1. School of Geography FACULTY OF ENVIRONMENT School of Geography FACULTY OF ENVIRONMENT GEOG5060 GIS and Environment Dr Steve Carver Email: S.J.Carver@leeds.ac.ukS.J.Carver@leeds.ac.uk

2. School of Geography FACULTY OF ENVIRONMENT Outline: Introduction Multi-criteria evaluation (MCE) Multi-objective land allocation (MOLA) Examples Lecture 10. Land suitability modelling

3. School of Geography FACULTY OF ENVIRONMENT Introduction Land is a scarce resource essential to make best possible use identifying suitability for: agriculture forestry recreation housing etc.

4. School of Geography FACULTY OF ENVIRONMENT Sieve mapping Early methods Ian McHarg (1969) Design with Nature tracing paper overlays landscape architecture and facilities location Bibby & Mackney (1969) Land use capability classification tracing paper overlays optimal agricultural land use mapping

5. School of Geography FACULTY OF ENVIRONMENT GIS approaches Sieve mapping using: polygon overlay (Boolean logic) cartographic modelling Example uses: nuclear waste disposal site location highway routing land suitability mapping etc.

6. School of Geography FACULTY OF ENVIRONMENT Question… What problems or limitations are there with sieve mapping approach to: site location? routing? suitability analyses?

7. School of Geography FACULTY OF ENVIRONMENT Multi-criteria evaluation (MCE) Basic MCE theory: “Investigate a number of choice possibilities in the light of multiple criteria and conflicting objectives” (Voogd, 1983) generate rankings of choice alternatives simple linear programming algorithms multi-objective optimisation multi-dimensionality of planning problems

8. School of Geography FACULTY OF ENVIRONMENT Principles of MCE Methodology construct evaluation matrix… standardisation (normalisation) of criterion scores evaluation of matrix using MCE algorithms S 11 …..S I1 S =.. S 1J …..S IJ

9. School of Geography FACULTY OF ENVIRONMENT MCE techniques Many techniques most developed for evaluating small matrices suitability for large (GIS) matrices? layers = criterion scores cells or polygons = choice alternatives incorporation of levels of importance (weights) Incorporation of constraint maps e.g. ideal point analysis, weighted linear summation, hierarchical optimisation, etc.

10. School of Geography FACULTY OF ENVIRONMENT Example: weighted linear summation User weights Map 1Map 2Map 3Map 4 Evaluation matrix MCE routine Output Standardise

11. School of Geography FACULTY OF ENVIRONMENT Multi-objective land allocation (MOLA) Basic MOLA theory: procedure for solving multi-objective land allocation problems for cases with conflicting objectives based on information from set of suitability maps one map for each objective relative weights assigned to objectives amount of area to be assigned to each land use determines compromise solution that attempts to maximize suitability of lands for each objective given weights assigned

12. School of Geography FACULTY OF ENVIRONMENT Principles of MOLA Methodology construct ranked suitability maps for each objective using MCE decide on relative objective weights and area tolerances evaluate conflict demands on limited land via iterative process

13. School of Geography FACULTY OF ENVIRONMENT Example: protected areas Multi-layered system in Britain: National Parks, Areas of Outstanding Natural Beauty, Heritage Coasts, Special Areas of Conservation, Special Protection Areas, Sites of Special Scientific Interest, Nature Reserves, Ramsar Sites, National and Community Forests, Environmentally Sensitive Areas, National Scenic Areas, Regional Parks, Common Land, and Less Favoured Areas

14. School of Geography FACULTY OF ENVIRONMENT Protected areas in Britain

15. School of Geography FACULTY OF ENVIRONMENT Identifying wilderness” areas Wilderness Britain? continuum of environmental modification from “paved to the primeval” (Nash, 1981) the “Wilderness Continuum” concept measurable and mappable? remoteness from settlement remoteness from mechanised access apparent naturalness (lack of human artefacts) biophysical naturalness (ecological integrity)

16. School of Geography FACULTY OF ENVIRONMENT Apparent naturalnessBiophysical naturalnessRemoteness from mechanised access Remoteness from settlement Factor maps

17. School of Geography FACULTY OF ENVIRONMENT Possible solutions Stressing naturalnessStressing remotenessEqually weighted

18. School of Geography FACULTY OF ENVIRONMENT Wild and city park output Wild park with & without existing protected areas constraint City park with & without existing protected areas constraint

19. School of Geography FACULTY OF ENVIRONMENT MOLA Results: wild park vs city park Suitability for wild parkSuitability for city park MOLA results (yellow = wild park, red = city park, blue = constraints

20. School of Geography FACULTY OF ENVIRONMENT Conclusions Few GIS packages provide MCE functionality (e.g. Idrisi32) Most GIS provide facilities for building MCE analyses (e.g. Arc/Info GRID) Important method for: site and route selection land suitability modelling

21. School of Geography FACULTY OF ENVIRONMENT Workshop Running MCE in Arc/Info GRID

22. School of Geography FACULTY OF ENVIRONMENT Practical MCE in GRID Task: Locate suitable sites for a wind farm in the Yorkshire Wolds using MCE Data: The following datasets are provided… Digital elevation model (50m resolution 1:50,000 OS Panorama data) Contour data (10m interval 1:50,000 OS Panorama data) ITE land cover map (25m resolution) Population data (200m resolution) Roads (1:250,000 Meridian data) Wind speed data

23. School of Geography FACULTY OF ENVIRONMENT Practical Steps: 1.Decide on criterion/factors required (including any constraints) 2.Pre-process factor and constraint maps (including standardisation of factor maps) 3.Decide on factor weights 4.Build and run MCE model 5.Display results

24. School of Geography FACULTY OF ENVIRONMENT Practical Experience with building and running MCE models in Arc/Info GRID Familiarity with MCE techniques and data requirements

25. School of Geography FACULTY OF ENVIRONMENT Next week… Spatial Decision Support Systems principles and theory examples online SDSS Workshop: Web-based systems Practical: Running web-based systems