2. Theme1 Generalisation and Symbolisation 《 Digital Cartography and Map Generalization 》

3. 2 Generalisation and Symbolisation The elements of generalisation The controls of generalisation Classification, simplification and exaggeration manipulations Symbolising geographical features

4. 3 Selection and generalisation Selection - to limit our concern to those classes of information that will serve the map ’ s purpose Generalisation - to fit portrayal of selected features to the map scale and to the requirements of effective communication

5. 4 Generalisation concepts Classification - order, scale and group features by their attributes Simplification - determine important characteristics of feature attributes and eliminate unwanted detail Exaggeration - enhance or emphasise important characteristics of the attributes Symbolisation - use graphic marks to encode the information for visualisation and place them into a map

6. 5 The elements of generalisation Classification Qualitative attributes Quantitative attributes Simplification Exaggeration E.g. 20m street on 1:100,000 map scale would be only 0.2mm wide Symbolisation

7. 6 Classification Classification of a point pattern. After clustering the points, the cartographer selects a position within each cluster and places a dot to “typify” the cluster. The “typical” position need not coincide with the position of any of the original data points.

8. 7 Simplification Simplification by point elimination. In the illustrated clusters of points, one original point is selected to represent each cluster of original points on the generalised map.

9. 8 Simplification (cont.) Reducing map scales leads to the consequent simplifications.

10. 9 Simplification (cont.) Reducing map scales leads to the consequent simplifications.

11. 10 Exaggeration Two representations of Great Britain and Ireland. (A) simplified to fit the scale and is suitable for a reference map intended to give the impression of detailed precision. (B) diagrammatric generalisation suitable as a base on which to display thematic data.

12. 11 The controls of generalisation Map purpose and conditions of use Map scale Quality and quantity of data Graphic limits Physical limits Physiological and psychological limits

13. 12 Classification manipulations Point feature methods Collapsing Typification Line feature typification Aggregation of areas Aggregation of volumes - e.g. Classification for choropleth maps

14. 13 Collapsing Illustrations of the collapsing process in cartographic generalisation. Each feature represented in the left diagrams has lost at least one dimension in its portrayal in the right diagram.

15. 14 Representations illustrating typification by classification of point, line and area features. Cited in Robinson, et al., 1995 Typification

16. 15 Aggregation of areas The original data area mapped at a scale of 1:X. (A) represents a smaller-scale agglomeration of the original data. (B) represents the further aggregation of areas for an even smaller-scale representation.

17. 16 Elimination Point elimination Area elimination Smoothing Filtering Simplification manipulations

18. 17 Elimination Simplification accompanied by scale reduction. Since the scale is successively reduced from (A) to (E), an increasing number of points in the outline must be eliminated.

19. 18 Elimination (cont.) Simplification applied at a constant scale. The four maps (A through D) represent increasing simplifications of the coastline and hydrography.

20. 19 Point elimination Simplification of the outline by point elimination. The points indicated on the map to the left were retained on the map to the right where they were connected with straight-line segments. All points not selected on the map to the left were eliminated in producing the map on the right.

21. 20 Line simplification process Successive stages in line simplification process: (1) The initial line. (2) Point C, having the greatest perpendicular distance to line AB in (1) is selected for retention. Lines AC and CB are drawn. (3) The elimination of points between points A and C, because no perpendicular exceeds the threshold, and retention of point D, because its perpendicular distance to line CB does exceed the threshold.

22. 21 Area elimination Simplification by feature elimination. Areas on the left map are either shown in their entirety or completely eliminated in the feature-simplified map on the right.

23. 22 Area elimination (cont.) Simplification by area elimination. Example algorithm using size and proximity to determine which features to eliminate.

24. 23 Smoothing Examples of smoothing operators applied to linear data. Line A represents the original data. Line B: a three-term moving average with unequal weights. Line C: a five-term moving average with uneven weights. Line D: a three-term equally weighted moving average.

25. 24 Surface fitting The shaded plane, with respect to the surface shown, is situated so as to minimise the sum of the squares of the deviations between points on the surface and corresponding points on the plane.

26. 25 Symbolising geographical features Point symbolisation Qualitative Quantitative Line symbolisation Qualitative Quantitative Area symbolisation Qualitative Quantitative

27. 26 Qualitative point symbolisation Nominally scaled pictorial symbols on a map promoting winter activities in a portion of the state of Wisconsin. The map legend lists 14 symbols.

28. 27 Qualitative point symbolisation (cont.) Nominally scaled symbols are used to indicate four classes of climatic stations. Left: the use of orientation of symbols. Right: the use of the visual variable, shape.

29. 28 Symbols are proportionally scaled so that areas of the symbols are in the same ratio as the population numbers they represent. Quantitative point symbolisation

30. 29 Quantitative point symbolisation (cont.) Left: symbols are range-graded to denote the population of the cities. Right: symbols are ordinally scaled. The legends are different due to the different levels or measurement.

31. 30 Quantitative point symbolisation (cont.) Three legends whose symbols are identical. The added information in the form of text puts one legend on an ordinal scale, one on a range-graded scale, and one on a ratio scale.

32. 31 Use of visual variable Symbols use the visual variable value (colour) to order the data.

33. 32 Use of visual variable (cont.) Left: total population is symbolised by size, while percentage of black inhabitants is symbolised by the value (colour). Right: Percentage of black inhabitants is symbolised by the size, while total population is symbolised by the value (colour). From Robinson, et al., 1995

34. 33 Qualitative line symbolisation Examples of lines of differing character (the visual variable shape) which are useful for the symbolisation of nominal linear data.

35. 34 Ordinal portrayal The use of line width (visual variable size) enhanced by the use of line character (visual variable shape) to denote the ordinal portrayal of civil administrative boundaries.

36. 35 Range-graded line symbols. On this map of immigrants from Europe in 1900, lines of standardised width are used to represent a specified range of numbers of immigrants. Quantitative line symbolisation

37. 36 Qualitative area symbolisation Some standardised symbols for indicating lithologic data as suggested by the International Geographical Union Commission on Applied Geomorphology.

38. 37 Qualitative area symbolisation (cont.) Portrayal of North American air masses and their source regions. Although data have quantitative characteristics, the intent of this illustration is simply to portray location of air masses. This can be accomplished by using nominal area symbolisation.

39. 38 Quantitative area symbolisation Map illustrating the range-graded classification of Florida counties. The use of the visual variable value (colour) creates a stepped surface.

40. 39 Appropriate uses of the visual variables Feature Dimensio n Level of Measurement NominalOrdinal/Interval/Ra tio QualitativeQuantitative PointHue (colour) Size shapeValue (colour) OrientationChroma (colour) LineHue (colour) Size ShapeValue (colour) OrientationChroma (colour) AreaHue (colour) Value (colour) ShapeChroma (colour) PatternSize Orientation VolumeHue (colour) Value (colour) ShapeChroma (colour) PatternSize Orientation Appropriate uses of the visual variables for symbolisation. The visual variable in italics are of secondary importance. From Robinson, et al., 1995