Literature Review

Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

In this chapter, we present a literature review about geosimulation characteristics and its difference with the traditional methods. It is not our intention to restate the basic foundations of each particular methodology; nevertheless it is essential to provide a comprehensive explanation of the basics of the Cellular Automata model, the Markov Chain Model, the Cellular Automata Markov approach and the Logistic Regression Model. This is helpful to deal with their strengths and weaknesses. Thus, this chapter will first introduce the ABM system in contrast with the aforementioned traditional methodologies. Then we present an overview about the current and existing toolkits to design an agent-based model and their capability to create a geosimulation environment.

Keywords

Land Cover Analytic Hierarchy Process Cellular Automaton Transition Rule Urban System 
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. Anylogic (2006) Anylogic. Available at: http://www.xjtek.com/
  2. Bakker MM, van Doorn AM (2009) Farmer-specific relationships between land use change and landscape factors: introducing agents in empirical land use modelling. Land Use Policy 26(3):809–817CrossRefGoogle Scholar
  3. Bandini S, Manzoni S, Vizzari G (2009) Agent based modeling and simulation: an informatics perspective. J Artif Soc Social Simul 12:4Google Scholar
  4. Banko G (1998) A review of assessing the accuracy of classifications of remotely sensed data and of methods including remote sensing data in forest inventory, Working Papers ir98081, International Institute for Applied Systems Analysis: AustriaGoogle Scholar
  5. Batty M (2005) Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals. The MIT Press, CambridgeGoogle Scholar
  6. Batty M, Jiang B (1999) Multi-agent simulation new: approaches to exploring space-time dynamics in GIS. Centre for Advanced Spatial Analysis (UCL), London, UKGoogle Scholar
  7. Bazghandi A, Pouyan A (2008) Considering geographic information systems in buyer/seller agents simulation. In: information and communication technologies: from theory to applications, 2008. ICTTA 2008. 3rd international conference on, pp 1–5Google Scholar
  8. Benenson I, Torrens PM (2003) Geographic automata systems: a new paradigm for integrating GIS and geographic simulation. In: Martin D (ed) Proceedings of the 7th international conference on geocomputation, Southampton, GeoComputation 2003 CD-ROMGoogle Scholar
  9. Benenson I, Torrens PM (2004) Geosimulation: automata-based modeling of urban phenomena. Wiley, New YorkCrossRefGoogle Scholar
  10. Benenson I, Aronovich S, Noam S (2005) Let’s talk objects: generic methodology for urban high-resolution simulation. Comput Environ Urban Syst 29(4):425–453CrossRefGoogle Scholar
  11. Berger T (2001) Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agric Econ 25(2–3):245–260CrossRefGoogle Scholar
  12. Berger T, Schreinemachers P (2006) Creating agents and landscapes for multiagent systems from random samples. Ecol Soc 11(2):19Google Scholar
  13. Bertelle C, Duchamp GHE, Kadri-Dahmani H (2009) Complex systems and self-organization modelling. Springer, New YorkCrossRefGoogle Scholar
  14. Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci U S A 99(90003):7280–7287CrossRefGoogle Scholar
  15. Bousquet F, Le Page C (2004) Multi-agent simulations and ecosystem management: a review. Ecol Modell 176(3–4):313–332CrossRefGoogle Scholar
  16. Bousquet F, Le Page C, Bakam I, Takforyan A (2001) Multiagent simulations of hunting wild meat in a village in eastern Cameroon. Ecol Modell 138(1–3):331–346CrossRefGoogle Scholar
  17. Brail RK, Klosterman RE (2001) Planning support systems: integrating geographic information systems, models, and visualization tools. ESRI Inc., New YorkGoogle Scholar
  18. Camazine S, Deneubourg J, Franks N, Sneyd J, Theraulaz G, Bonabeau E (2003) Self-organization in biological systems. Princeton University Press, PrincetonGoogle Scholar
  19. Castella J, Boissau S, Trung T, Quang D (2005) Agrarian transition and lowland-upland interactions in mountain areas in northern Vietnam: application of a multi-agent simulation model. Agric Syst 86(3):312–332CrossRefGoogle Scholar
  20. Castle CJ, Crooks AT (2006) Principles and concepts of agent-based modelling for developing geospatial simulations, centre for advanced spatial analysis (UCL). UCL (University College London), LondonGoogle Scholar
  21. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46CrossRefGoogle Scholar
  22. Cohn AG, Gotts NM (1996) The `Egg-Yolk’ representation of regions with indeterminate boundaries. In: Burrough PA, Frank AU (eds) Geographic objects with indeterminate boundaries. Taylor and Francis, London, pp 171–187Google Scholar
  23. Congalton R, Mead R (1983) A quantitative method to test for consistency and correctness in photointerpretation. Photogramm Eng Remote Sens 49(1):69–74Google Scholar
  24. Congalton RG, Oderwald RG, Mead RA (1983) Assessing landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm Eng Remote Sens 49:1671–1678Google Scholar
  25. Crawford TW, Messina JP, Manson SM, O’Sullivan D (2005) Complexity science, complex systems, and land-use research. Env Planning B 32:792–798CrossRefGoogle Scholar
  26. Crooks AT (2006) Exploring cities using agent-based models and GIS. Proceedings of the agent 2006 conference on social agents: results and prospects, university of Chicago and Argonne national laboratory, Chicago, IL, USAGoogle Scholar
  27. Crooks AT (2007a) The repast simulation/modelling system for geospatial simulation, centre for advanced spatial analysis (University College London): Working Paper 123, London, UKGoogle Scholar
  28. Crooks AT (2007b) Experimenting with cities: utilizing agent-based models and GIS to explore urban dynamics. University College London, LondonGoogle Scholar
  29. Crooks AT, Castle C, Batty M (2008) Key challenges in agent-based modelling for geo-spatial simulation. Comput Env Urban Syst 32(6):417–430CrossRefGoogle Scholar
  30. De Feo G, De Gisi S (2010) Using an innovative criteria weighting tool for stakeholders involvement to rank MSW facility sites with the AHP, waste management, vol 30, issue 11. Special thematic section: sanitary land filling, pp 2370–2382Google Scholar
  31. Dubois D, Prade H (1979) Fuzzy real algebra: some results. Fuzzy Sets Syst 2(4):327–348CrossRefGoogle Scholar
  32. Ducheyne E (2003) Multiple objective forest management using GIS and genetic optimisation techniques, PhD thesis, faculty of agricultural and applied biological sciences. University of Ghent, BelgiumGoogle Scholar
  33. Ellingson AR, Andersen DC (2002) Spatial correlations of diceroprocta apache and its host plants: evidence for a negative impact from tamarix invasion. Ecol Entomol 27(1):16–24CrossRefGoogle Scholar
  34. Ellis E, Pontius Jr RG (2006) land-use and land-cover change—encyclopedia of earth. Available at: http://www.eoearth.org/article/land-use_and_land-cover_change
  35. Epstein JM (2007) Agent-based computational models and generative social science, in generative social science studies in agent-based computational modeling. Princeton University Press, Princeton, pp 41–60Google Scholar
  36. Epstein JM, Axtell RL (1996) Growing artificial societies: social science from the bottom up, 1st edn. The MIT Press, CambridgeGoogle Scholar
  37. Ettema D, De Jong K, Timmermans H, Bakema A (2007) PUMA: multi-agent modelling of urban systems. In modelling land-use change. The geojournal library. Springer, Netherlands, pp 237–258Google Scholar
  38. Forman EH, Selly MA (2001) Decision by objectives: how to convince others that you are right. World Scientific, SingaporeCrossRefGoogle Scholar
  39. Franklin S, Graesser A (1996) Is it an agent, or just a program?: a taxonomy for autonomous agents. In: Müller JP, Wooldridge MJ, Jennings NR (eds) Proceedings of the third international workshop on agent theories, architectures, and languages, Springer, pp 21–35Google Scholar
  40. Getchell A (2008) Agent-based modeling, university of California, Davis. Available at: http://www2.econ.iastate.edu/tesfatsi/AgentBasedModeling.AdamGetchell.phy250.Report.pdf
  41. Hill MJ, Braaten R (2005) Multi-criteria decision analysis in spatial decision support: the ASSESS analytic hierarchy process and the role of quantitative methods and spatially explicit analysis. Environ Model Softw 20(7):955–976CrossRefGoogle Scholar
  42. Holland J (1996) Hidden order: how adaptation builds complexity, 1st edn. Addison Wesley Longman, Redwood CityGoogle Scholar
  43. Hossain MS, Das NG (2010) GIS-based multi-criteria evaluation to land suitability modelling for giant prawn macrobrachium rosenbergii farming in companigonj upazila of noakhali Bangladesh. Comput Electron Agric 70(1):172–186CrossRefGoogle Scholar
  44. Hsu P, Wu C, Li Y (2008) Selection of infectious medical waste disposal firms by using the analytic hierarchy process and sensitivity analysis. Waste Manage 28(8):1386–1394CrossRefGoogle Scholar
  45. Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749CrossRefGoogle Scholar
  46. Huigen MGA (2004) First principles of the MameLuke multi-actor modelling framework for land use change, illustrated with a Philippine case study. J Environ Manage 72(1–2):5–21CrossRefGoogle Scholar
  47. Irwin EG, Bockstael NE (2002) Interacting agents, spatial externalities and the evolution of residential land use patterns. J Econ Geogr 2(1):31–54CrossRefGoogle Scholar
  48. Irwin EG, Geoghegan J (2001) Theory, data, methods: developing spatially explicit economic models of land use change. Agric Ecosyst Env 85(1–3):7–24CrossRefGoogle Scholar
  49. Janssen MA, Ostrom E (2006) Empirically based, agent-based models. Ecol Soc 11(2):37Google Scholar
  50. Jepsen MR, Leisz S, Rasmussen K, Jakobsen J, Müller-Jensen L, Christiansen L (2006) Agent-based modelling of shifting cultivation field patterns, Vietnam. Int J Geog Inf Sci 20(9):1067–1085CrossRefGoogle Scholar
  51. Kainz W (2008) Fuzzy logic and GIS. University of Vienna, Available at: http://homepage.univie.ac.at/wolfgang.kainz/Lehrveranstaltungen/ESRI_Fuzzy_Logic/File_2_Kainz_Text.pdf
  52. Karamous M, Zahraie B, Kerachian R, Jaafarzadeh N, Mahjouri N (2007) Developing a master plan for hospital solid waste management: a case study. Waste Manage 27(5):626–638CrossRefGoogle Scholar
  53. Klungboonkrong P, Taylor MAP (1998) A microcomputer-based- system for multicriteria environmental impacts evaluation of urban road networks. Comput Env Urban Syst 22(5):425–446CrossRefGoogle Scholar
  54. Kok K, Veldkamp A (2001) Evaluating impact of spatial scales on land use pattern analysis in central America. Agric Ecosyst Env 85(1–3):205–221CrossRefGoogle Scholar
  55. Kok K, Farrow A, Veldkamp A, Verburg PH (2001) A method and application of multi-scale validation in spatial land use models. Agric Ecosyst Env 85(1–3):223–238CrossRefGoogle Scholar
  56. Koomen E, Stillwell J, Bakema A, Scholten HJ (2007) Modelling land-use change: progress and applications. Springer, New YorkCrossRefGoogle Scholar
  57. Lakide V (2009) Classification of synthetic aperture radar images using particle swarm optimization technique. MSc. thesis, National Institute of Technology Rourkela. Available at: http://ethesis.nitrkl.ac.in/1438/
  58. Lambin EF, Geist HJ, Ellis E (2007) Causes of land-use and land-cover change. In encyclopedia of earthGoogle Scholar
  59. Levy S (1992) Artificial life. The quest for a new creation. PenguinGoogle Scholar
  60. Li X, Liu X (2007) Defining agents’ behaviors to simulate complex residential development using multicriteria evaluation. J Environ Manage 85(4):1063–1075CrossRefGoogle Scholar
  61. Ligtenberg A, Wachowicz M, Bregt AK, Beulens A, Kettenis D (2004) A design and application of a multi-agent system for simulation of multi-actor spatial planning. J Environ Manage 72(1–2):43–55CrossRefGoogle Scholar
  62. Lodwick W (2007) Fuzzy surfaces in GIS and geographical analysis. CRC Press, Boca RatonCrossRefGoogle Scholar
  63. Longley P, Batty M (2003) Advanced spatial analysis: the CASA book of GIS. ESRI Inc, CaliforniaGoogle Scholar
  64. Macal CM, North MJ (2005) Tutorial on agent-based modeling and simulation. In: Kuhl ME, Steiger NM, Armstrong FB, Joines JA (eds) Proceedings of the 37th conference on winter simulation, Winter Simulation Conference, Orlando, Florida, pp 2–15Google Scholar
  65. Matthews R (2006) The people and landscape model (PALM): towards full integration of human decision-making and biophysical simulation models. Ecol Modell 194(4):329–343CrossRefGoogle Scholar
  66. Meyer WB, Turner BL (1994) Changes in land use and land cover: a global perspective. Cambridge University Press, CambridgeGoogle Scholar
  67. Monticino M, Acevedo M, Callicott B, Cogdill T, Ji M, Lindquist C (2007) Coupled human and natural systems: a multi-agent-based approach. Environ Model Softw 22(5):656–663CrossRefGoogle Scholar
  68. Macal CM, North MJ (2006) Tutorial on agent-based modeling and simulation part 2: how to model with agents. In: Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM (eds) Proceedings of the 38th conference on winter simulation, Winter Simulation Conference, Monterey, California, pp 73–83Google Scholar
  69. Openshaw S (1983) The modifiable areal unit problem concepts and techniques in modern geography, 28th edn. Geo Books, NorwichGoogle Scholar
  70. O’Sullivan D, Macgill JR, Yu C (2003) Agent-based residential segregation: a hierarchically structured spatial model. Proceedings of agent 2003 conference on challenges in social simulation, The University of Chicago, Chicago, pp 493–507Google Scholar
  71. Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93:314–337CrossRefGoogle Scholar
  72. Pontius RG Jr, Batchu K (2003) Using the relative operating characteristic to quantify certainty in prediction of location of land cover change in India. Trans GIS 7(4):467–484CrossRefGoogle Scholar
  73. Pontius RG Jr, Chen H (2006) GEOMOD modeling, idrisi andes help contents. Clark University, MassachusettsGoogle Scholar
  74. Pontius RG Jr, Schneider LC (2001) Land-cover change model validation by an ROC method for the ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1–3):239–248CrossRefGoogle Scholar
  75. Pontius RG Jr, Cornell JD, Hall CAS (2001) Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for costa rica. Agric Ecosyst Env 85(1–3):191–203CrossRefGoogle Scholar
  76. Pontius RG Jr, Huffaker D, Denman K (2004) Useful techniques of validation for spatially explicit land-change models. Ecol Modell 179(4):445–461CrossRefGoogle Scholar
  77. Quattrochi DA, Goodchild MF (1997) Scale in remote sensing and GIS, 1st edn. CRC Press, Boca RatonGoogle Scholar
  78. Ramanathan R, Ganesh LS (1995) Energy resource allocation incorporating qualitative and quantitative criteria: an integrated model using goal programming and AHP. Socio-Econ Planning Sci 29(3):197–218CrossRefGoogle Scholar
  79. Repenning A, Ioannidou A, Zola J (2000) Agentsheets: end-user programmable simulations. J Artif Soc Social Simul 3:3Google Scholar
  80. Rindfuss RR, Walsh SJ, TurnerII BL, Fox J, Mishra V (2004) Developing a science of land change: challenges and methodological issues. Proc Natl Acad Sci U S A 101(39):13976–13981CrossRefGoogle Scholar
  81. Robinson DT, Brown DG, Parker DC, Schreinemachers P, Janssen MA, Huigen M, Wittmer H, Grotts N, Promburom P, Irwin E, Berger T, Gatzweiler F, Barnaud C (2007) Comparison of empirical methods for building agent-based models in land use science. J Land Use Sci 2(1):31–55CrossRefGoogle Scholar
  82. Rosenfield GH, Fitzpatrick-Lins K (1986) A coefficient of agreement as a measure of thematic classification accuracy. Photogramm Eng Remote Sens 52(2):223–227Google Scholar
  83. Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Englewood CliffsGoogle Scholar
  84. Rykiel EJ (1996) Testing ecological models: the meaning of validation. Ecol Modell 90(3):229–244CrossRefGoogle Scholar
  85. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281CrossRefGoogle Scholar
  86. Saaty TL (1999) Decision making for leaders: the analytic hierarchy process for decisions in a complex world, New edition 2001, 3rd edn. RWS Publications, PittsburghGoogle Scholar
  87. Sawyer RK (2003) Artificial societies: multiagent systems and the micro-macro link in sociological theory. Sociol Methods Res 31:325–363CrossRefGoogle Scholar
  88. Showalter P, Lu Y (2009) Geospatial techniques in Urban hazard and disaster analysis. Springer, The NetherlandsGoogle Scholar
  89. Smith MJD, Goodchild MF, Longley PA (2007) Geospatial analysis: a comprehensive guide to principles, techniques and software tools, 2nd edn. Troubador Publishing Ltd, KibworthGoogle Scholar
  90. Swets JA (1986) Indices of discrimination or diagnostic accuracy: their ROCs and implied models. Psychol Bull 99(1):100–117CrossRefGoogle Scholar
  91. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science (New York) 240(4857):1285–1293CrossRefGoogle Scholar
  92. Timmermans H (2003) The saga of integrated land use-transport modeling: how many more dreams before we wake up. In Keynote paper auf der 10th international conference on travel behavior research, LuzernGoogle Scholar
  93. Tisue S, Wilensky U (2004) NetLogo: a simple environment for modelling complexity. International conference on complex systems (ICCS 2004), Boston, MA, pp 16–21 Google Scholar
  94. Tiwari DN, Loof R, Paudyal GN (1999) Environmental-economic decision-making in lowland irrigated agriculture using multi-criteria analysis techniques. Agric Syst 60(2):99–112CrossRefGoogle Scholar
  95. Tobias R, Hofmann C (2004) Evaluation of free Java-libraries for social-scientific agent based simulation. J Artif Soc Social Simul 7:1Google Scholar
  96. Torrens P (2006a) Simulating sprawl. Ann Assoc Am Geogr 96(2):248–275CrossRefGoogle Scholar
  97. Torrens PM (2006b) Geosimulation and its application to Urban growth modeling. Springer, London, pp 119–134Google Scholar
  98. TurnerII BL, Skole D, Sanderson S, Fischer G, Fresco L, Leemans R (1995) Land-use and land-cover change science/research plan, IGBP report no. 35, HDP report no. 7, Stockholm and GenevaGoogle Scholar
  99. Valbuena D, Verburg PH, Bregt AK (2008) A method to define a typology for agent-based analysis in regional land-use research. Agric Ecosyst Environ 128(1–2):27–36CrossRefGoogle Scholar
  100. Veldkamp A, Lambin EF (2001) Predicting land-use change. Agric Ecosyst Env 85(1–3):1–6CrossRefGoogle Scholar
  101. Verburg PH, Schot P, Dijst M, Veldkamp A (2004) Land use change modelling: current practice and research priorities. GeoJournal 61(4):309–324CrossRefGoogle Scholar
  102. Wooldridge MJ, Jennings NR (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10(2):115–152CrossRefGoogle Scholar
  103. Wu F, Webster CJ (1998) Simulation of land development through the integration of cellular automata and multicriteria evaluation. Env Plan B 25(1):103–126CrossRefGoogle Scholar
  104. Yang Q, Li X, Shi X (2008) Cellular automata for simulating land use changes based on support vector machines. Comput Geosci 34(6):592–602CrossRefGoogle Scholar
  105. Yedla S, Shrestha R (2003) Multi-criteria approach for the selection of alternative options for environmentally sustainable transport system in Delhi. Transp Res Part A 37:717–729Google Scholar
  106. Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  1. 1.Department of Geography and Regional ResearchUniversity of ViennaViennaAustria

Personalised recommendations