Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Environmental Modelling and Software

Volumn 75, Issue , 2016, Pages 163-175

  Liao, Jiangfu ^a,b   Tang, Lina ^a   Shao, Guofan ^a,c   Su, Xiaodan ^a   Chen, Dingkai ^a   Xu, Tong ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b JIMEI UNIVERSITY   (China)

^c PURDUE UNIVERSITY   (United States)

Author keywords

Cellular automata; Extended enrichment factors; Neighborhood effects; Neighborhood rules; Urban expansion

Indexed keywords

CELLULAR AUTOMATA; SENSITIVITY ANALYSIS;

CELLULAR AUTOMATA SIMULATIONS; ENRICHMENT FACTORS; EXTENDED NEIGHBORHOODS; NEIGHBORHOOD EFFECTS; NEIGHBORHOOD RULES; PARAMETER SENSITIVITY ANALYSIS; URBAN CELLULAR AUTOMATON; URBAN EXPANSION;

LAND USE;

CELLULAR AUTOMATON; LAND USE; LOGISTICS; NEIGHBORHOOD; QUANTITATIVE ANALYSIS; REGRESSION ANALYSIS; SIMULATION; SPATIAL ANALYSIS; URBAN DEVELOPMENT; URBAN REGION;

CHINA; FUJIAN; XIAMEN;

EID: 84945390718     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2015.10.014     Document Type: Article

References (72)

1. Al-Ahmadi K., See L., Heppenstall A., Hogg J. Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecol. Complex. 2009, 6(2):80-101.
2. Arsanjani J.J., Helbich M., Kainz W., Darvishi Boloorani A. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J. Appl. Earth Obs. Geoinf. 2013, 21:265-275.
3. Batty M., Xie Y. From cells to cities. Environ. Plan. B Plan. Des. 1994, 21:531-548.
4. Bennett N.D., Croke B.F.W., Guariso G., Guillaume J.H.A., Hamilton S.H., Jakeman A.J., Marsili-Libelli S., Newham L.T.H., Norton J.P., Perrin C., Pierce S.A., Robson B., Seppelt R., Voinov A.A., Fath B.D., Andreassian V. Characterising performance of environmental models. Environ. Model. Softw. 2013, 40:1-20.
5. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer, New York.
6. Chen B., Xu H. Urban expansion and its driving force analysis using remote sensed data a case of Xiamen city. Econ. Geogr. 2005, 25(1):79-83.
7. Chen H., Pontius R.G. Sensitivity of a land change model to pixel resolution and precision of the independent variable. Environ. Model. Assess. 2010, 16(1):37-52.
8. Cheng J., Masser I. Urban growth pattern modeling: a case study of Wuhan city, PR China. Landsc. Urban Plan. 2003, 62(4):199-217.
9. Clarke K., Hoppen S., Gaydos L. A self-modifying cellular automaton model of historical. Environ. Plan. B 1997, 24:247-261.
10. Clarke K.C., Gaydos L.J. Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int. J. Geogr. Inf. Sci. 1998, 12(7):699-714.
11. Couclelis H. Of mice and men: what rodent populations can teach us about complex spatial dynamics. Environ. Plan. A 1988, 20(1):99-109.
12. Dai E., Wu S., Shi W., Cheung C.-k, Shaker A. Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach. Environ. Manag. 2005, 36(4):576-591.
13. Dendoncker N., Rounsevell M., Bogaert P. Spatial analysis and modelling of land use distributions in Belgium. Comput. Environ. Urban Syst. 2007, 31(2):188-205.
14. Feng Y., Liu Y. A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing. Int. J. Geogr. Inf. Sci. 2013, 27(3):449-466.
15. Feng Y., Liu Y., Tong X., Liu M., Deng S. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landsc. Urban Plan. 2011, 102(3):188-196.
16. Fuglsang M., Münier B., Hansen H.S. Modelling land-use effects of future urbanization using cellular automata: an eastern Danish case. Environ. Model. Softw. 2013, 50:1-11.
17. García A.M., Santé I., Boullón M., Crecente R. A comparative analysis of cellular automata models for simulation of small urban areas in Galicia, NW Spain. Comput. Environ. Urban Syst. 2012, 36(4):291-301.
18. Guan D., Li H., Inohae T., Su W., Nagaie T., Hokao K. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol. Model. 2011, 222(20):3761-3772.
19. Hagoort M., Geertman S., Ottens H. Spatial externalities, neighbourhood rules and CA land-use modelling. Ann. Reg. Sci. 2008, 42(1):39-56.
20. Hansen H.S. An adaptive land-use simulation model for integrated coastal zone planning. The European Information Society 2007, 35-53. Springer.
21. Hansen H.S. Quantifying and analysing neighbourhood characteristics supporting urban land-use modelling. The European Information Society 2008, 283-299. Springer.
22. Hansen H.S. Empirically derived neighbourhood rules for urban land-use modelling. Environ. Planning-Part B 2012, 39(2):213.
23. Harrop K.J. Nuisances and Their Externality Fields 1973, Department of Geography, University of Newcastle Upon Tyne.
24. Hewitt R., Van Delden H., Escobar F. Participatory land use modelling, pathways to an integrated approach. Environ. Model. Softw. 2014, 52:149-165.
25. Hirzel A., Guisan A. Which is the optimal sampling strategy for habitat suitability modelling. Ecol. Model. 2002, 157(2):331-341.
26. Hu Z., Lo C. Modeling urban growth in Atlanta using logistic regression. Comput. Environ. Urban Syst. 2007, 31(6):667-688.
27. Huang B., Zhang L., Wu B. Spatiotemporal analysis of rural-urban land conversion. Int. J. Geogr. Inf. Sci. 2009, 23(3):379-398.
28. Kocabas V., Dragicevic S. Enhancing a GIS cellular automata model of land use change: Bayesian networks, influence diagrams and causality. Trans. GIS 2007, 11(5):681-702.
29. Krugman P. The role of geography in development. Int. Reg. Sci. Rev. 1999, 22(2):142-161.
30. Lagarias A. Urban sprawl simulation linking macro-scale processes to micro-dynamics through cellular automata, an application in Thessaloniki, Greece. Appl. Geogr. 2012, 34:146-160.
31. Lauf S., Haase D., Hostert P., Lakes T., Kleinschmit B. Uncovering land-use dynamics driven by human decision-making-a combined model approach using cellular automata and system dynamics. Environ. Model. Softw. 2012, 27:71-82.
32. Li X., Yeh A.G.-O. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. Int. J. Geogr. Inf. Sci. 2000, 14(2):131-152.
33. Li X., Yeh A.G.-O. Integration of principal components analysis and cellular automata for spatial decisionmaking and urban simulation. Sci. China Ser. D Earth Sci. 2002, 45(6):521-529.
34. Li X., Yeh A.G.-O. Data mining of cellular automata's transition rules. Int. J. Geogr. Inf. Sci. 2004, 18(8):723-744.
35. Li X., Liu X., Yu L. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. Int. J. Geogr. Inf. Sci. 2014, 28(7):1317-1335.
36. Liao J., Tang L., Shao G., Qiu Q., Wang C., Zheng S., Su X. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence. Int. J. Geogr. Inf. Sci. 2014, 28(4):720-738.
37. Liu X., Li X., Yeh A.G.-O., He J., Tao J. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Sci. China Ser. D Earth Sci. 2007, 50(10):1578-1588.
38. Liu X., Li X., Liu L., He J., Ai B. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. Int. J. Geogr. Inf. Sci. 2008, 22(11-12):1247-1269.
39. Liu X., Li X., Shi X., Wu S., Liu T. Simulating complex urban development using kernel-based non-linear cellular automata. Ecol. Model. 2008, 211(1-2):169-181.
40. Liu X., Ma L., Li X., Ai B., Li S., He Z. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. Int. J. Geogr. Inf. Sci. 2014, 28(1):148-163.
41. Liu Y., Yang S., Chen J. Modeling environmental impacts of urban expansion: a systematic method for dealing with uncertainties. Environ. Sci. Technol. 2012, 46(15):8236-8243.
42. Mas J.-F., Kolb M., Paegelow M., Camacho Olmedo M.T., Houet T. Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ. Model. Softw. 2014, 51:94-111.
43. McCullagh P., Nelder J. Generalized Linear Models 1989, CRC Press, Boca Raton.
44. McMillen D.P. An empirical model of urban fringe land use. Land Econ. 1989, 138-145.
45. Munroe D.K., Southworth J., Tucker C.M. Modeling spatially and temporally complex land-cover change: the case of western Honduras*. Prof. Geogr. 2004, 56(4):544-559.
46. Pan Y., Roth A., Yu Z., Doluschitz R. The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Computers. Environ. Urban Syst. 2010, 34(5):400-408.
47. Pontius J., Robert G., Huffaker D., Denman K. Useful techniques of validation for spatially explicit land-change models. Ecol. Model. 2004, 179(4):445-461.
48. Rodrigue J.P. Transport Geography on TheWeb 2004, Department of Economics and Geography, Hofstra University, New York.
49. Santé I., García A.M., Miranda D., Crecente R. Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc. Urban Plan. 2010, 96(2):108-122.
50. Shaker R.R. The well-being of nations: an empirical assessment of sustainable urbanization for Europe. Int. J. Sustain. Dev. World Ecol. 2015, 22(5):375-387.
51. Stevens D., Dragicevic S., Rothley K. iCity: a GIS-CA modelling tool for urban planning and decision making. Environ. Model. Softw. 2007, 22(6):761-773.
52. Takeyama M., Couclelis H. Map dynamics: integrating cellular automata and GIS through geo-algebra. Int. J. Geogr. Inf. Sci. 1997, 11(1):73-91.
53. Tang L., Zhao Y., Yin K., Zhao J. City Profile: Xiamen. Cities 2013, 31:615-624.
54. Tang L., Su X., Shao G., Zhang H., Zhao J. A Clustering-Assisted Regression (CAR) approach for developing spatial climate data sets in China. Environ. Model. Softw. 2012, 38:122-128.
55. Tobler W. Cellular geography. Philosophy in Geography 1979, 379-386. Springer.
56. Van Vliet J., Naus N., Van Lammeren R.J., Bregt A.K., Hurkens J., Van Delden H. Measuring the neighbourhood effect to calibrate land use models. Comput. Environ. Urban Syst. 2013, 41:55-64.
57. Verburg P.H., Van Eck J.R., de Nijs T.C., Dijst M.J., Schot P. Determinants of land-use change patterns in the Netherlands. Environ. Plan. B 2004, 31(1):125-150.
58. Verburg P.H., de Nijs T., Ritsema van Eck J., Visser H., de Jong K. A method to analyse neighbourhood characteristics of land use patterns. Comput. Environ. urban Syst. 2004, 28(6):667-690.
59. Verburg P.H., Soepboer W., Veldkamp A., Limpiada R., Espaldon V., Mastura S.S. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ. Manag. 2002, 30(3):391-405.
60. Verstegen J.A., Karssenberg D., Van Der Hilst F., Faaij A.P. Identifying a land use change cellular automaton by Bayesian data assimilation. Environ. Model. Softw. 2014, 53:121-136.
61. Wang H., He S., Liu X., Dai L., Pan P., Hong S., Zhang W. Simulating urban expansion using a cloud-based cellular automata model: a case study of Jiangxia, Wuhan, China. Landsc. Urban Plan. 2013, 110:99-112.
62. White R., Engelen G. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environ. Plan. A 1993, 25(8):1175-1199.
63. White R., Engelen G. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput. Environ. urban Syst. 2000, 24(5):383-400.
64. White R., Engelen G. A calibration procedure for constrained large neighbourhood cellular automata based land use models. 13th European Colloquium on Theoretical and Quantitative Geography, Lucca, Italy 2003.
65. White R., Engelen G., Uljee I. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environ. Plan. B 1997, 24:323-344.
66. Wu F. Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int. J. Geogr. Inf. Sci. 2002, 16(8):795-818.
67. Wu F., Webster C.J. Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environ. Plan. B 1998, 25:103-126.
68. XCSB Xiamen City Statistics Bureau and National Bureau of Statistics of China (XCSB). Yearbook of Xiamen Special Economic Zone in 2008 2008, China Statistics Press, Beijing. http://www.stats-xm.gov.cn/2008/.
69. XCSB Xiamen City Statistics Bureau and National Bureau of Statistics of China (XCSB). Yearbook of Xiamen Special Economic Zone in 2011 2011, China Statistics Press, Beijing. http://www.stats-xm.gov.cn/2011/.
70. Xie C., Huang B., Claramunt C., Chandramouli C. Spatial logistic regression and gis to model rural-urban land conversion. Proceedings of PROCESSUS Second International Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation Models: Frameworks, Models and Applications 2005, 12-15.
71. Yang Q., Li X., Shi X. Cellular automata for simulating land use changes based on support vector machines. Comput. Geosci. 2008, 34(6):592-602.
72. Yeh A.G.-O., Li X. Errors and uncertainties in urban cellular automata. Comput. Environ. Urban Syst. 2006, 30(1):10-28.