Advertisement

Land Use Dynamics and Coastal Management

  • Corentin M. Fontaine
  • Mustafa Mokrech
  • Mark D. A. Rounsevell
Part of the Advances in Global Change Research book series (AGLO, volume 49)

Abstract

Changes in climate risk are driven by a range of socio-economic factors, as well as the climatic drivers considered in Chaps.  2 and  3. Exploring how the built environment might change over time is an essential element for assessing changing risks. This chapter presents and discusses two algorithm-based approaches used in the Tyndall Coastal Simulator to analyse local changes in residential and other urban land uses.

The first approach uses multi-criteria analysis (MCA) to calculate spatial weights based on a number of attracting features such as transport, existing development, flood risk and proximity to the coast to identify the development patterns under different socio-economic futures. The second method uses agent-based modelling (ABM) to examine interactions between residential households and local planners as a demand–supply process that produces possible development patterns under different socio-economic futures. The core elements of this model are the location preferences of (changing) individual residential agents and the constraints imposed by planners through land-use policy.

The MCA method is simpler than the ABM method and useful for developing and realising socio-economic scenarios because of its flexibility and the possibility of quick implementation and adjustment. As a result, the MCA method is easy to implement, and it provides flexible tools that can be used for first-step socio-economic scenario development. While the MCA method requires fewer input variables, the ABM method is better able to account for feedbacks between governance processes, individual choices and external changes such as migration. The ABM is more sophisticated and more detailed and so provides a comprehensive approach that considers both location preferences and planning policies. The ABM method captures local behaviour and the iterative characteristics of urban dynamics. For both models, the key outputs are plausible distributions (scenarios) of urban dwellings in East Anglia over the twenty-first century under four different socio-economic storylines (see also Appendix A). Both methods are relevant to the process of risk assessment of climate change, and both have much scope for improvement, e.g. including brownfield sites and change in vertical as well as horizontal urban densities.

For the Tyndall Coastal Simulator, these model outputs provide the distribution of property that is vulnerable to coastal erosion and flooding, as described in the risk analysis in Chap.  9. More generally these types of algorithm-based models of socio-economic parameters have great potential for inclusion in a wide range of future change analyses.

Keywords

Urban development Land-use change Urban system dynamics Algorithm-based modelling Agent-based modelling Multi-criteria analysis 

References

  1. Barker, K. (2006). Barker review of land use planning: Final report recommendations. London: The Stationary Office.Google Scholar
  2. Batty, M. (2005). Cities and complexity: Understanding cities with cellular automata, agent-based models and fractals. Cambridge: MIT Press.Google Scholar
  3. Batty, M. (2008). The size, scale, and shape of cities. Science, 319(5864), 769–771.CrossRefGoogle Scholar
  4. Brown, D. G., Robinson, D. T., An, L., Nassauer, J. I., Zellner, M., Rand, W., Riolo, R., Page, S. E., Low, B., & Wang, Z. F. (2008). Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system. Geoforum, 39(2), 805–818.CrossRefGoogle Scholar
  5. Cavailhes, J., Peeters, D., Sekeris, E., & Thisse, J. F. (2004). The periurban city: Why to live between the suburbs and the countryside. Regional Science and Urban Economics, 34(6), 681–703.CrossRefGoogle Scholar
  6. Crooks, A. T., Castle, C. J. E., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430.CrossRefGoogle Scholar
  7. Eastman, J., Kyem, P., Toledano, J., & Jin, W. (1993). GIS and decision making. In Explorations in geographic information systems technology (Vol. 4). Geneva: United Nations Institute for Training and Research (UNITAR).Google Scholar
  8. Fontaine, C. M. (2010). Residential agents and land use change modelling. Thesis, School of GeoSciences, Faculty of Science and Engineering, The University of Edinburgh, Scotland (UK).Google Scholar
  9. Fontaine, C. M., & Rounsevell, M. D. A. (2009). An agent-based approach to model future residential pressure on a regional landscape. Landscape Ecology, 24(9), 1237–1254.CrossRefGoogle Scholar
  10. Holman, I. P., Rounsevell, M. D. A., Shackley, S., Harrison, P. A., Nicholls, R. J., Berry, P. M., & Audsley, E. (2005a). A regional, multi-sectoral and integrated assessment of the impacts of climate and socio-economic change in the UK Part I. Methodology. Climate Change, 71, 9–41.CrossRefGoogle Scholar
  11. Holman, I. P., Nicholls, R. J., Berry, P. M., Harrison, P. A., Audsley, E., Shackley, S., & Rounsevell, M. D. A. (2005b). A regional, multi-sectoral and integrated assessment of the impacts of climate and socio-economic change in the UK Part II. Results. Climate Change, 71, 43–73.CrossRefGoogle Scholar
  12. Lambin, E. F., & Geist, H. J. (2001). Global land-use and land-cover change: What have we learned so far? Global Change Newsletter, 46, 27–30.Google Scholar
  13. Mokrech, M., Nicholls, R. J., & Dawson, R. J. (2012). Scenarios of future built environment for impact assessment of climate change using a multi-criteria approach in GIS. Environment and Planning B: Planning and Design, 30, 120–136.CrossRefGoogle Scholar
  14. Nicholls, R. J., Mokrech, M., Richards, J., Bates, P., Dawson, R., Hall, J., Walkden, M., Dickson, M., Jordan, A., & Milligan, J. (2005). Assessing coastal flood risk at specific sites and regional scales: Regional assessment of coastal flood risk (Tyndall Centre technical report no. 45). Norwich: Tyndall Centre for Climate Change Research.Google Scholar
  15. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314–337.CrossRefGoogle Scholar
  16. Reginster, I., & Rounsevell, M. (2006). Scenarios of future urban land use in Europe. Environment and Planning B: Planning and Design, 33(4), 619–636.CrossRefGoogle Scholar
  17. Rounsevell, M. D., & Metzger, M. (2010). Developing qualitative scenarios and storylines. Wiley Interdisciplinary Reviews: Climate Change, 1(4), 606–619.Google Scholar
  18. Verburg, P. H. (2006). Simulating feedbacks in land use and land cover change models. Landscape Ecology, 21(8), 1171–1183.CrossRefGoogle Scholar
  19. Zellner, M. L., Theis, T. L., Karunanithi, A. T., Garmestani, A. S., & Cabezas, H. (2008). A new framework for urban sustainability assessments: Linking complexity, information and policy. Computers, Environment and Urban Systems, 32(6), 474–488.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Corentin M. Fontaine
    • 1
  • Mustafa Mokrech
    • 2
  • Mark D. A. Rounsevell
    • 3
  1. 1.Namur Centre for Complex Systems and Research Group in Sustainable Development, Department of GeographyUniversity of NamurNamurBelgium
  2. 2.Environmental Institute of Houston, School of Science and Computer EngineeringUniversity of Houston Clear LakeHoustonUSA
  3. 3.Institute of Geography and the Lived Environment, School of GeosciencesUniversity of EdinburghEdinburghUK

Personalised recommendations