A cellular automata model based on nonlinear kernel principal component analysis for urban growth simulation

Environment and Planning B: Planning and Design

Volumn 40, Issue 1, 2013, Pages 117-134

  Feng, Yongjiu ^a   Liu, Yan ^b  

^a SHANGHAI OCEAN UNIVERSITY   (China)

^b UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Cellular automata; GIS; Kernel principal component analysis; Transition rules; Urban growth simulation

Indexed keywords

CELLULAR AUTOMATON; COMPUTER SIMULATION; GIS; MODELING; NONLINEARITY; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; URBAN GROWTH;

CHINA; SHANGHAI;

EID: 84873492089     PISSN: 02658135     EISSN: 14723417     Source Type: Journal    
DOI: 10.1068/b37142     Document Type: Article

References (52)

1. Al-kheder S, Wang J, Shan J, 2008, Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images International Journal of Geographical Information Science 22 1271-1293
2. Almeida C M, Gleriani J M, Castejon E F, Soares-Filho B S, 2008, Using neural networks and cellular automata for modelling intra-urban land-use dynamics International Journal of Geographical Information Science 22 943-963
3. Barredo J I, Marjo K, Niall M C, Carlo L, 2003, Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata Landscape and Urban Planning 64 145-160
4. Batty M, Xie Y, 1994, From cells to cities Environment and Planning B: Planning and Design 21 531-548
5. Batty M, Xie Y, Sun Z, 1999, Modelling urban dynamics through GIS-based cellular automata Computers, Environment and Urban Systems 23 205-233
6. Camastra F, Verri A, 2005, A novel kernel method for clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 27 801-804
7. Chen Q, Mynett A E, 2003, Effects of cell size and configuration in cellular automata based preypredator modelling Simulation Modelling Practice and Theory 11 609-625
8. Clarke K C, Gaydos L J, 1998, Loose-coupling of a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/ Baltimore International Journal of Geographical Information Science 12 699-714
9. Clarke K C, Gaydos L J, Hoppen S, 1997, A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area Environment and Planning B: Planning and Design 24 247-261
10. Couclelis H, 1997, From cellular automata to urban models: new principles for model development and implementation Environment and Planning B: Planning and Design 24 165-174
11. Cristianini N, Shawe-Taylor J, 2000 An Introduction to Support Vector Machines and Other Kernelbased Learning Methods (Cambridge University Press, Cambridge)
12. Dietzel C, Clarke K C, 2006, The effect of disaggregating land use categories in cellular automata during model calibration and forecasting Computers, Environment and Urban Systems 30 78-101
13. Dragićević S, Marceau D J, Marois C, 2001, Space, time, and dynamics modeling in historical GIS databases: a fuzzy logic approach Environment and Planning B: Planning and Design 28 545-562
14. Feng Y, Liu Y, Tong X, Liu M, Deng S, 2011, Modeling dynamic urban growth using cellular automata and particle swarm optimization rules Landscape and Urban Planning 102 188-196
15. He C, Norio O, Zhang Q, Shi P, Zhang J, 2006, Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China Applied Geography 26 323-345
16. Hu Z, Lo C P, 2007, Modeling urban growth in Atlanta using logistic regression Computers, Environment and Urban Systems 31 667-688
17. Huang B, Xie C L, Tay R, Wu B, 2009, Land-use-change modeling using unbalanced supportvector machines Environment and Planning B: Planning and Design 36 398-416
18. Kocabas V, Dragićević S, 2006, Assessing cellular automata model behaviour using a sensitivity analysis approach Computers, Environment and Urban Systems 30 921-953
19. Li J, Li X, Tao D, 2008, KPCA for semantic object extraction in images Pattern Recognition 41 3244-3250
20. Li X, Yeh A G-O, 2002a, Urban simulation using principal components analysis and cellular automata for land-use planning Photogrammetric Engineering and Remote Sensing 68 341-351
21. Li X, Yeh A G-O, 2002b, Neural-network based cellular automata for simulating multiple land use changes using GIS International Journal of Geographical Information Science 16 323-343
22. Liu X, Li X, Liu L, He J, Ai B, 2008a, A bottom-up approach to discover transition rules of cellular automata using ant intelligence International Journal of Geographical Information Science 22 1247-1269
23. Liu X, Li X, Shi X, Wu S, Liu T, 2008b, Simulating complex urban development using kernel based non-linear cellular automata Ecological Modelling 211 169-181
24. Liu Y, 2008 Modelling Urban Development with Geographical Information Systems and Cellular Automata (CRC Press, New York)
25. Liu Y, Phinn S R, 2003, Modelling urban development with cellular automata incorporating fuzzyset approaches Computers, Environment and Urban Systems 27 637-658
26. Luo J, Wei Y H D, 2009, Modelling spatial variations of urban growth patterns in Chinese cities: the case of Nanjing Landscape and Urban Planning 91 51-64
27. Moreno N, Wang F, Marceau D J, 2009, Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model Computers, Environment and Urban System 33 44-54
28. Pontius R G, Spencer J, 2005, Uncertainty in extrapolations of predictive land-change models Environment and Planning B: Planning and Design 32 211-230
29. Pontius R G, Huffaker D, Denman K, 2004, Useful techniques of validation for spatially-explicit land-change models Ecological Modelling 179 445-461
30. Sahbi H, 2007, Kernel PCA for similarity invariant shape recognition Neurocomputing 70 3034-3045
31. Schölkopf B, Smola A, Müller K-R, 1997, Kernel principal component analysis Lecture Notes in Computer Science 1327 583-588
32. Schölkopf B, Smola A, Müller K-R, 1998, Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 1299-1319
33. Shawe-Taylor J, Cristianini N, 2004 Kernel Methods for Pattern Analysis (Cambridge University Press, Cambridge)
34. Stevens D, Dragićević S, Rothley K, 2007, iCity: a GIS-CA modelling tool for urban planning and decision making Environmental Modelling and Software 22 761-773
35. Takeshi A, Tetsuya A, 2004, Empirical analysis for estimating land use transition potential functions: case in the Tokyo metropolitan region Computers, Environment and Urban Systems 28 65-84
36. Teixeira A R, Tomé A M, Stadlthanner K, Lang E W, 2008, KPCA denoising and the pre-image problem revisited Digital Signal Processing 18 568-580
37. Vapnik V N, 1998 Statistical Learning Theory (John Wiley, New York)
38. Verburg P H, de Nijs T C M, van Eck J R, Visser H, de Jong K, 2004, A method to analyse neighbourhood characteristics of land use patterns Computers, Environment and Urban Systems 28 667-690
39. Wang F, Hasbani J G, Wang X, Marceau D J, 2011, Identifying dominant factors for the calibration of a land-use cellular automata model using Rough Set Theory Computers, Environment and Urban Systems 35 116-125
40. Wang H, 1999 Partial Least Squares Regression: Method and Applications (Defense Industry Press of China, Beijing)
41. Wang H, Leng Y, Wang Z, Wu X, 2007, Application of image correction and bit-plane fusion in generalised PCA based face recognition Pattern Recognition Letters 28 2352-2358
42. Ward D P, Murray A T, Phinn S R, 2000, A stochastically constrained cellular model of urban growth Computers, Environment and Urban Systems 24 539-558
43. Wei Y D, Chi K L, Jun L, 2006, Globalising Shanghai: foreign investment and urban restructuring Habitat International 30 231-244
44. White E M, Anita T M, Ralph J A, 2009, Past and projected rural land conversion in the US at state, regional, and national levels Landscape and Urban Planning 89 37-48
45. White R, Engelen G, 1993, Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns Environment and Planning A 25 1175-1199
46. White R, Engelen G, Uijee I, 1997, The use of constrained cellular automata for high-resolution modelling of urban land use dynamics Environment and Planning B: Planning and Design 24 323-343
47. Wu F, 1998, SimLand: a prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules International Journal of Geographical Information Science 12 63-82
48. Wu F, 2002, Calibration of stochastic cellular automata: the application to rural-urban land conversions International Journal of Geographical Information Science 16 795-818
49. Wu F, Webster C J, 1998, Simulation of land development through the integration of cellular automata and multicriteria evaluation Environment and Planning B: Planning and Design 25 103-126
50. Yang Q, Li X, Shi X, 2008, Cellular automata for simulating land use changes based on support vector machines Computers and Geosciences 34 592-602
51. Yeh A G-O, Li X, 2006, Errors and uncertainties in urban cellular automata Computers, Environment and Urban Systems 30 10-28
52. Zhang K, Wang R, Shen C, Da L, 2010, Temporal and spatial characteristics of the urban heat island during rapid urbanization in Shanghai, China Environmental Monitoring and Assessment 169 101-112