Suitability Modeling: Best Practices

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This document discusses suitability modeling and multi-criteria decision analysis (MCDA) techniques. It describes suitability modeling as a process that involves identifying relevant criteria, converting criteria to a common scale, weighting criteria based on their importance, and combining them to determine overall suitability. The document outlines several MCDA methods including weighted overlay, pairwise comparison, and rating to determine criteria weights. It provides an example of using a pairwise comparison matrix and normalization to calculate criteria weights to model riparian quality and threats.

Technology Business

Suitability Modeling
BEST
PRACTICES

GRETCHEN N. PETERSON, PETERSONGIS
Author of GIS Cartography: A Guide to Effective Map Design

What is suitability modeling?
Input criteria considerations

What is suitability modeling?
Input criteria considerations
Converting to a common scale

What is suitability modeling?
Input criteria considerations
Converting to a common scale
How to weight criteria

MCDA
Multi Criteria Decision Analysis

Suitability Modeling
Habitat Suitability Modeling

weighted overlay
Weighted Summation
multi-criteria evaluation

test scores
median age
educational
attainment

test scores median age
0-100% 0-100
percentage years

educational attainment
high school, some college, etc.
categorical

Delphi

iteration
anonymity
documentation

trade-off analysis
pairwise comparison
ranking
rating

ranking

1 Educational attainment
2 Median age
3 Test scores

rating
point allocation
50 Educational attainment
40 Median age
10 Test scores

rating
ratio estimation
100 Educational attainment

75 Median age

20 Test scores

rating
ratio estimation
100 5 Educational attainment

75 3.75 Median age

20 1 Test scores

9.75

rating
ratio estimation
100 5 0.51 Educational attainment
75 3.75 0.39 Median age
20 1 0.10 Test scores
1.00
9.75

trade-off analysis

A is better than B
B is better than C

pairwise comparison

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educational
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attainment

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median age

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test scores

pairwise comparison

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educational
X 3 9
attainment

X 6
median age

X
test scores

pairwise comparison

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educational
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attainment

1 6
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test scores

pairwise comparison

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educational
1 3 9
attainment

1/3 1 6
median age

1/9 1/6 1
test scores

pairwise comparison

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educational
1 3 9
attainment

0.33 1 6
median age

0.11 0.17 1
test scores

pairwise comparison

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educational
1 3 9
attainment

0.33 1 6
median age

0.11 0.17 1
test scores

1.44 4.17 16

normalized comparison matrix

es
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me

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di

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me

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educational 1/1.44 3/4.17 9/16
attainment

0.33/1.44 1/4.17 6/16
median age

0.11/1.44 0.17/4.17 1/16
test scores

normalized comparison matrix

es
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in al

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ta on
me

sc
at ati

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di

st
uc

me

te
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educational 0.69 0.72 0.56
attainment

0.23 0.24 0.38
median age

0.08 0.04 0.06
test scores

normalized comparison matrix

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average
educational 0.69 0.72 0.56
attainment

0.23 0.24 0.38
median age

0.08 0.04 0.06
test scores

normalized comparison matrix

es
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in al

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ag

or
ta on
me

sc
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di

st
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me

te
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0.67
educational 0.69 0.72 0.56
attainment

0.28
0.23 0.24 0.38
median age

0.18
0.08 0.04 0.06
test scores

high
Riparian Quality

focus
effort
here
low

low
high
Riparian Threat Level

Quality Model
Canopy density 0.317
Stream cover 0.200
Riparian width 0.183
Understory 0.183
Slope 0.117

Threat Model

Encroachment 0.690
Landscape Type 0.251
Understory 0.059

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1. Suitability Modeling BEST PRACTICES GRETCHEN N. PETERSON, PETERSONGIS Author of GIS Cartography: A Guide to Effective Map Design

2. What is suitability modeling?

3. What is suitability modeling? Input criteria considerations

4. What is suitability modeling? Input criteria considerations Converting to a common scale

5. What is suitability modeling? Input criteria considerations Converting to a common scale How to weight criteria

6. MCDA Multi Criteria Decision Analysis Suitability Modeling Habitat Suitability Modeling weighted overlay Weighted Summation multi-criteria evaluation

7. test scores

8. test scores median age

9. test scores median age educational attainment

10. test scores median age educational attainment

11.

12.

13. comprehensiveness of data

14.

15. converting to a common scale

16. test scores median age 0-100% 0-100 percentage years educational attainment high school, some college, etc. categorical

17.

18. decision maker

19.

20.

21. Delphi iteration anonymity documentation

22. trade-off analysis pairwise comparison ranking rating

23. ranking 1 Educational attainment 2 Median age 3 Test scores

24. rating point allocation 50 Educational attainment 40 Median age 10 Test scores

25. rating ratio estimation 100 Educational attainment 75 Median age 20 Test scores

26. rating ratio estimation 100 5 Educational attainment 75 3.75 Median age 20 1 Test scores 9.75

27. rating ratio estimation 100 5 0.51 Educational attainment 75 3.75 0.39 Median age 20 1 0.10 Test scores 1.00 9.75

28.

29. trade-off analysis A is better than B B is better than C

30. pairwise comparison es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational X attainment X median age X test scores

31. Thomas L. Saaty

32. pairwise comparison es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational X 3 9 attainment X 6 median age X test scores

33. pairwise comparison es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational 1 3 9 attainment 1 6 median age 1 test scores

34. pairwise comparison es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational 1 3 9 attainment 1/3 1 6 median age 1/9 1/6 1 test scores

35. pairwise comparison es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational 1 3 9 attainment 0.33 1 6 median age 0.11 0.17 1 test scores

36.

37. pairwise comparison es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational 1 3 9 attainment 0.33 1 6 median age 0.11 0.17 1 test scores 1.44 4.17 16

38. normalized comparison matrix es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational 1/1.44 3/4.17 9/16 attainment 0.33/1.44 1/4.17 6/16 median age 0.11/1.44 0.17/4.17 1/16 test scores

39. normalized comparison matrix es nt in al e ag or ta on me sc at ati n a di st uc me te ed educational 0.69 0.72 0.56 attainment 0.23 0.24 0.38 median age 0.08 0.04 0.06 test scores

40. normalized comparison matrix es nt in al e ag or ta on me sc at ati n a di st uc me te ed average educational 0.69 0.72 0.56 attainment 0.23 0.24 0.38 median age 0.08 0.04 0.06 test scores

41. normalized comparison matrix es nt in al e ag or ta on me sc at ati n a di st uc me te ed 0.67 educational 0.69 0.72 0.56 attainment 0.28 0.23 0.24 0.38 median age 0.18 0.08 0.04 0.06 test scores

42.

43. high Riparian Quality focus effort here low low high Riparian Threat Level

44. Quality Model Canopy density 0.317 Stream cover 0.200 Riparian width 0.183 Understory 0.183 Slope 0.117

45. Threat Model Encroachment 0.690 Landscape Type 0.251 Understory 0.059

46.

47.

48.

49. GIS and Multicriteria Decision Analysis By Jacek Malczewski

50. PetersonGIS Gretchen Peterson www.petersongis.com

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