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Core Principles and Concepts in Land-Use Modelling: A Literature Review

  • Jonas van Schrojenstein LantmanEmail author
  • Peter H. Verburg
  • Arnold Bregt
  • Stan Geertman
Chapter
Part of the GeoJournal Library book series (GEJL, volume 101)

Abstract

Simulation models of land use predict or describe land-use change over space and time. Recent overviews of land-use simulation models show an overwhelming amount of different types of models and applications (Heistermann, Muller & Ronneberger, 2006; Koomen, Stillwell, Bakema & Scholten, 2007; Verburg, Schot, Dijst & Veldkamp, 2004). Obviously, such models are simplifications of reality, but increasing computing power over the years has made it possible to incorporate more and more complexity in such models. This increased complexity, however, tends to obscure the theoretical foundations of land-use simulation models. This theoretical foundation relates to the core principles that are used to explain land-use change and the concepts that are applied to translate these principles into a functioning model of land-use change. An in-depth review of land-use change concepts, their underlying principles, applicability and translation into actual models does not exist to our knowledge. In this chapter we aim, therefore, to analyse the application of various theoretical concepts of land-use change that are used in modelling. This analysis is a first step to better understand the conceptual background of land-use change and the application of these concepts in computer simulation models. Based on this review we present some observations on important research issues in land-use modelling and suggest possible ways for further model improvement.

Keywords

Cellular Automaton Cellular Automaton Neighbourhood Interaction Core Principle Cellular Automaton Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik., C. M. (2001). A review and assessment of land-use change models: Dynamics of space, time, and human choice. Bloomington, IN: South-Burlington, Center for the Study of Institutions Population, and Environmental Change, Indiana University.Google Scholar
  2. Alcamo, J., Kreileman, G. J. J., Krol, M. S., & Zuidema, G. (1994a). Modeling the global society-biosphere-climate system: Part 1: Model description and testing. Water, Air, & Soil Pollution, 76(1–2), 1–35.CrossRefGoogle Scholar
  3. Alcamo, J. G. J., van den Born, A. F., Bouwman, B. J., de Haan, K., Klein Goldewijk, O., Klepper, J., et al. (1994b). Modeling the global society-biosphere-climate system: Part 2: Computed scenarios. Water, Air, & Soil Pollution, 76(1–2), 37–78.CrossRefGoogle Scholar
  4. Alonso, W. F. (1964). Location and land use. New Haven, CT: Harvard University Press.Google Scholar
  5. Arentze, T. A., & Timmermans, H. J. P. (2000). Albatross, a learning based transportation oriented simulation system. Eindhoven: Eindhoven University.Google Scholar
  6. Baker, W. L. (1989). A review of models of landscape change. Landscape Ecology, 2(2), 111–133.CrossRefGoogle Scholar
  7. Balmann, A. (1996). Farm-based modelling of regional structural change: A cellular automata approach. European Review of Agricultural Economics, 24, 85–108.Google Scholar
  8. Batty, M., & Xie, Y. (1994). From cells to cities. Environment & Planning B: Planning & Design, 21, S31.CrossRefGoogle Scholar
  9. Bella, I. E. (1971). A new competition model for individual trees. Forest Science, 17(3), 364–372.Google Scholar
  10. Benenson, I. (2007). Warning! The scale of land-use CA is changing! Computers, Environment and Urban Systems, 31(2), 107–113.CrossRefGoogle Scholar
  11. Borsboom-van Beurden, J. A. M., Boersma, W. T., Bouwman, A. A., Crommentuijn, L. E. M., Dekkers, J. E. C., & Koomen., E. (2005). Spatial impressions – Visualisation of future land use in the Netherlands. Bilthoven – The Netherlands, Netherlands Environmental Assesment Agency–550016003/2005.Google Scholar
  12. Briassoulis, H. (2000). Analysis of Land use change: Theoretical and modeling approaches. Morgantown, WV: West Virginia University.Google Scholar
  13. Brown, D. G., Page, S., Riolo, R., Zellner, M., & Rand, W. (2005). Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19(2), 153–174.CrossRefGoogle Scholar
  14. Bürgi, M., Hersperger, A. M., & Schneeberger, N. (2005). Driving forces of landscape change – Current and new directions. Landscape Ecology, 19(8), 857–868.CrossRefGoogle Scholar
  15. Burnham, B. O. (1973). Markov intertemporal land use simulation model. Southern Journal of Agricultural Economics, 5(1), 253–258.Google Scholar
  16. Chomitz, K. M., & Gray, D. A. (1996). Roads, land use, and deforestation: A spatial model applied to Belize. World Bank Economic Review, 10(3), 487–512.Google Scholar
  17. Couclelis, H. (1985). Cellular worlds: A framework for modeling micro – macro dynamics. Environment and Planning A, 17(5), 585–596.CrossRefGoogle Scholar
  18. deMaris, A. (1992). Logit modeling: Practical applications, University of Iowa–07–086.Google Scholar
  19. de Nijs, T. C. M., de Niet, R., & Crommentuijn, L. (2004). Constructing land-use maps of the Netherlands in 2030. Journal of Environmental Management, 72(1–2), 35–42.Google Scholar
  20. Dungan, J. L., Perry, J. N., Dale, M. R. T., Legendre, P., Citron-Pousty, S., Fortin, M. J., et al. (2002). A balanced view of scale in spatial statistical analysis. Ecography, 25(5), 626–640.CrossRefGoogle Scholar
  21. Engelen, G., White, R., Uljee, I., & Drazan, P. (1995). Using cellular automata for integrated modelling of socio-environmental systems. Environmental Monitoring and Assessment, 34(2), 203–214.CrossRefGoogle Scholar
  22. Fearnside, P. M. (1996). Amazonian deforestation and global warming: Carbon stocks in vegetation replacing Brazil’s Amazon forest. Forest Ecology and Management, 80(1–3), 21–34.CrossRefGoogle Scholar
  23. Ferrand, N. (1996). Modelling and supporting multi-actor planning using multi-agents systems. Santa Barbara, CA: Third NCGIA Conference on GIS and Environmental Modelling.Google Scholar
  24. Fujita, M., Krugman, P., & Venables, A. J. (1999). The spatial economy. London: The MIT press.Google Scholar
  25. Gardner, M. (1970). Mathematical Games: The fantastic combinations of John Conway’s new solitaire game ‘life’. Scientific American, 223, 120–123.CrossRefGoogle Scholar
  26. Geertman, S., Hagoort, M., & Ottens, H. (2007). Spatial-temporal specific neighbourhood rules for cellular automata land-use modelling. International Journal of Geographical Information Science, 21(5), 547–568.CrossRefGoogle Scholar
  27. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., et al. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1–2), 115–126.CrossRefGoogle Scholar
  28. Hagoort, M. (2006). The Neighbourhood Rules. Land-use interaction, urban dynamics and cellular automata modeling (300p). PhD, Faculty of geosciences, Utrecht University, Utrecht.Google Scholar
  29. Heistermann, M., Muller, C., & Ronneberger, K. (2006). Land in sight?: Achievements, deficits and potentials of continental to global scale land-use modeling. Agriculture, Ecosystems & Environment, 114(2–4), 141–158.CrossRefGoogle Scholar
  30. Hilferink, M., & Rietveld, P. (1999). LAND USE SCANNER: An integrated GIS based model for long term projections of land use in urban and rural areas. Journal of Geographical Systems, 1(2), 155–177.CrossRefGoogle Scholar
  31. Hunt, J. D., Kriger, D. S., & Miller, E. J. (2004). Current operational urban land-use-transport modelling frameworks: A REVIEW. Transport Reviews, 25(3), 329–376.CrossRefGoogle Scholar
  32. Koomen, E., Loonen, W., & Hilferink, M. (2008). Climate-change adaptations in land-use planning; A scenario-based approach. In L. Bernard, A. Friis-Christensen, & H. Pundt (Eds.), The European information society; Taking geoinformation science one step further (pp. 261–282). Berlin: Springer.Google Scholar
  33. Koomen, E., & Stillwell, J. (2007). Modelling land-use change; Theories and methods. Chapter 1. In E. Koomen, J. Stillwell, A. Bakema, & H. J. Scholten (Eds.), Modelling land-use change; Progress and applications (pp. 1–21). Dordrecht: Springer.CrossRefGoogle Scholar
  34. Koomen, E., J. Stillwell, A. Bakema, & H. J. Scholten, (Eds.). (2007). Modelling land-use change. Progress and applications. Dordrecht, The Netherlands: Springer.Google Scholar
  35. Krugman, P. (1991). Geography and trade (Gaston Eyskens lecture series). Leuven: Leuven University Press.Google Scholar
  36. Krugman, P. (1999). The Role of Geography in Development. International Regional Science Review, 22(2), 142–161.CrossRefGoogle Scholar
  37. Kuijpers-Linde, M., Geurs, K. T., Knoop, J. M., Kuiper, R., Lagas, P., Ligtvoet, W., et al. (2007). Nederland Later, Tweede Duurzaamheidsverkenning, deel fysieke leefomgeving Nederland. Bilthoven.Google Scholar
  38. Lambin, E. F., Rounsevell, M. D. A., & Geist, H. J. (2000). Are agricultural land-use models able to predict changes in land-use intensity? Agriculture, Ecosystems & Environment, 82(1–3), 321–331.CrossRefGoogle Scholar
  39. Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., et al. (2001). The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change, 11(4), 261–269.CrossRefGoogle Scholar
  40. Langdon, W. B. (1998). Genetic programming and data structures (350p). MSc, University College, London .Google Scholar
  41. Le Page, C., Bousquet, F., Bakam, I., Bah, A., & Baron, C. (2000). CORMAS: A multiagent simulation toolkit to model natural and social dynamics at multiple scales. Wageningen: Workshop ‘The ecology of scales’.Google Scholar
  42. Lesschen, J. P., Verburg, P. H., & Staal, S. J. (2005). Statistical methods for analysing the spatial dimension of changes in land use and farming systems, International Livestock Research Institute LUCC Focus 3 Office.Google Scholar
  43. Li, X., & Yeh, A. G.-O. (2001). Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A, 33(8), 1445–1462.CrossRefGoogle Scholar
  44. Liao, T. F. (1994). Interpreting probability models. Logit, Probit, and Other Generalized Linear Models, University of Iowa–07–101.Google Scholar
  45. Ligtenberg, A., Bregt, A. K., & Lammeren, Rv. (2001). Multi-actor-based land use modelling: Spatial planning using agents. Elsevier, 56, 21–33.Google Scholar
  46. Lopez, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: A case in Morelia city, Mexico. Landscape and Urban Planning, 55, 271–285.CrossRefGoogle Scholar
  47. LUMOS (2005). Platform for land use modeling in the Netherlands. Lumospro. (2007). Project team website. http://www.lumospro.nl
  48. Mas, J. F., Puig, H., Palacio, J. L., & Sosa-Lopez, A. (2004). Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software, 19(5), 461–471.CrossRefGoogle Scholar
  49. Matthews, R., Gilbert, N., Roach, A., Polhill, G., & Gotts, N. (2007). Agent-based land-use models: A review of applications. Landscape Ecology, 22, 1447–1459.CrossRefGoogle Scholar
  50. McGarigal, K., & Marks., B. J. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. Portland, OR: US, Department of Agriculture, Forest Service, Pacific Northwest Research Station-PNW-GTR–351.Google Scholar
  51. Meyer, W. B., & Turner, B. L. (1992). Human population growth and global land-use/cover change. Annual Review of Ecology and Systematics, 23(1), 39–61.CrossRefGoogle Scholar
  52. Moeckel, R., Schurmann, C., & Wegener, M. (2002). Microsimulation of land use. 42nd European Congress of The Regional Science Association. Dortmund: Institut fur Raumplanung, University of Dortmund.Google Scholar
  53. Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.Google Scholar
  54. Nelson, G. C., & Hellerstein, D. (1997). Do roads cause deforestation? Using satellite images in econometric analysis of land use. American Journal of Agricultural Economics, 79(1), 80–88.CrossRefGoogle Scholar
  55. Overmars, K. P., de Koning, G. H. J., & Veldkamp, A. (2003). Spatial autocorrelation in multi-scale land use models. Ecological Modelling, 164(2–3), 257–270.CrossRefGoogle Scholar
  56. Overmars, K. P., de Groot, T., & Huigen, M. G. A. (2007). Comparing inductive and deductive modeling of land use decisions: Principles, a model and an illustration from the Philippines. Human Ecology, 35, 439–452.CrossRefGoogle Scholar
  57. Parker, D. C., Berger, T., & Manson, S. M. (2001). Agent-based models of land-use and land-cover change. Adaptive agents, intelligence and emergent human organization: Capturing complexity through agent-based modelling. Irvine, CA: LUCC International Project Office.Google Scholar
  58. Pijanowski, B. C., Brown, D. G., Shellito, B. A., & Manik, G. A. (2002). Using neural networks and GIS to forecast land use changes: A land transformation model. Computers, Environment and Urban Systems, 26(6), 553–575.CrossRefGoogle Scholar
  59. Pijanowski, B. C., Pithadia, S., Shellito, B. A., & Alexandridis, K. (2005). Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19(2), 197–215.CrossRefGoogle Scholar
  60. Pinto, N. N., & Antunes, A. P. (2007). Cellular automata and urban studies: A literature survey. Architecture, City and Environment, 4, 471–486.Google Scholar
  61. Pontius, R. G., Boersma, W., Castella, J.-C., Clarke, K., De Nijs, T., Dietzel, C., et al. (2008). Comparing the input, output, and validation maps for several models of land change. Annals of Regional Science, 42(1), 11–37.CrossRefGoogle Scholar
  62. Pontius, R. G., Cornell, J. D., & Hall, C. A. S. (2001). Modeling the spatial pattern of land-use change with GEOMOD2: Application and validation for Costa Rica. Agriculture, Ecosystems & Environment, 85(1–3), 191–203.CrossRefGoogle Scholar
  63. Ricardo, D. (1817). On the Principles of Political Economy and Taxation, Library of Economics and Liberty.Google Scholar
  64. Scopus. (2008). The largest abstract and citation database of research literature and quality web sources.Google Scholar
  65. Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6), 525–552.CrossRefGoogle Scholar
  66. Sinclair, R. (1967). Von Thunen and Urban Sprawl. Annals of the Association of American Geographers, 57(1), 72–87.CrossRefGoogle Scholar
  67. Skapura, D. (1996). Building neural networks. New York: ACM Press.Google Scholar
  68. Tobler, W. (1979). Cellular geography. In S. Gale & G. Olsson (Eds.), Philosophy in geography (pp. 379–386). Dordrecht: Reidel.Google Scholar
  69. Turner, B. L., Skole, D., Sanderson, S., Fischer, G., Fresco, L., & Leemans., R. (1995). Land-Use and Land-Cover Change Science/Research Plan, International Human Dimensions Programme on Global Environmental Change-7.Google Scholar
  70. Veldkamp, A., & Fresco, L. O. (1996). CLUE: A conceptual model to study the conversion of land use and its effects. Ecological Modelling, 85(2–3), 253–270.CrossRefGoogle Scholar
  71. Verburg, P. H., de Koning, G. H. J., Kok, K., Veldkamp, A., & Bouma, J. (1999). A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecological Modelling, 116(1), 45–61.CrossRefGoogle Scholar
  72. Verburg, P. H., van Eck, J. R. R., de Nijs, T. C. M., Dijst, M. J., & Schot, P. (2004). Determinants of land-use change patterns in the Netherlands. Environment and Planning B: Planning and Design, 31(1), 125–150.Google Scholar
  73. Verburg, P. H., Schot, P. P., Dijst, M. J., & Veldkamp, A. (2004). Land use change modelling: Current practice and research priorities. GeoJournal, 61, 309–324.CrossRefGoogle Scholar
  74. von Thünen, J. H. (1966). Isolated state: An English edition of Der isolierte Staat. New York: Pergamom Press.Google Scholar
  75. Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation and environmental planning.Google Scholar
  76. Wagner, P., & Wegener, M. (2007). Urban land use, transport and environment models. disP, 3, 45–57.CrossRefGoogle Scholar
  77. Walker, R. (2004). Theorizing land-cover and land-use change: The case of tropical deforestation. International Regional Science Review, 27(3), 247–270.CrossRefGoogle Scholar
  78. Walsh, S. E., Soranno, P. A., & Rutledge, D. T. (2003). Lakes, Wetlands, and Streams as Predictors of land use/cover distribution. Environmental Management, 31(2), 198–214.CrossRefGoogle Scholar
  79. Wear, D. N., & Bolstad, P. (1998). Land-use changes in southern appalachian landscapes: Spatial analysis and forecast evaluation. Ecosystems, 1(6), 575–594.CrossRefGoogle Scholar
  80. White, R., & Engelen, G. (1994). Cellular dynamics and GIS: Modelling spatial complexity. Geographical Systems, 1, 237–253.Google Scholar
  81. White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24(5), 383–400.CrossRefGoogle Scholar
  82. Wrigley, N. (1976). Introduction to the use of logit models in geography. Norwich: University of East Anglia–10.Google Scholar
  83. Wu, J., & Li, H. (2006). Concepts of scale and scaling. In J. Wu, K. B. Jones, H. Li, & O. L. Loucks (Eds.), Scaling and uncertainty analysis in ecology: Methods and applications. New York: Springer, p. 6.CrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jonas van Schrojenstein Lantman
    • 1
    Email author
  • Peter H. Verburg
    • 2
  • Arnold Bregt
    • 3
  • Stan Geertman
    • 4
  1. 1.Nelen & SchuurmansUtrechtThe Netherlands
  2. 2.Institute for Environmental Studies, VU University AmsterdamAmsterdamThe Netherlands
  3. 3.Laboratory of Geo-Information Science and Remote SensingWageningen UniversityWageningenThe Netherlands
  4. 4.Faculty of GeosciencesUtrecht UniversityUtrechtThe Netherlands

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