Multi-Criteria Analysis A framework for spatial decision making

1. Multi-Criteria Analysis A framework for spatial decision making MCAS-S development partnership

2. Challenges in Environmental Management • Introducing multi-criteria analysis (MCA) • What makes a good multi criteria analysis? • The framework • Example - Where to buy coffee. Step 1-5 • Introducing MCAS-S software • What is MCAS-S? • When to use MCAS-S • Example – Where to live in Australia • Where to get data • Getting data into MCAS-S Outline

3. Challenges in environmental management Decisions often include environmental, social, and economic aspects. Problems can be difficult to define, are still evolving, have no “right answer”. These “wicked problems” may benefit from multi-criteria analysis. Cotter Dam Canberra, Image courtesy of Department of Agriculture and Water Resources image library Photographer Joshua Smith

4. Introducing multi-criteria analysis (MCA) We all use multi-criteria analysis (MCA). For example: “Where should I get my coffee?” This module is about formalising, recording and refining the decision-making process.

5. What makes a good multi-criteria analysis? • MCA can: • Include social, economic and environmental information • Evaluate options, trade-offs and consequences • Incorporate stakeholder values, public opinion and policy goals • Be spatial or non-spatial • Good MCAs are: • Structured • Defensible and transparent • Able to include qualitative and quantitative data • Broader than cost-benefit analysis

6. The framework • Define or identify components for evaluation • Objective • Criteria • Inputs • Assemble or collect data • Explore and combine data • Rank or scale data • Apply weights • Calculate final score and develop options • Review; repeat the process to refine

7. Example: Where to buy coffee? Step 1. Where should I get my coffee? a) Objective Define the problem, the decision context, criteria and inputs. Bang for my buck Time b) Criteria Price Distance Quality c) Inputs

8. Example: Where to buy coffee? Steps 2-4.

9. Summary Next Multi-criteria analysis (MCA) • spatial MCA using MCAS-S • concepts applicable to other tools • means-to-an-end diagram • example – Where to live in Australia? • a decision making framework • many tools exist • spatial or non spatial

10. What is MCAS-S ? What is MCAS-S? • Free spatial tool • Supports MCA as well as other spatial analysis • Addresses ‘where’ questions • Flexible and simple to use • Shows all the data that informs a decision • Updates on the fly • Useful for workshops http://www.agriculture.gov.au/abares/aclump/multi-criteria-analysis/mcas-s-tool • ‘Where can we most effectively invest in revegetation?’ • ‘Where is the greatest risk to soil resources?’

11. When to use MCAS-S ? Do I want to know “where”? Can I define my area? Use a non-spatial tool Can I make my data spatial? • Consider other tools, e.g.: • cost benefit analysis • cost effectiveness • cost utility analysis Do my input data have the different units? Do I need a flexible framework? Yes Consider other tools! Is transparency important? No Use MCAS-S

12. Exercise: Where to live in Australia Step 1. Define Exercise • ‘Where to live in Australia?’ • There is no single ‘right’ answer • Step 1: Define objectives, criteria and data • A means-to-an-end diagram can express our objective and criteria • Our first example priorities are: proximity to skiing and coast and urban land uses. • Open “Where to live - blank.mcas” Where to live? Close to recreation Urban setting Coast Land use Skiing

13. The MCAS-S interface Data and Tools Workspace to assemble your model Viewer

14. Step 2: Assemble data Select all three datasets: Click holding the Shift key Drag into the workspace

15. Step 3: Explore data • Rename layers: • ‘dist_coast’ to ‘Close to coast’ • ‘dist_ski’ to ‘Close to skiing’ • ‘landuse’ to ‘Suitable land use’ • Classify data: • ‘Close to coast’ and ‘Close to skiing’ • Distribution Log • Allocate classes in reverse order • ‘Suitable land use’ – select 3 classes: • Not suitable (blue) • Partly suitable (green) = Nature conservation, Other protected areas, Minimal use, Production forestry, Plantation forestry, Agricultural classes (grazing, cropping and horticulture), Intensive animal and plant production • Suitable (red) = Urban intensive uses

16. Step 3: Combine data • Combine inputs using a ‘Composite’ • Drag Composite tool into the workspace • Rename to ‘Where to live’ • Set Weighting for each layer to 1 • Overlay ‘states’

17. Step 4: Develop options • Change the weightings • Skiing is more important than diving • Change the ranks • Reclass ‘Suitable land use’ to prioritise agriculture • Consider the results

18. 1. Define objective, criteria and inputs Objective • What is the overall ambition? Criteria • Measures by which options will be judged. • “What is the difference between a good and bad choice?” Input data • Simple, Measurable, Available, Relevant, Timely • Avoid overlap

19. 2. Assemble inputs – MCAS-S data

20. 2. Assemble data inputs - Classification Continuous Categorical • 2 - 10 classes • Use a consistent classification: ‘Low’ to ‘High’ or ‘Unsuitable’ to ‘Suitable’ • MCAS-S scales between ~0 to 1 • Name classes for reference or reporting

21. 3. Explore and combine data • How to combine inputs? • How can the input data best inform your objective? • Are any inputs critical? • What type of answer do you want? • - Yes / No • - Scale or shortlist • - Count • MCAS-S tools • - Two-way (coincidence) • - Multi-way (coincidence or count) • Mask, Count, • - Composite (assign weights): • Manual, Formula, Analytical Hierarchy Process Explore Compare Combine

22. Combine tools: Two-way Requirements: • Two layers ONLY • Scaled or classified (not Raw) • Scales don’t need to align • Coincidence

23. Combine tools: Multi-ways Requirements: • Two or more layers • Scaled or classified (not Raw) • Scales don’t need to align • Coincidence (mask) or • Conditions met (count)

24. Combine tools: Composites Requirements: • Two or more layers • Raw, scaled or classified • Scales align • Specify weights: • Manual • Function editor • Analytical Hierarchy Process (AHP)

25. Combine tools: Matrix

26. 4. Develop options - come to a decision Change weights Include or exclude inputs Tools include: • Sensitivity analysis • Correlation analysis • What is an option? • A complete, internally coherent and distinct solution to the problem. • For each option: • Is the result robust / expected / acceptable / practical? • Estimate consequences • Choosing between options: • Is there a dominant option? • What trade-offs or choices distinguish between options?

27. Where to live?

28. 5. Review and report - analysis Export • Google Earth • To another software for further analysis or presentation Mask layers can be selected Proportion or cell counts Analysis • Area by region • Share of region • Report on a point (city)

29. Where to get MCAS-S data Data packs hold all the data you need for a project: • Primary • Overlay • Mask • Classified • Project based, e.g. Lantana exercise • National – datasets useful for NRM, public, consistent • Biophysical – climate, soils, terrain • Social – remoteness index, SEIFA, employment • Economic – land value, farm cash income, rate of return • Environment – endangered species and communities, protected areas • Land use, land cover, ground cover, vegetation, agriculture • Infrastructure – distance to towns, roads, coast, rivers • Water – soil moisture, distance to water features • Pests and weeds • Fire – frequency, time since last burn TERN data portal http://mcas.auscover.net.au/mcas-s/

30. MCAS-S data requirements • Identical extents means that your data overlay each other • Choose a projection that enables the calculation of area e.g.: • National – Australian Albers or Lamberts • Regional – Transverse Mercator • Local – Geographic MCAS-S uses spatial data that conforms in terms of: • Extent • Projection Albers Geographic

31. www.ecoed.org.au