Land Use Dynamics and Coastal Management

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


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.


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


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Corentin M. Fontaine
    • 1
    Email author
  • 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

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