Advertisement

Constraint Cellular Automata for Urban Development Simulation: An Application to the Strasbourg-Kehl Cross-Border Area

  • J. P. Antoni
  • V. Judge
  • G. Vuidel
  • O. Klein
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Urban sprawl and space consumption have become key issues in sustainable territorial development. Traditional planning approaches are often insufficient to anticipate their complex spatial consequences, especially in cross-border areas. Such complexity requires the use of dynamic spatial simulations and the development of adapted tools like LucSim, a CA-based tool offering solutions for sharing spatial data and simulations among scientists, technicians and stakeholders. Methodologically, this tool allows us to simulate future land use change by first quantifying and then locating the changes. Quantification is based on Markov chains and location on transition rules. The proposed approach is implemented on the Strasbourg-Kehl cross-border area and calibrated with three contrasting prospective scenarios to try to predict cross-border territorial development.

Keywords

Cellular automata Markov chains Cross-border area Land use scenarios Prospective 

Notes

Acknowledgements

The research presented in this chapter is part of the Smart. Boundary project supported by the Fonds National de la Recherche in Luxembourg and CNRS in France (ref. INTER/CNRS/12/02). The authors would like also to thank the Grasp Program of LISER for allowing cross-collaboration between the two teams based in Luxembourg and France.

References

  1. Antoni JP (2006) Calibrer un modèled’évolution de l’occupation du sol urbain. L’exemple de Belfort.Cybergeo : Eur J Geogr. http://cybergeo.revues.org/2436
  2. Antoni JP (2009) Un territoire de projet à co-construire. In: Grossouvre (de) H, Maulin E (eds) Euro-District Strasbourg-Ortenau : la construction de l’Europeréelle. Xénia, pp 25–31Google Scholar
  3. Basse RM, Omrani H, Charif O et al (2014) Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geogr 53:160–171CrossRefGoogle Scholar
  4. Batty M, Xie Y (1994) From Cells to Cities. Environ Plan 21:531–538Google Scholar
  5. Benenson I, Torrens PM (2004) Geosimulation: automata-based modeling of urban phenomena, 1st edition. WileyGoogle Scholar
  6. Berchtold A (1998) Chaînes de Markov etmodèles de transition. Hermes Science Publications, Paris, Applications aux sciences socialesGoogle Scholar
  7. Clarke KC, Hoppen S, Gaydos L (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environ Plan 24:247–261CrossRefGoogle Scholar
  8. Coplan DB (2012) Border Show Business and Performing States. In: Wilson TM, Donnan H (eds) A companion to border studies. Wiley-Blackwell, pp 507–521Google Scholar
  9. Couclelis H (1985) Cellular Worlds: a framework for modeling micro-macro dynamics. Environ Plann A 20:99–109CrossRefGoogle Scholar
  10. Couclelis H (1987) Cellular dynamics: How individual decisions lead to global urban change. Eur J Oper Res 30:344–346CrossRefGoogle Scholar
  11. Couclelis H (2005) “Where has the Future Gone?” Rethinking the role of integrated land-use models in spatial planning. Environ Plann A 37:1353–1371CrossRefGoogle Scholar
  12. Durr MJ, Kayali ML (2014) Six millions d’habitants pour l’Alsace, Chiffres pour l’Alsace, 50Google Scholar
  13. European Environment Agency (2006) Urban sprawl in Europe: the ignored challenge. EEA ReportGoogle Scholar
  14. Feller W (1968) An introduction to probability theory and its applications, 3rd edn. WileyGoogle Scholar
  15. Judge V, Antoni J-P, Klein O (2015) Land use simulation: statistical analysis approaches to calibrate cellular automataGoogle Scholar
  16. Kaiser RJ (2012) Performativity and the Eventfulness of Bordering Practices. In: Wilson TM, Donnan H (eds) A companion to border studies. Wiley-Blackwell, pp 522–537Google Scholar
  17. Kolossov V (2012) Euroborderscapes: State of the Debate, Report for the 7th Framework Programme of the European CommissionGoogle Scholar
  18. Koomen E, Hilferink M, Borsboom-Van Beurden J (2011) Introducing land use scanner. In: Koomen E, Borsboom-Van Beurden J (eds) Land-use modelling in planning practice. Springer, pp 3–21Google Scholar
  19. Stoklosa K, Besier G (2014) European border regions in comparison: overcoming nationalistic aspects or re-nationalization?, 1st edn. Routledge, New York, LondonGoogle Scholar
  20. Timmermans HJP (2003) The Saga of integrated land use-transport modeling: how many more dreams before we wake up? In: 10th International conference on travel behaviour research, Lucerne, Switzerland, p 35Google Scholar
  21. Torrens PM (2000) How cellular models of urban systems work (1. theory). CASA Working Paper SeriesGoogle Scholar
  22. Wegener M, Fürst F (1999) Land use transport interaction: state of the art. Transland, IrpudGoogle Scholar
  23. White R, Engelen G (1993) Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environ Plann A 25:1175–1199CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Laboratoire the MA UMR 6049 CNRSUniversity of BurgundyFranche-Comté, DijonFrance
  2. 2.LISER Luxembourg Institute of Socio-Economic ResearchEsch-sur-AlzetteLuxembourg

Personalised recommendations