Abstract
Cellular Automata (CA) simulation models have been increasingly used in land use studies. However, neighborhood configuration, an essential element of CA model, remarkably impacts the accuracy of simulated results. Moreover, errors from data source may propagate through the CA modeling process. The objective of this study is to analyze the effect of neighborhood configuration to CA model and further on to explore its capacity of resisting disturbance from data source error. With statistic-based CA model and several neighborhood configurations respectively, the land use changes of Wuhan, China were analyzed. It is demonstrated that there are significant differences on the simulated results produced by different neighborhoods. Besides, different neighborhoods respond differently to data source error. In light of these results, we find out that (1) neighborhood configurations with larger neighborhood size and planar neighborhood type, introduced in this paper, contribute to higher prediction accuracy; and (2) the neighborhood configurations above also have higher capacity of resisting disturbance from data source error and give rise to more stable simulated results. This study provides a comprehensive basis for scale selection of CA model with a meaningful consideration of data source error and thus will improve the research on land use change.
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References
Batty M, Xie Y, Sun Z (1999) Modeling urban dynamics through GIS-based cellular automata. Comput Environ Urban Syst 23(3):203–233
Chen Q, Mynett AE, Minns AW (2002) Application of cellular automata to modelling competitive growths of two underwater species Chara aspera and Potamogeton pectinatus in Lake Veluwe. Ecol Model 147(3):253–265
Clarke KC, Gaydos LJ (1998) Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inform Sci: IJGIS 12(7):699–714
Couclelis H (1985) Cellular worlds - a framework for modeling micro–macro dynamics. Environ Plann 17(5):585–596
Craig PD (2010) Imposed and inherent scales in cellular automata models of habitat. Ecol Model 221(20):2425–2434
Cushman SA, Landguth EL (2010) Scale dependent inference in landscape genetics. Landsc Ecol 25(6):967–979
Deng JS, Wang K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape patteern in response to rapid urganization. Landsc Urban Plann 92:187–198
Fonstad MA (2006) Cellular automata as analysis and synthesis engines at the geomorphology-ecology interface. Geomorphology 77(3–4):217–234
Gaucherel C (2009) Self-similar land cover heterogeneity of temperate and tropical landscapes. Ecol Complex 6(3):346–352
Hagoort M, Geertman S, Ottens H (2007) Spatial externalities, neighbourhood rules and CA land-use modelling. Ann Reg Sci 42(1):39–56
Hecht B, Moxley E (2009) Terabytes of Tobler: evaluating the first law in a massive. Domain-neutral representation of world knowledge. Spatial Inform Theory 5756:88–105, Springer
Karssenberg D, De Jong K (2005) Dynamic environmental modelling in GIS: 2. Modelling error propagation. Int J Geogr Inf Sci 19(6):623–637
Kavzoglu T (2009) Increasing the accuracy of neural network classification using refined training data. Environ Model Softw 24(7):850–858
Keith CC, Leonard JG (1998) Loose-coupling a cellular automata model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12(7):699–714
Kocabas V, Dragicevic S (2006) Assessing cellular automata model behaviour using a sensitivity analysis approach. Comput Environ Urban Syst 30(6):921–953
Lai T, Dragicevic S (2011) Development of an urban landslide cellular automata model: a case study of North Vancouver, Canada. Earth Sci Informatics 4(2):69–80
Li X, Yeh AG (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16(4):323–343
Li X, Yang QS, Liu XP (2008) Discovering and evaluating urban signatures for simulating compact development using cellular automata. Landsc Urban Plann 86(2):177–186
Liu X, Li X, Shi X, Wu S, Liu T (2008) Simulating complex urban development using kernel-based non-linear cellular automata. Ecol Model 211(1–2):169–181
Menard A, Marceau DJ (2005) Exploration of spatial scale sensitivity in geographic cellular automata. Environ Plann Plann Des 32(5):693–714
Myint SW, Wang L (2006) Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Can J Remote Sens 32(6):390–404
Myint SW, Jain J, Lukinbeal C, Lara-Valencia F (2010) Simulating urban growth on the US-Mexico border: Nogales, Arizona, and Nogales, Sonora. Can J Remote Sens 36(3):166–184
Overmars KP, de Koning GHJ, Veldkamp A (2003) Spatial autocorrelation in multi-scale land use models. Ecol Model 164:257–270
Pan Y, Roth A, Yu Z, Doluschitz R (2010) The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Comput Environ Urban Syst 34(5):400–408
Santé I, García AM, Miranda D, Crecente R (2010) Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landsc Urban Plann 96(2):108–122
Sun T, Wang J (2007) A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation. Simulat Model Pract Theor 15(7):864–878
Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240
Tobler WR (1979) Cellular geography. In Philosophy in geography. D. Reidel Publishing Company, pp 379–386
Veldkamp A, Verburg PH, Kok K, de Koning GHJ, Priess J, Bergsma AR (2001) The need for scale sensitive approaches in spatially explicit land use change modeling. Environ Model Assess 6(2):111–121
Verburg PH, Nijs TCM, Eck JR, Visser H, Jong K (2004) A method to analyse neighborhood characteristics of land use patterns. Comput Environ Urban Syst 28(6):667–690
Wang Y, Li S (2011) Simulating multiple class urban land-use/cover changes by RBFN-based CA model. Comput Geosci 37(2):111–121
Wang J, Chen YQ, He T, Lv CY, Liu AX (2010) Application of geographic image cognition approach in land type classification using Hyperion image: a case study in China. Int J Appl Earth Obs Geoinform 12:S212–S222
White R, Engelen G (1993) Cellular automata and fractal urban form: a cellular modelling approach to the evaluation of urban land-use patterns. Environ Plann 25:1175–1199
White R, Engelen G (1997) Cellular automata as the basis of integrated dynamic regional modeling. Environ Plann Plann Des 24:235–246
Wu DQ, Liu J, Zhang GS, Ding WJ, Wang W, Wang RQ (2009) Incorporating spatial autocorrelation into cellular automata model: an application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.). Ecol Model 220(24):3490–3498
Wu H, Chen X, Li Z, Wang S, Cui W, Meng Q (2010) Identifying spatial patterns of land use and cover change at different scales based on self-organizing map. Adv Neural Netw Res Appl 67:355–361, Springer
Yang Q, Li X, Shi X (2008) Cellular automata for simulating land use changes based on support vector machines. Comput Geosci 34(6):592–602
Yeh A, Li X (2006) Errors and uncertainties in urban cellular automata. Comput Environ Urban Syst 30(1):10–28
Acknowledgements
We are greatly thankful to anonymous reviewers for the constructive suggestions for improving the manuscript. And special thanks to Prof. Diansheng Guo (the Editorial Board of The Professional Geographer) for linguistic advice. This work is supported by National Natural Science Foundation of China (Grant NO.40901214), the Fundamental Research Funds for the Central Universities (Grant NO.2010-Ia-015) and the Youth Chenguang Project of Science and Technology of Wuhan City of China (Grant NO.201150431093).
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Communicated by: H. A. Babaie
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Wu, H., Zhou, L., Chi, X. et al. Quantifying and analyzing neighborhood configuration characteristics to cellular automata for land use simulation considering data source error. Earth Sci Inform 5, 77–86 (2012). https://doi.org/10.1007/s12145-012-0097-8
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DOI: https://doi.org/10.1007/s12145-012-0097-8