Journal of Geographical Systems

, Volume 15, Issue 4, pp 403–426 | Cite as

Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

Original Article

Abstract

Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network–based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.

Keywords

Agent-based models (ABMs) Bayesian networks (BNs) Cellular automata (CA) Geographic information systems (GIS) Land-use change Population change 

JEL Classification

C11 C63 021 R23 

Notes

Acknowledgments

The authors thank the Natural Sciences and Engineering Research Council (NSERC) and Social Sciences and Humanities Research Council (SSHRC) of Canada for financial support of this study. The Metro Vancouver and the Greater Vancouver Transportation Authority (Translink) provided some of the spatial data. The MATLAB software was made available by the Network Support Group, Faculty of Applied Sciences and Centre for Systems Science at Simon Fraser University. The authors are thankful to the journal Editor and the two anonymous reviewers for their valuable comments.

References

  1. An L, Linderman M, Qi J, Shortridge A, Liu J (2005) Exploring complexity in a human-environment system: an agent-based spatial model for multidisciplinary and multiscale integration. Ann Assoc Am Geogr 95(1):54–79CrossRefGoogle Scholar
  2. Batty M (2005) Agents, cells, and cities: new representational models for simulating multiscale urban dynamics. Environ Plan A 37(8):1373–1394CrossRefGoogle Scholar
  3. Benenson I (2004) Agent-based modeling: From individual residential choice to urban residential dynamics. In: Goodchild MF, Janelle DG (eds) Spatially Integrated Social Science. Oxford University Press, New York, pp 67–95Google Scholar
  4. Benenson I, Omer I, Hatna E (2002) Entity-based modeling of urban residential dynamics: the case of Yaffo, Tel Aviv. Environ Plan B 29(4):491–512CrossRefGoogle Scholar
  5. Bennett DA, Tang W, Wang S (2011) Toward an understanding of provenance in complex land use dynamics. J Land Use Sci 6(2–3):211–230CrossRefGoogle Scholar
  6. Bromley J, Jackson NA, Clymer OJ, Giacomello AM, Jensen FV (2005) The use of Hugin® to develop Bayesian Networks as an aid to integrated water resource planning. Environ Modell Softw 20(2):231–242CrossRefGoogle Scholar
  7. Brown DG, Xie Y (2006) Spatial agent-based modeling. Int J Geogr Inf Sci 20(9):941–943CrossRefGoogle Scholar
  8. Brown DG, Page SE, Riolo R, Rand W (2004) Agent-based and analytical modeling to evaluate the effectiveness of greenbelts. Environ Modell Softw 19(12):1097–1109CrossRefGoogle Scholar
  9. Brown DG, Page S, Riolo R, Zellner M, Rand W (2005a) Path dependence and the validation of agent-based spatial models of land use. Int J Geogr Inf Sci 19(2):153–174CrossRefGoogle Scholar
  10. Brown DG, Riolo R, Robinson DT, North M, Rand W (2005b) Spatial process and data models: toward integration of agent-based models and GIS. J Geogr Syst 7(1):25–47CrossRefGoogle Scholar
  11. Cheng J, Greiner R, Kelly J, Bell D, Liu WR (2002) Learning Bayesian networks from data: an information-theory based approach. Artif Intell 137(1–2):43–90CrossRefGoogle Scholar
  12. Collins RJ, Jefferson DR (1992) AntFarm: Towards simulated evolution. In: Langton CG (ed) Artificial life II : Proceedings of the Workshop on Artificial Life. Addison-Wesley, Redwood City, CA, pp 579–601Google Scholar
  13. Cooper GF, Herskovits E (1992) A Bayesian Method for the Induction of Probabilistic Networks from Data. Mach Learn 9(4):309–347Google Scholar
  14. de Almeida MC, Monteiro AMV, Soares GCBS, Cerqueira GC, Pennachin CL, Batty M (2005) GIS and remote sensing as tools for the simulation of urban land-use change. Int J Remote Sens 26(4):759–774Google Scholar
  15. Dorling D (1993) Map design for census mapping. Cartogr J 30(2):167–183CrossRefGoogle Scholar
  16. ESRI (2012) GIS and Mapping software. http://www.esri.com. Accessed 20 May 2012
  17. Evans TP, Sun W, Kelley H (2006) Spatially explicit experiments for the exploration of land-use decision-making dynamics. Int J Geogr Inf Sci 20(9):1013–1037CrossRefGoogle Scholar
  18. Ferber J (1998) Multi-agent systems: An introduction to distributed artificial intelligence. Addison-Wesley, HarlowGoogle Scholar
  19. Gigerenzer G, Selten R (2001) Bounded rationality the adaptive toolbox. MIT Press, Cambridge, Mass, MIT cognetGoogle Scholar
  20. Gilbert N, Terna P (2000) How to build and use agent-based models in social science. Mind Soc 1(1):57–72CrossRefGoogle Scholar
  21. Graniero PA, Robinson VB (2006) A probe mechanism to couple spatially explicit agents and landscape models in an integrated modeling framework. Int J Geogr Inf Sci 20(9):965–990CrossRefGoogle Scholar
  22. Heckerman D, Wellman MP (1995) Bayesian Networks. Commun ACM 38(3):27–30CrossRefGoogle Scholar
  23. Howard RA, Matheson JE (1984) Influence diagrams. In: Howard RA, Matheson JE (eds) Readings on the Principles and Applications of Decision Analysis, vol II., Strategic Decisions GroupMenlo Park, Calif, pp 719–762Google Scholar
  24. Janssens D, Wets G, Brijs T, Vanhoof K, Arentze T, Timmermans H (2006) Integrating Bayesian networks and decision trees in a sequential rule-based transportation model. Eur J Oper Res 175(1):16–34CrossRefGoogle Scholar
  25. Jensen FV (2001) Bayesian networks and decision graphs. Statistics for engineering and information science. Springer, New YorkGoogle Scholar
  26. Kocabas V, Dragicevic S (2006) Coupling Bayesian Networks with GIS-based cellular automata for modeling land use change. Lect Notes Comput Sci 4197:217–233CrossRefGoogle Scholar
  27. Kocabas V, Dragicevic S (2007) Enhancing a GIS cellular automata model of land use change: bayesian networks, influence diagrams and causality. Trans GIS 11(5):679–700CrossRefGoogle Scholar
  28. Kocabas V, Dragicevic S (2009) Agent-based model validation using Bayesian Networks and vector spatial data. Environ Plan B 36(5):787–801CrossRefGoogle Scholar
  29. Kocabas V, Dragicevic S (2012) Integration of a GIS-Bayesian Network agent-based model in a planning support system as framework for policy generation. URISA J 24(1):35–52Google Scholar
  30. Lauritzen S, Spiegelhalter D (1988) Local computations with probabilities on graphical structures and their applications to expert systems. J Roy Stat Soc 50(2):157–224Google Scholar
  31. Lei Z, Pijanowski BC, Alexandridis KT, Olson J (2005) Distributed modeling architecture of a multi-agent-based behavioral economic landscape (MABEL) model. Simul-T Soc Mod Sim 81(7):503–515Google Scholar
  32. LeSage JP, Pace RK (2004) Arc_Mat, a toolbox for using ArcView shape files for spatial econometrics and statistics. Lect Notes Comput Sci 3234:179–190CrossRefGoogle Scholar
  33. Ligmann-Zielinska A, Jankowski P (2007) Agent-based models as laboratories for spatially explicit planning policies. Environ Plan B 34(2):316–335CrossRefGoogle Scholar
  34. Ligmann-Zielinska A, Sun L (2010) Applying time dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change. Int J Geogr Inf Sci 24(12):1829–1850CrossRefGoogle Scholar
  35. Ligtenberg A, Bregt AK, van Lammeren R (2001) Multi-actor-based land use modelling: spatial planning using agents. Landsc Urban Plan 56(1–2):21–33CrossRefGoogle Scholar
  36. Liu X, LeSage J (2009) Arc_mat: a Matlab-based spatial data analysis toolbox. J Geogr Syst 12(1):69–87CrossRefGoogle Scholar
  37. Loibl W, Toetzer T (2003) Modeling growth and densification processes in suburban regions–simulation of landscape transition with spatial agents. Environ Modell Softw 18(6):553–563CrossRefGoogle Scholar
  38. Ma L, Arentze T, Borgers A, Timmermans H (2004) Using Bayesian decision networks for knowledge representation under conditions of uncertainty in multi-agent land use simulation models. In: Leeuwen J, Timmermans HJP (eds) Recent Advances in Design and Decision Support Systems in Architecture and Urban Planning. Kluwer, Dordrecht; Boston, pp 129–144Google Scholar
  39. Ma L, Arentze T, Borgers A, Timmermans H (2007) Modelling land-use decisions under conditions of uncertainty. Comput Environ Urban 31(4):461–476CrossRefGoogle Scholar
  40. Maes P (1994) Modeling adaptive autonomous agents. Artif Life 1:135–162CrossRefGoogle Scholar
  41. Manson SM (2006) Bounded rationality in agent-based models: experiments with evolutionary programs. Int J Geogr Inf Sci 20(9):991–1012CrossRefGoogle Scholar
  42. Mas JF, Puig H, Palacio JL, Sosa-Lopez A (2004) Modelling deforestation using GIS and artificial neural networks. Environ Modell Softw 19(5):461–471CrossRefGoogle Scholar
  43. Mathworks (2012) Getting started with Matlab. http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/getstart.pdf. Accessed 20 May 2012
  44. MetroVancouver (2012a) Greater Vancouver regional district. http://www.metrovancouver.org/Pages/default.aspx. Accessed May 20 2012
  45. MetroVancouver (2012b) Metro 2040 residential growth projections. http://www.metrovancouver.org/planning/development/strategy/RGSBackgroundersNew/RGSMetro2040ResidentialGrowth.pdf. Accessed 20 May 2012
  46. Miller EJ, Douglas Hunt J, Abraham JE, Salvini PA (2004) Microsimulating urban systems. Comput Environ Urban 28(1–2):9–44CrossRefGoogle Scholar
  47. Murphy K (2001) The Bayes net toolbox for Matlab. In: Wegman EJ, Braverman A, Goodman A, Smyth P (eds) Computing science and statistics vol 33, 33rd Symposium on the interface, Costa Mesa, CA, 2001. Interface Foundation of North America, pp 331–350Google Scholar
  48. Neapolitan RE (2003) Learning Bayesian Networks. Prentice Hall, HarlowGoogle Scholar
  49. Parker DC, Berger T, Manson SM (2001) Agent-based models of land-use and land-cover change: Report and review of an international workshop. http://www.indiana.edu/~act/focus1/ABM_Report6.pdf. Accessed 20 May 2012
  50. 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(2):314–337CrossRefGoogle Scholar
  51. Pearl J (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, CA, Morgan Kaufmann series in representation and reasoningGoogle Scholar
  52. Robinson DT, Brown DG (2009) Evaluating the effects of land-use development policies on ex-urban forest cover: an integrated agent-based GIS approach. Int J Geogr Inf Sci 23(9):1211–1232CrossRefGoogle Scholar
  53. Robinson D, Brown DG, Parker DC, Schreinemachers P, Janssen MA, Huigen M, Wittmer H, Gotts 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
  54. Rodrigues A, Grueau C, Raper J, Neves N (1998) Environmental planning using spatial agents. In: Carver S (ed) Innovations in GIS 5. Taylor & Francis, London, UK, pp 108–118Google Scholar
  55. Smajgl A, Brown DG, Valbuena D, Huigen MGA (2011) Empirical characterisation of agent behaviours in socio-ecological systems. Environ Modell Softw 26(7):837–844CrossRefGoogle Scholar
  56. Statistics-Canada (2012) Canada’s national statistical agency. http://www.statcan.ca. Accessed 20 May 2012
  57. Tobler WR (1984) Application of image processing techniques to map processing. In: Proceedings of the International Symposium on Spatial Data Handling, Zurich, IGU, pp 140–144Google Scholar
  58. Torrens PM (2006) Simulating sprawl. Ann Assoc Am Geogr 96(2):248–275CrossRefGoogle Scholar
  59. Torrens PM, Benenson I (2005) Geographic automata systems. Int J Geogr Inf Sci 19(4):385–412CrossRefGoogle Scholar
  60. Torrens PM, Nara A (2007) Modeling gentrification dynamics: a hybrid approach. Comput Environ Urban 31(3):337–361CrossRefGoogle Scholar
  61. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Model 203(3–4):312–318CrossRefGoogle Scholar
  62. Waddell P (2002) UrbanSim—Modeling urban development for land use, transportation, and environmental planning. J Am Plan Assoc 68(3):297–314CrossRefGoogle Scholar
  63. Wright JK (1936) A method of mapping densities of population: with Cape Cos as an example. Geogr Rev 26(1):103–110CrossRefGoogle Scholar
  64. Wu S, Wang L, Qiu X (2008) Incorporating GIS building data and census housing statistics for sub-block-level population estimation. Prof Geogr 60(1):121–135CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Spatial Analysis and Modeling Laboratory, Department of GeographySimon Fraser UniversityBurnabyCanada

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