Background. Advanced Remote Sensing. Background contd. Land is a scarce resource. Lecture-5

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1 Advanced Remote Sensing Lecture-5 Multi Criteria Evaluation contd. Background Multicriteria analysis appeared in the 1960s as a decisionmaking tool. It is used to make a comparative assessment of alternative projects or heterogeneous measures. With this technique, several criteria can be taken into account simultaneously in a complex situation. The method is designed to help decision-makers to integrate the different options, reflecting the opinions of the actors concerned, into a prospective or retrospective framework. 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. Background contd. Land is a scarce resource Multicriteria evaluation be organized with a view to produce a single synthetic conclusion at the end of the evaluation or, on the contrary, with a view to producing conclusions adapted to the preferences and priorities of several different partners. Multicriteria analysis is a tool for comparison in which several points of view are taken into account, and therefore is particularly useful during the formulation of a judgment on complex problems. The analysis can be used with contradictory judgment criteria (for example, comparing jobs with the environment) or when a choice between the criteria is difficult. Essential to make best possible use Identifying suitability for: a) agriculture b) forestry c) recreation d) housing e) etc. MCE can provide solutions»non-monetary decision making tool»developed for complex problems

2 GIS Approaches Decision Polygon overlay (Boolean logic) Cartographic modelling Example uses: a) nuclear waste disposal site location b) highway routing c) land suitability mapping d) solid waste management planning e) habitat mapping f) etc. Decision is a choice between alternatives Decision frame: the set of all possible alternative Candidate set: the set of all locations / pixels that are being considered Decision set: the areas assigned to a decision (one alternative) Criterion Decision rule Criterion is some basis for a decision. Two main classes are: Factor: enhances or detracts from the suitability of a land use alternative Constraint: limits the alternatives Can be a continuum from crisp decision rules (constraints) to fuzzy decision rules (factors) Goal or target: some characteristic that the solution must possess (a positive constraint) Decision rule is 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 Objective: the measure by which the decision rule operates Evaluation: the actual process of applying the decision rule

3 Kinds of Evaluations Basic MCE theory Single-criterion evaluation Multi-criteria evaluation: to meet one objective, several criteria must be considered Multi-objective evaluations: Complementary objectives: non-conflicting objectives (e.g., forest and residential land) Conflicting objectives: both cannot exist at the same place, same time (e.g., reserve forest and timber licenses) 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: Boolean overlays (polygon-based methods) Weighted linear combinations (WLC) (raster-based methods) MCE Techniques Principles of MCE 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) Methodology Determine criteria (factors / constraints) to be included Standardization (normalization) of factors / criterion scores Determining the weights for each factor Evaluation using MCE algorithms Sensitivity analysis of results

4 Determine the criteria to be included Factor normalization Oversimplification of the decision problem could lead to too few criteria being used Using a large number of criteria reduces the influence of any one criteria They should be comprehensive, measurable, operational, non-redundant, and minimal Often proxies must be used since the criteria of interest may not be determinable (e.g., % slope is used to represent slope stability) A multistep, process that considers the literature, analytical studies and, possibly opinions Standardization of the criteria to a common scale Need to compare apples to apples, not apples to oranges Good: For example: Output Output Distance from a road (km) Slope (%) Wind speed Poor: 0 0 low high low Input Consider Range (convert all to a common range) Meaning (which end of the scale = good) Input high Fuzzy Membership Functions Factor Normalization: example Used to standardize the criterion scores fuzzy membership fuzzy membership monthly maximum T Cholera Health Risk Prediction in Southern Africa the relation between temperature and risk Graphs of the Fuzzy Memberships within IDRISI (Based on Eastman 1999)

5 Determine the weights Analytical hierarchy process 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 some 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 clear process. One of the more commonly-used methods to calculate the weights. (This is a freely downloadable ArcGIS script) MCE Algorithms Example: weighted linear summation The most commonly used decision rule is the weighted linear combination S = w i x i x c j Map 1 Map 2 Map 3 Map 4 Standardise where: S is the composite suitability score x factor scores (cells) w weights assigned to each factor c constraints (or boolean factors) -- sum of weighted factors -- product of constraints (1-suitable, 0-unsuitable) User weights Output Evaluation matrix MCE routine

6 Sensitivity analysis Sensitivity analysis contd. Choice for criteria (e.g., why included?) Reliability data 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 the sensitivity of the solution to minor changes Only addresses one of the sources of uncertainty involved in making a decision (i.e., the validity of the information used) 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) Example of MCE: Wind Farm Siting Preliminary Siting Factors Spatial Analytical Hierarchy Process Wind farm siting Find the best wind farm sites based on siting factors Alternatives Location infinite Divide the space into squares/cells (200m * 200m) Evaluate each cell based on the siting factors 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 Appalachian State University

7 Siting Steps (MCE) AHP 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 Factor Layers Wind Turbine visibility--viewshed (Turbine: 50m; Observer: 1.5m; Visual distance: 20mi)

8 Wind Turbine Viewshed Size Visibility Factor Viewshed Size Red 505km2 Green--805km2 Blue--365km2 Visibility Factor--# of People in Viewshed Final Score Layer 2000 census block data

9 Candidate Sites Constraints (binary) Sites Waste Dumping Site Selection (MCE)

10 Waste Dumping Site Selection (MCE)