 0.4047 | 0.1504 | 0.3915 | 0.1454 | |
Slope | 0.0661 | 0.1865 | 0.0284 | 0.1447 | 0.0260 | 0.0137 | |
2011 | Distance to agriculture | 0.4496 | 0.5449 | 0.9966 | 0.1789 | 0.9238 | 0.1283 |
Distance to BTS and MRT | 0.2466 | 0.5991 | 0.5356 | 0.0902 | 0.5253 | 0.0696 | |
Distance to built-up area | 0.4723 | 0.7801 | 0.9306 | 0.3010 | 1.0000 | 0.0656 | |
Distance to center of city | 0.1734 | 0.7794 | 0.3712 | 0.1161 | 0.3462 | 0.0753 | |
Distance to forest | 0.2027 | 0.4043 | 0.2934 | 0.1734 | 0.2912 | 0.2918 | |
Distance to miscellaneous | 0.2338 | 0.4055 | 0.3398 | 0.0567 | 0.2914 | 0.0384 | |
Distance to road | 0.1651 | 0.0109 | 0.3463 | 0.0751 | 0.3564 | 0.0322 | |
2011 | Distance to water | 0.3646 | 0.5748 | 0.2449 | 0.7866 | 0.1878 | 0.0899 |
Economic growth rate | 0.1801 | 0.0298 | 0.3674 | 0.1104 | 0.3495 | 0.1172 | |
Elevation | 0.2585 | 0.6823 | 0.1397 | 0.5676 | 0.1075 | 0.0219 | |
Housing density | 0.2404 | 0.5694 | 0.5053 | 0.1330 | 0.4951 | 0.0776 | |
Housing growth rate | 0.2322 | 0.2842 | 0.4554 | 0.1634 | 0.4384 | 0.1609 | |
Population density | 0.2370 | 0.4840 | 0.5058 | 0.1209 | 0.4909 | 0.0648 | |
Population growth rate | 0.2136 | 0.7338 | 0.4244 | 0.1551 | 0.4086 | 0.1311 | |
Slope | 0.0661 | 0.1697 | 0.0284 | 0.1447 | 0.0260 | 0.0137 | |
2014 | Distance to agriculture | 0.4491 | 0.6516 | 0.9981 | 0.1619 | 0.9456 | 0.1127 |
Distance to BTS and MRT | 0.2272 | 0.3075 | 0.4932 | 0.0771 | 0.4896 | 0.0627 | |
Distance to built-up area | 0.4559 | 0.7176 | 0.9484 | 0.2107 | 0.4552 | 0.0821 | |
Distance to center of city | 0.1681 | 0.4045 | 0.3639 | 0.0916 | 0.3325 | 0.0939 | |
Distance to forest | 0.2069 | 0.6778 | 0.2977 | 0.1547 | 0.3009 | 0.3128 | |
Distance to miscellaneous | 0.2781 | 0.4165 | 0.0854 | 0.2331 | 0.1068 | 0.0794 | |
Distance to road | 0.1545 | 0.2389 | 0.3302 | 0.5810 | 0.3345 | 0.0318 | |
Distance to water | 0.2663 | 0.4229 | 0.1508 | 0.0660 | 0.0980 | 0.0786 | |
Economic growth rate | 0.1700 | 0.1255 | 0.3366 | 0.0718 | 0.3415 | 0.1443 | |
Elevation | 0.2764 | 0.0618 | 0.1358 | 0.6100 | 0.1003 | 0.0275 | |
Housing density | 0.2251 | 0.2134 | 0.4769 | 0.0991 | 0.4732 | 0.1076 | |
Housing growth rate | 0.1847 | 0.1065 | 0.3495 | 0.1267 | 0.3514 | 0.1670 | |
Population density | 0.2218 | 0.2052 | 0.4712 | 0.0880 | 0.4671 | 0.1105 | |
Population growth rate | 0.2075 | 0.3831 | 0.4078 | 0.1104 | 0.3978 | 0.1905 | |
Slope | 0.0814 | 0.0361 | 0.0595 | 0.1719 | 0.0431 | 0.0182 |
Class | Agriculture | Water | Urban | Forest | Miscellaneous | |
---|---|---|---|---|---|---|
1998 | Agriculture | 0.9486 | 0.0025 | 0.0418 | 0.0032 | 0.0039 |
Water | 0.1121 | 0.8490 | 0.0358 | 0.0005 | 0.0026 | |
Urban | 0.0000 | 0.0041 | 0.9880 | 0.0000 | 0.0079 | |
Forest | 0.3487 | 0.0073 | 0.0160 | 0.6236 | 0.0044 | |
Miscellaneous | 0.6223 | 0.0180 | 0.3080 | 0.0000 | 0.0516 | |
2003 | Agriculture | 0.9465 | 0.0022 | 0.0451 | 0.0013 | 0.005 |
Water | 0.1745 | 0.7905 | 0.0269 | 0.0034 | 0.0047 | |
Urban | 0.0000 | 0.0032 | 0.9905 | 0.0002 | 0.0061 | |
Forest | 0.6245 | 0.0016 | 0.0186 | 0.3524 | 0.0029 | |
Miscellaneous | 0.7160 | 0.0272 | 0.1657 | 0.0067 | 0.0843 | |
2008 | Agriculture | 0.9112 | 0.0022 | 0.0682 | 0.0042 | 0.0141 |
Water | 0.1277 | 0.8167 | 0.0446 | 0.0051 | 0.0059 | |
Urban | 0.0000 | 0.0030 | 0.9795 | 0.0011 | 0.0164 | |
Forest | 0.294 | 0.0010 | 0.0805 | 0.6245 | 0.0000 | |
Miscellaneous | 0.5427 | 0.0069 | 0.2602 | 0.0024 | 0.1878 | |
2011 | Agriculture | 0.9357 | 0.0025 | 0.0458 | 0.0015 | 0.0144 |
Water | 0.1349 | 0.8521 | 0.0126 | 0.0005 | 0.0000 | |
Urban | 0.0000 | 0.0021 | 0.9922 | 0.0002 | 0.0055 | |
Forest | 0.2310 | 0.0155 | 0.0463 | 0.6426 | 0.0646 | |
Miscellaneous | 0.3111 | 0.0051 | 0.4806 | 0.0000 | 0.2033 | |
2014 | Agriculture | 0.8221 | 0.0097 | 0.1586 | 0.0051 | 0.0045 |
Water | 0.1542 | 0.7630 | 0.0650 | 0.0158 | 0.0021 | |
Urban | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | |
Forest | 0.3185 | 0.0159 | 0.0494 | 0.6148 | 0.0015 | |
Miscellaneous | 0.5736 | 0.0077 | 0.3688 | 0.0142 | 0.0357 | |
2035 | Agriculture | 0.1780 | 0.0140 | 0.8023 | 0.0046 | 0.0010 |
Water | 0.2400 | 0.0849 | 0.6624 | 0.0111 | 0.0015 | |
Urban | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | |
Forest | 0.2504 | 0.0213 | 0.7122 | 0.0147 | 0.0015 | |
Miscellaneous | 0.1327 | 0.0105 | 0.8525 | 0.0036 | 0.0008 |
Classes | Agriculture | Water | Urban | Forest | Miscellaneous | |
---|---|---|---|---|---|---|
Extrapolation | Agriculture | 0.8426 | 0.0099 | 0.1393 | 0.0030 | 0.0052 |
Water | 0.1541 | 0.7854 | 0.0481 | 0.0125 | 0.0000 | |
Urban | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | |
Forest | 0.2861 | 0.0234 | 0.0326 | 0.6255 | 0.0324 | |
Miscellaneous | 0.0809 | 0.0030 | 0.7959 | 0.0056 | 0.1146 | |
SD | Agriculture | 0.8995 | 0.0055 | 0.0896 | 0.0029 | 0.0026 |
Water | 0.2016 | 0.7976 | 0.0007 | 0.0002 | 0.0000 | |
Urban | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | |
Forest | 0.4011 | 0.0001 | 0.5986 | 0.0002 | 0.0000 | |
Miscellaneous | 0.0079 | 0.0001 | 0.0051 | 0.0002 | 0.9868 |
Classes | Agriculture | Water | Urban | Forest | Miscellaneous | |
---|---|---|---|---|---|---|
Extrapolation | Agriculture | 0.5941 | 0.0000 | 0.3860 | 0.0022 | 0.0177 |
Water | 0.1717 | 0.7269 | 0.0728 | 0.0286 | 0.0000 | |
Urban | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | |
Forest | 0.1458 | 0.0862 | 0.6602 | 0.1000 | 0.0078 | |
Miscellaneous | 0.7859 | 0.0136 | 0.0000 | 0.0009 | 0.1996 | |
SD | Agriculture | 0.5365 | 0.0253 | 0.4130 | 0.0133 | 0.0118 |
Water | 0.1535 | 0.7644 | 0.0647 | 0.0158 | 0.0017 | |
Urban | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 | |
Forest | 0.2276 | 0.0012 | 0.7668 | 0.0042 | 0.0002 | |
Miscellaneous | 0.1366 | 0.0018 | 0.7703 | 0.0034 | 0.0879 |
Types | Years | |||||||
---|---|---|---|---|---|---|---|---|
1998 | 2003 | 2008 | 2011 | |||||
CA-MC | MLP-MC | CA-MC | MLP-MC | CA-MC | MLP-MC | CA-MC | MLP-MC | |
Overall accuracy | 92.3728 | 92.1738 | 88.4218 | 90.5811 | 89.4320 | 89.4969 | 85.5354 | 85.5690 |
K standard | 0.9097 | 0.9108 | 0.8748 | 0.8979 | 0.8913 | 0.8924 | 0.8570 | 0.8571 |
Kno | 0.9374 | 0.9383 | 0.9088 | 0.9257 | 0.9168 | 0.9171 | 0.8859 | 0.8863 |
KlocationStrata | 0.9148 | 0.9146 | 0.9054 | 0.9298 | 0.9145 | 0.9072 | 0.9359 | 0.9356 |
LULC Classes | Years | |||||||
---|---|---|---|---|---|---|---|---|
1998 | 2003 | 2008 | 2011 | |||||
CA-MC | MLP-MC | CA-MC | MLP-MC | CA-MC | MLP-MC | CA-MC | MLP-MC | |
Agriculture | 6359.45 | 6339.11 | 6075.25 | 6067.47 | 5399.02 | 5478.06 | 5027.78 | 5101.09 |
Water | 74.96 | 81.25 | 77.86 | 76.58 | 79.76 | 82.47 | 93.99 | 97.51 |
Urban | 1184.09 | 1192.15 | 1468.49 | 1489.37 | 2031.3 | 2055.99 | 2429.72 | 2424.47 |
Forest | 41.03 | 52.42 | 36.83 | 15.77 | 52.53 | 53.54 | 29.2 | 33.65 |
Miscellaneous | 35.36 | 28.32 | 36.46 | 44.06 | 131.87 | 23.19 | 114.66 | 36.53 |
Types | CA-MC | MLP-MC | Extrapolation | SD |
---|---|---|---|---|
Overall accuracy | 90.4473 | 90.8636 | 93.5340 | 94.6711 |
Kstandard | 0.8908 | 0.8949 | 0.9212 | 0.9324 |
Kno | 0.9113 | 0.9148 | 0.9364 | 0.9451 |
KlocationStrata | 0.9706 | 0.9634 | 0.9588 | 0.9754 |
LULC Classes | Landsat 8 | CA-MC | MLP-MC | Extrapolation | SD |
---|---|---|---|---|---|
Agriculture | 4368.03 | 3715.57 | 3715.6 | 3814.48 | 4265.74 |
Water | 123.56 | 138.47 | 147.58 | 148.48 | 123.01 |
Urban | 3179.87 | 3761.32 | 3750.99 | 3677.12 | 3254.88 |
Forest | 8.76 | 56.44 | 57.38 | 49.66 | 43.38 |
Miscellaneous | 13.03 | 21.45 | 21.70 | 3.51 | 6.24 |
LULC Classes | Extrapolation | SD | ||
---|---|---|---|---|
km | % | km | % | |
Agriculture | 2478.6 | 32.22 | 3735.72 | 48.56 |
Water | 217.19 | 2.82 | 147.67 | 1.92 |
Urban | 4896.27 | 63.64 | 3750.77 | 48.75 |
Forest | 98.55 | 1.28 | 57.45 | 0.75 |
Miscellaneous | 2.64 | 0.03 | 1.64 | 0.02 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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Losiri, C.; Nagai, M.; Ninsawat, S.; Shrestha, R.P. Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models. Sustainability 2016, 8, 686. https://doi.org/10.3390/su8070686
Losiri C, Nagai M, Ninsawat S, Shrestha RP. Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models. Sustainability. 2016; 8(7):686. https://doi.org/10.3390/su8070686
Chicago/Turabian StyleLosiri, Chudech, Masahiko Nagai, Sarawut Ninsawat, and Rajendra P. Shrestha. 2016. "Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models" Sustainability 8, no. 7: 686. https://doi.org/10.3390/su8070686