Modeling With Rasters II: Intro to MCDA

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12 Terms

1
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What is MCDA?

a framework used to evaluate and prioritize multiple and often conflicting criteria to supports-decision making

  • has no priorities

2
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Why do we use MCDA in GIS

  • Helps make complex spatial decisions more structured and transparent

  • integrates both qualitative and quantitative factor

  • combines spatial data with expert knowledge and stakeholder input

3
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Components of an MCDA problem?

  • a goal or set of goals to achieve

  • multiple decisions makers involved in decisions making process

  • conflicting preferences among decisions maker

  • potentially many alternatives to evaluate

  • each alternative is evaluation based on multiple criteria

  • decisions are often made under uncertainty

<ul><li><p>a goal or set of goals to achieve</p></li><li><p>multiple decisions makers involved in decisions making process</p></li><li><p>conflicting preferences among decisions maker</p></li><li><p>potentially many alternatives to evaluate</p></li><li><p>each alternative is evaluation based on multiple criteria</p></li><li><p>decisions are often made under uncertainty</p></li></ul><p></p>
4
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Steps for Spatial MCDA mapping

(1) define the goal

  • what is being mapped what information is needed(objectives: maximization, or minimization)

(2) Criterion Maps

  • will selected concurrent scale

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What do Criterion maps do?

determine the criteria for inclusion

should all be measureable

<p>determine the criteria for inclusion</p><p>should all be measureable</p>
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Constraint Maps

  • displays limitations on the values that the attribute may assume

  • limit the alternatives under consideration and are boolean in nautre(act as a mask)

<ul><li><p>displays limitations on the values that the attribute may assume</p></li><li><p>limit the alternatives under consideration and are boolean in nautre(act as a mask)</p></li></ul><p></p><p></p>
7
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Factor Maps

  • enhance or detract from suitability of an alternative

<ul><li><p>enhance or detract from suitability of an alternative </p></li></ul><p></p>
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Natural Scales

  • needed for constructing criterion maps

  • well established, common usage and interpretation

  • directly measurable, physical or observable data

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Constructed Scales

  • subjective in nature, created by assigning values based on judgment, ranking on classification

  • for construction of criterion maps

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Proxy Measures

  • mo obvious measures available to directly estimate an attribute/objective, so something stands in for it

  • for construction of criterion maps

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Deterministic Scales

  • for construction of criterion maps

  • known with certainty or measured precisely

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Fuzzy Scales

  • For construction of criterion maps

  • lacking a solid boundary, involves uncertainty or vagueness, with values assigned based on likelihood or membership to a group

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