1. Tools for map analysis1 TOOLS FOR MAP ANALYSIS: MULTIPLE MAPS The ultimate purpose of most GIS projects is to combine spatial data from different sources, in order to: describe and analyze interactions, make predictions with models, and to provide support to decision-makers (Bonham-Carter (1996): Geographic Information systems for geoscientists)

2. Tools for map analysis2 In general a GIS model can be described as: Output map = ƒ (2 or more input maps) TYPES OF MODELS

3. Tools for map analysis3 4Boolean logic model +Binary evidence maps +multi-class maps 4Fuzzy logic method 4Bayesian method; weights of evidence modelling MODELLING APPLICATIONS 4Index overlay

4. Tools for map analysis4 BOOLEAN LOGIC MODEL 4One looks for areas that meet certain conditions 4When a condition is meet, it is indicated with a “1” 4When a condition is NOT meet, it is indicated with a “0” 4The final statement applies the boolean AND operator 4The output map shows only 2 values: + 1 (all conditions are met) + 0 (1 or more conditions are not met)

5. Tools for map analysis5 EXAMPLE OF BOOLEAN LOGIC MODEL Model for landfill site selection Conditions: low permeability unlikely river flooding within 6 km of a major road Overburden of unconsolidated surficial material more than 4 m. Not on (fractured) limestone Not on good agricultural land

6. Tools for map analysis6 EXAMPLE OF BOOLEAN LOGIC MODEL INPUT MAPS low permeability Low Medium High City limit River

7. Tools for map analysis7 EXAMPLE OF BOOLEAN LOGIC MODEL INPUT MAPS City limit River Overburden of unconsolidated surficial material more than 4 m. 1 - 2 m 2 - 3 m 3 - 4 m 4 - 5 m 5 - 6 m > 6 m

8. Tools for map analysis8 EXAMPLE OF BOOLEAN LOGIC MODEL INPUT MAPS City limit River Not on (fractured) limestone Granite Sandstone Shale Limestone Conglomerate

9. Tools for map analysis9 EXAMPLE OF BOOLEAN LOGIC MODEL INPUT MAPS unlikely river flooding City limit River Flood zone Non-flood zone

10. Tools for map analysis10 EXAMPLE OF BOOLEAN LOGIC MODEL INPUT MAPS within 6 km of a major road City limit River < 2 km 2- 4 km 4 - 6 km > 6 km

11. Tools for map analysis11 EXAMPLE OF BOOLEAN LOGIC MODEL INPUT MAPS City limit River Not on good agricultural land Good Fair Poor

12. Tools for map analysis12 EXAMPLE OF BOOLEAN LOGIC MODEL CREATING BINARY MAPS Low Medium High City limit River True False

13. Tools for map analysis13 City limit River 1 - 2 m 2 - 3 m 3 - 4 m 4 - 5 m 5 - 6 m > 6 m True False EXAMPLE OF BOOLEAN LOGIC MODEL CREATING BINARY MAPS

14. Tools for map analysis14 City limit River Granite Sandstone Shale Limestone Conglomerate True False EXAMPLE OF BOOLEAN LOGIC MODEL CREATING BINARY MAPS

15. Tools for map analysis15 EXAMPLE OF BOOLEAN LOGIC MODEL BOOLEAN AND OPERATION AND

16. Tools for map analysis16 EXAMPLE OF BOOLEAN LOGIC MODEL Suitable Unsuitable

17. Tools for map analysis17 BOOLEAN LOGIC MODEL COMMENTS 4Major advantage in its simplicity 4Disadvantage of equal importance of each of the criteria 4Clear guidelines or rules are needed +suitable for decision trees or expert systems 4Resembles overlay technique on light table 4Output only true or false (1 or 0)

18. Tools for map analysis18 INDEX OVERLAY MODEL BINARY EVIDENCE MAPS 4Binary input maps are used with only 2 classes: true or false 4Not every binary input map carries the same weight

19. Tools for map analysis19 INDEX OVERLAY MODEL BINARY EVIDENCE MAPS 4The output is normalized by the sum of the weights 4 The output value is between 0 (extremely unfavourable) and 1 (highly favourable) 4Each map is multiplied by its weight factor 4 All weighted maps are summed

20. Tools for map analysis20 EXAMPLE OF BINARY EVIDENCE MAPS CREATING BINARY MAPS Low Medium High City limit River True False

21. Tools for map analysis21 True False WEIGHT = 3 3 0 0 1 EXAMPLE OF BINARY EVIDENCE MAPS APPLICATION OF WEIGHT FACTOR

22. Tools for map analysis22 City limit River 1 - 2 m 2 - 3 m 3 - 4 m 4 - 5 m 5 - 6 m > 6 m True False EXAMPLE OF BINARY EVIDENCE MAPS CREATING BINARY MAPS

23. Tools for map analysis23 True False WEIGHT = 5 0 1 5 0 EXAMPLE OF BINARY EVIDENCE MAPS APPLICATION OF WEIGHT FACTOR

24. Tools for map analysis24 City limit River Granite Sandstone Shale Limestone Conglomerate True False EXAMPLE OF BINARY EVIDENCE MAPS CREATING BINARY MAPS

25. Tools for map analysis25 True False WEIGHT = 1 0 1 0 1 EXAMPLE OF BINARY EVIDENCE MAPS APPLICATION OF WEIGHT FACTOR

26. Tools for map analysis26 3 0 5 0 0 1 6 4 9 1 8 3 9 EXAMPLE OF BINARY EVIDENCE MAPS SUMMATION

27. Tools for map analysis27 EXAMPLE OF BINARY EVIDENCE MAPS NORMALIZATION 6 4 9 1 8 3 9 0.7 0.4 1 0.1 0.9 0.3 1 SUM OF THE WEIGHTS = 9 5 0.6

28. Tools for map analysis28 INDEX OVERLAY MODEL MULTI-CLASS MAPS 4 Multi-class input maps are used 4The classes within each input map carry different weights, called scores 4 The input maps carry also different weights 4The average weight at a certain location is determined by the score of the class at that location and the weight of the corresponding input map

29. Tools for map analysis29 INDEX OVERLAY MODEL MULTI-CLASS MAPS 4 Each map has several classes 4For each class ij the score of that class is multiplied with the weight factor of the corresponding map i 4All scores/weights of the different maps are summed 4The output is normalized by the sum of the weights

30. Tools for map analysis30 Low Medium High City limit River Scores: Low = 7 Medium = 5 High = 3 21 15 9 Weight = 3 EXAMPLE OF MULTI-CLASS MAPS APPLICATION OF SCORES AND WEIGHT FACTOR

31. Tools for map analysis31 City limit River 1 - 2 m 2 - 3 m 3 - 4 m 4 - 5 m 5 - 6 m > 6 m Scores: 1- 2 m = 2 2 - 3 m = 5 3 - 4 m = 7 4 - 5 m = 8 5 - 6 m = 9 > 6 m = 10 Weight = 5 10 25 35 40 45 50 45 EXAMPLE OF MULTI-CLASS MAPS APPLICATION OF SCORES AND WEIGHT FACTOR

32. Tools for map analysis32 City limit River Granite Sandstone Shale Limestone Conglomerate Scores: Granite = 6 Sandstone = 4 Shale = 6 Limestone = 1 Conglo. = 3 Weight = 1 6 6 4 1 3 EXAMPLE OF MULTI-CLASS MAPS APPLICATION OF SCORES AND WEIGHT FACTOR

33. Tools for map analysis33 21 + 3 + 50 = 74 21 + 1 + 10 = 32 EXAMPLE OF MULTI-CLASS MAPS SUMMATION Coloured map: permeability Black lines: geology Purple lines: overburden

34. Tools for map analysis34 74/9 = 8.2 32/9 = 3.6 EXAMPLE OF MULTI-CLASS MAPS NORMALIZATION SUM OF THE WEIGHTS = 9 Coloured map: permeability Black lines: geology Purple lines: overburden

35. Tools for map analysis35 4More flexible combination of maps than with boolean logic 4Incorporation of expert knowledge through scores and/or weights INDEX OVERLAY MODEL COMMENTS 4Output in a wider range than only 1 or 0 4Range of scores should be within the same range