1. Lecture 9GEOG2590 - GIS for Physical Geography1 Outline: – Introduction – Multi-criteria evaluation (MCE) – Multi-objective land allocation (MOLA) – Examples Lecture 9. Land suitability modelling

2. Lecture 9GEOG2590 - GIS for Physical Geography2 Introduction Land is a scarce resource –essential to make best possible use –identifying suitability for:  agriculture  forestry  recreation  housing  etc.

3. Lecture 9GEOG2590 - GIS for Physical Geography3 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

4. Lecture 9GEOG2590 - GIS for Physical Geography4 GIS approaches Sieve mapping using: –polygon overlay (Boolean logic) –cartographic modelling –Example uses:  nuclear waste disposal site location  highway routing  land suitability mapping  etc.

5. Lecture 9GEOG2590 - GIS for Physical Geography5 Question… What problems or limitations are there with the sieve mapping approach?

6. Lecture 9GEOG2590 - GIS for Physical Geography6 Multi-criteria evaluation 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

7. Lecture 9GEOG2590 - GIS for Physical Geography7 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

8. Lecture 9GEOG2590 - GIS for Physical Geography8 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.

9. Lecture 9GEOG2590 - GIS for Physical Geography9 Example: weighted linear summation User weights Map 1Map 2Map 3Map 4 Evaluation matrix MCE routine Output Standardise

10. Lecture 9GEOG2590 - GIS for Physical Geography10 Multi-objective land allocation 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

11. Lecture 9GEOG2590 - GIS for Physical Geography11 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

12. Lecture 9GEOG2590 - GIS for Physical Geography12 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

13. Lecture 9GEOG2590 - GIS for Physical Geography13 Protected areas in Britain

14. Lecture 9GEOG2590 - GIS for Physical Geography14 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)

15. Lecture 9GEOG2590 - GIS for Physical Geography15 Factor maps Apparent naturalnessBiophysical naturalnessRemoteness from mechanised access Remoteness from settlement

16. Lecture 9GEOG2590 - GIS for Physical Geography16 Possible solutions Stressing naturalnessStressing remotenessEqually weighted

17. Lecture 9GEOG2590 - GIS for Physical Geography17 Wild and city park output Wild park with & without existing protected areas constraint City park with & without existing protected areas constraint

18. Lecture 9GEOG2590 - GIS for Physical Geography18 MOLA Results: wild park vs city park Suitability for wild parkSuitability for city park MOLA results (yellow = wild park, red = city park, blue = constraints

19. Lecture 9GEOG2590 - GIS for Physical Geography19 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

20. Lecture 9GEOG2590 - GIS for Physical Geography20 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

21. Lecture 9GEOG2590 - GIS for Physical Geography21 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

22. Lecture 9GEOG2590 - GIS for Physical Geography22 Practical Experience with building and running MCE models in Arc/Info GRID Familiarity with MCE techniques and data requirements

23. Lecture 9GEOG2590 - GIS for Physical Geography23 Next week… Spatial Decision Support Systems –principles and theory –examples –online SDSS Practical: Siting radioactive waste disposal facilities using web-based SDSS