Spatial modeling with GIS. Multi-criteria evaluation

Transcription

1 Spatial modeling with GIS Multi-criteria evaluation

2 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example: MCE Multi-objective land allocation (MOLA) Example Roadmap

3 Multi-criteria evaluation is one of many possible models we can use. We use models to: Support a decision process (find a solution) To do what if analyses Analysis vs models? (Chaps 13, 14 & 15) Search for patterns or anomalies (-> hypotheses) (A) Data manipulation (A) Implementing ideas and hypotheses (M) Experimenting with policy options and scenarios (M) Introduction

4 Land is a scarce resource Therefore it is essential to make the best possible use of it But, identifying suitability for what? agriculture forestry recreation housing etc. Conflicting demands Introduction

5 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 Sieve mapping

6 Sieve mapping: Boolean logic (typically, polygon overlay) How is boolean logic normally presented? implemented through cartographic modelling Example uses: nuclear waste disposal site location Highway / transmission line routing land suitability mapping GIS approaches

7 Question What problems or limitations are there with the sieve / boolean overlay mapping approach?

8 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example: MCE Multi-objective land allocation (MOLA) Example: MOLA Roadmap

9 Single-criterion evaluation (e.g., do I have enough money to see a movie?) Multi-criteria evaluation: to meet a single objective, several criteria must be considered (e.g., do I have enough money to see a movie if so, do I want to see an action flick or a horror movie, which theatre is closest?) [MCE] Multi-objective evaluations: Complementary objectives: non-conflicting objectives, multiple criteria (e.g., extensive grazing and recreational hiking) Conflicting objectives: both cannot exist at the same place, same time (e.g., ecological reserves and timber licenses) [MOLA] Kinds of evaluations

10 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. Two basic methodologies are commonly used: Boolean overlays (polygon-based methods) [AND] Weighted linear combinations [WLC] (raster-based methods) Why is AND considered low risk? Multi-criteria evaluation

11 Multi-criteria analysis appeared in the 1960s as a decisionmaking tool, used to make a comparative assessment of alternative projects. With this technique, several criteria can be taken into account simultaneously in a complex situation. The method is designed to help decision-makers integrate the different options, reflecting the opinions of those concerned. Participation of the decision-makers in the process is a central part of the approach. The results are usually directed at providing operational advice or recommendations for future activities. Multi-criteria evaluation

12 Multi-criteria evaluation be organised with a view to producing a single synthetic conclusion at the end of the evaluation or, on the contrary, with a view to producing multiple conclusions adapted to the preferences and priorities of several different partners. Multi-criteria analysis is a tool for comparison in which several points of view are taken into account, and therefore is particularly useful when working on complex problems. The analysis can be used with contradictory judgement criteria (for example, comparing jobs with the environment) or when a choice between the criteria is difficult. Multi-criteria evaluation

13 Non-monetary decision making tool Developed for complex problems, where uncertainty can arise if a logical, well-structured decision-making process is not followed Reaching consensus in a (multidisciplinary) group is difficult to achieve. MCE

14 Many techniques (decision rules) Most developed for evaluating small problem sets (few criteria, limited candidate sets) Some are suitable for large (GIS) matrices layers = criteria cells or polygons = choice alternatives Incorporation of levels of importance (weights WLC methods) Incorporation of constraints (binary maps) MCE techniques

15 Comparison of Boolean and MCE (WLC) Roads Low slopes Labour Away from parks Not in park Multi-criteria evaluation and GIS by J.R. Eastman 1999 using the example of Nakura, Kenya

16 Cons: Dynamic problems strongly simplified into a linear model Static, lacks the time dimension Controversial method too subjective? Pros: Gives a structured and traceable analysis Possibility to use different evaluation factors makes it a good tool for discussion Copes with large amounts of information It works! MCE pros and cons

17 MCE is not perfect quick and dirty -option, unattractive for real analysts but what are the alternatives? - system dynamics modelling impossible for huge socio-technical problems - BOGGSATT is not satisfactory (Bunch of Old Guys/Gals Sitting Around a Table Talking) MCE is good for complex spatial problems Emphasis on selecting good criteria, data collection and sensitivity analysis MCE pros and cons

18 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example: MCE Multi-objective land allocation (MOLA) Example: MOLA Roadmap

19 Decision: a choice between alternatives Decision frame: the set of all possible alternatives [ Parks Forestry ] the choices we are making Candidate set: the set of all locations [pixels] that are being considered [ all Crown lands ] where can those choices be made? Decision set: the areas assigned to a decision (one alternative) [ all pixels identified as Park ] Definitions

20 Decision rule: the procedure by which criteria are combined to make a decision. Can be: Functions: numerical, exact decision rules Heuristics: approximate procedures for finding solutions that are good enough (Travelling salesperson problem) Objective: the measure by which the decision rule operates (e.g., identify potential parks) Evaluation: the actual process of applying the decision rule Definitions

21 Goal or target: some characteristic that the solution must possess (a positive constraint) E.g., 12% of the land base identified as park Criterion: some basis for a decision: Constraints: limit the alternatives (N) [e.g., private /crown lands] [boolean: 0/1] Factors: enhance or detract from the suitability of a land use alternative (OIR) [e.g., distance from a road] Can be a continuum from crisp decision rules (constraints) to fuzzy decision rules (factors) Definitions

22 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example Multi-objective land allocation (MOLA) Example Roadmap

23 Methodology 1. Identify the decision to be made, and set the goal. 2. Determine criteria (factors / constraints) to be included 3. Standardization (normalization) of factors / criterion scores 4. Determining the weights for each factor 5. Evaluation using MCE algorithms 6. Sensitivity analysis of results Principles of MCE

24 Oversimplification of the decision problem could lead to too few criteria being used Why could oversimplification be an issue? Too many What criteria about included over-complication reduces / excessive the influence details? of any one criteria It is said that criteria should be: Criteria should be comprehensive, measurable, operational, non-redundant, and minimal comprehensive, measurable, operational, Often proxies must be used since the criteria of interest may non-redundant and not be determinable (e.g., minimal. % slope is used to represent slope stability; SES represents health ) What does this mean? A multistep, iterative process that considers the literature, analytical studies and, possibly, opinions What are proxies? Why are they so important in so many geospatial analyses? Determine the criteria to be included

25 Standardization of the criteria to a common scale (commensuration) Need to compare apples to apples, not apples to oranges to walnuts. For example: Distance from Rd Slope Located far from a road (km) Low slope (%) Wind speed (km/hr) Good: 255 Output 255 Output Consider Range (convert all to a common range) Meaning (which end of the scale = good?) Poor: 0 close Input 0 far low Input Need to transform to a common meaning high Factor normalization

26 Used to standardize the criterion scores Linguistic concepts are inherently fuzzy (hot/cold; short/tall) Wind speed? Becoming a spatial thinker Fuzzy membership functions Graphs of the Fuzzy Memberships within IDRISI (Based on Eastman 1999)

27 fuzzy membership fuzzy membership monthly maximum T Below 28.5 there is no risk, above 37.5 it can t rise. Malaria Health Risk Prediction in Southern Africa the relation between temperature and risk Factor normalization: example

28 A decision is the result of a comparison of one or more alternatives with respect to one or more criteria that we consider relevant for the task at hand. Among the relevant criteria, we consider some as more important and others as less important; this is equivalent to assigning weights to the criterion according to their relative importance. By normalizing the factors we make the choice of the weights an explicit process. Determine the weights

29 AHP: One of the more commonly-used methods to calculate the weights. 0 Saaty s analytical hierarchy process calibrates each criteria into a numerical scale. The process works by providing a sequence of pairs of criteria (i.e. each criterion is evaluated by comparing it with all the others in a sequence of pairings). For each pairing, participants are asked to rank, on a scale from (e.g.) -9 to +9, how important that criterion is compared with the other one. Continuing with this procedure, the mathematical model eventually gives a relative weight for each criterion, and the summation is normalized to 100%. Analytical hierarchy process

30 0 123aHP My Choice, My Decision online Analytical Hierarchical Process (AHP) program demonstrates how the scores can be determined. Analytical hierarchy process

31 The most commonly used decision rule is the weighted linear combination where: S is the composite suitability score -- sum of weighted factors (sigma) w i weights assigned to each factor ( determined through AHP, for example; sum to 1.0) x i factor scores (cells with normalized values) x -- times -- product of constraints (pi) c i constraints (or boolean factors) (1-suitable, 0-unsuitable) MCE Algorithms S = w i x i x c j

32 S = w i x i + The two input rasters have been reclassified to a common measurement scale of 1 to 3 (x i ). Each raster is assigned a weight (w i - %). The cell values (x) are multiplied by their weight (w), and the results are added together to create the output raster. For example, consider the top left cell. The values for the two inputs become (2 * 0.75) = 1.5 and (3 * 0.25) = The sum of 1.5 and 0.75 is Because the output raster from ESRI s Weighted Overlay is integer, the final value is rounded to 2. 1 MCE Algorithms: Factors

33 c j Lakes Steep Areas Suitable Areas (in white) 0 Two input rasters have been reclassified (to 0/1) in order to highlight those areas that are absolute constraints (e.g., a lake in the first raster, too-steep areas in the second). [0 times any value = 0] MCE Algorithms: Constraints

34 w i x i x c j = S Factors Constraints Wt d Suitability Map 0 The final results reflect the sum of the factors multiplied by the product of the constraints. MCE Algorithms

35 Map 1 Map 2 Map 3 Map 4 Standardise User weights Evaluation matrix MCE routine Output MCE Example: weighted linear summation Example

36 A major difference between boolean (sieve methods) and MCE (WLC) is that for boolean methods every condition must be met before an area is included in the decision set [AND]. There is no distinction between those areas that fully meet the criteria and those that are at the edges of the criteria. (All = good) 1 0 A 1000 m buffer from a road There is also no means for weighting the factors differentially in boolean methods. MCE

37 Choice for criteria (e.g., why included?) Reliability data (e.g., elevation vs precip) Choice for weighing factors is subjective Will the overall solution change if you use other weighing factors? How stable is the final conclusion? Sensitivity analysis: vary the scores / weights of the factors to determine if the solution is sensitive to minor changes; remove factors Sensitivity analysis

38 Only addresses one of the sources of uncertainty involved in making a decision (i.e., the validity of the information used data uncertainty) A second source of uncertainty concerns future events that might lead to differentially preferred outcomes for a particular decision alternative. Decision rule uncertainty should also be considered (? MCE itself) Sensitivity analysis

39 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example: MCE Multi-objective land allocation (MOLA) Example: MOLA Roadmap

40 Dennis Scanlin (Department of Technology) Xingong Li Chris Larson (Department of Geography & Planning) Appalachian State University Example MCE: Wind Farm Siting

41 Wind farm siting Find the best wind farm sites based on siting factors Alternatives Location infinite (lat/lon Divide the space into squares/cells (200m * 200m) Evaluate each cell based on the siting factors Spatial Analytical Hierarchy Process

42 Accessibility to roads Distance to primary roads Distance to secondary roads Distance to rural roads Accessibility to transmission lines Distance to 100K lines Distance to 250K lines Distance to above250k lines Wind power (or wind speed) Visibility Viewshed size # of people in viewshed Preliminary Siting Factors

43 Factor generation Distance calculation Visibility calculation Factor standardization (0 100) Each factor is a map layer Factor weights determination by AHP Final score Weighted combination of factors Exclusion areas Siting Steps (MCE)

44 AHP

45 Factor Layers

46 (Turbine: 50m; Observer: 1.5m; Visual distance: 20mi) Wind Turbine visibility--viewshed

47 Red 505km 2 Green--805km 2 Blue--365km 2 Software tool developed to calculate viewshed size for each cell Wind Turbine Viewshed Size

48 Computational expensive About 700,000 cells Each cell requires 10 seconds About 76 days Parallel computing 12 computers Each computer runs two counties About cells 6 days Succeed with 3000 cells but failed with 55,000 cells Visibility Factor Viewshed Size

49 2000 census block data Visibility Factor--# of People in Viewshed

50 Final Score Layer S = w i x i x c j

51 Candidate Sites

52 Constraints (binary) S = w i x i x c j

53 Sites left S = w i x i x c j

54 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example MCE Multi-objective land allocation (MOLA) Example Roadmap

55 Basic MOLA theory: procedure for solving multi-objective land allocation problems for cases with conflicting objectives (Parks vs Logging; Industrial vs Residential) 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 Multi-objective land allocation

56 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 Principles of MOLA

57 Yellow line splits conflicting region into two equal parts (assuming equal wts to each objective). Since each objective lost some area, additional cells must be added to the solution. 255 Non-conflicting choices for Objective 2 Conflicting choices: Optimal for both objectives Objective 2 0 Unsuitable choices Non-conflicting choices for Objective 1 Assumption is that areas with low scores would not be considered suitable for that objective. It is only in those areas where both objectives have high scores that a conflict emerges Objective 1 MOLA decision space In Lab 2 you will just be working with the one objective, and selecting a score that satisfies the area criteria.

58 Outline: Introduction Multi-criteria evaluation (MCE) Definitions Principles of MCE Example: MCE Multi-objective land allocation (MOLA) Example: MOLA Roadmap

59 Multi objective land allocation (MOLA) for zoning Ghamishloo Wildlife Sanctuary in Iran Shila Hajehforooshnia, Alireza Soffianian, A. Salman Mahiny, Sima Fakheran Journal for Nature Conservation 19 (2011)

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64 Only a few GIS packages provide MCE functionality directly (e.g., Idrisi, which includes AHP functions) Most GIS provide facilities for building MCE models, such as the set of Overlay tools (within Spatial Analyst Tools) provided in ArcMap (as well as the ArcGIS ModelBuilder) Important method for: Site and route selection land suitability modelling Conclusions

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