1. Geographical Data Types, relations, measures, classifications, dimension, aggregation

2. To be seen on maps Topographic map urban grass water dike text (name, elevation) Classified isoline map

3. To be seen on maps Choropleth map: Map with administrative boundaries which shows per region a value by a color or shade Use of pesticide 1_3_D per county

4. More choropleth maps Africa, GDP

5. Maps show... Relation of place (geographic location) to a value (here 780 mm precipitation) or name (here is Minnesota). An abstraction (model, simplification) of reality A combination of themes (different sorts of data) Connections (subway maps) Tokyo subway map

6. Scales of measurement Nominal scale Ordinal scale Interval scale Ratio scale ( Angle/direction, vector, … ) Classification of types of data by statistical properties (Stevens, 1946)

7. Nominal scale Administrative map (names of the countries) Landuse map (names of landuse: urban, grass, forest, water, …) Geological map (names of soil types: sand, clay, rock, …) Finite number of classes, each with a name. Testing is possible for equivalence of name.

8. Ordinal scale School type (VMBO, HAVO, VWO) Wind force on schale of Beaufort (0=no wind,... 6=heavy wind, …, 9=storm,...) Questionnaire-answers (disagree, partly disagree, neutral, partly agree, agree) Finite number of classes, each with a name Testing for equivalence of name and for order

9. Interval scale Temperature in degrees Celsius or Fahrenheit Time/year on Christian calendar Unbounded number of classes, each with a value Testing for equivalence, for order and for difference (a unit distance exists)

10. Ratio scale Measurements: concentration of lead in soil Counts: population, number of airports Percentages: unemployment percentage, percent of landuse type forest Unbounded number of classes, each with a value Testing for equivalence, for order, for difference and for ratio (a natural zero exists)

11. Examples

12. Overview two data collection

13. Other scales Angle (wind direction, direction of spreading) Vector: angle and value (primary wind direction and speed) Categorical scales with partial membership (fuzzy sets; points on indeterminate boundary between “plains” and “mountains”; location of coast line: tide)

14. Example

15. Classification schemes Data on nominal scale: hierarchical classification schemes nature water working houses flats cattle plants fruit urban agriculture living landuse

17. Classification schemes Data on interval and ratio scales Fixed intervals Fixed intervals based on spread Quantiles: equal representatives “Natural” boundaries [1-10], [11-20], [21-30] [4-11], [12-19], [20-27] 4, 5, 5, 8, 12, 14, 17, 23, 27 [4-5], [8-14], [17-27] [4-5], [8-17], [23-27]

18. Classification schemes, cont’d Statistical boundaries: average , standard deviation , then e.g. boundaries  - 2 ,  - , ,  + ,  + 2  Arbitrary

19. Two classifications Four equal intervalsQuartiles Counties of Arizona, total population

20. Why is choice of classification important? Visualization often needs classification Choice of class intervals influences interpretation Think of a report that addresses air pollution due to a factory made by the board of the factory or by an environmental organization

21. Data: object and field view Object view: discrete objects in the real world –road –telephone pole –lake Field view: geographic variable has a “value” at every location in the real world –elevation –temperature –soil type –land cover

22. Reference system Data according to the scales of measurement are attribute values in a reference system A geographical reference system is spatial, temporal or both At 12 noon of August 26, 1999, a temperature of 17.6 degrees Celsius is measured at 5 degrees longitude and 53 degrees latitude

23. Spatial objects Points; 0-dimensional, e.g. measurement point (Polygonal) line; 1-dimensional, e.g. border between Bolivia and Peru Polygons; 2-dimensional, e.g. Switzerland Sets of points, e.g. locations of accidents Systems of lines (trees, graphs), e.g. street network Sets of polygons, subdivisions, e.g. island group, provinces of Nederland

24. Dependency of dimension Dimension of an object can be scale dependent: Rhine river at scale 1 on 25.000 is 2-dim.; Rhine at scale 1 on 1.000.000 is 1-dim. Dimension of an object can be application dependent: Rhine as transport route is 1-dim. (length is relevant; not the surface area); Rhine as land cover in Nederland is 2-dim.

25. The third dimension Elevation can be considered an attribute on the ratio (!?) scale at (x,y)-coordinates For civil engineering: crossing of street and railroad can be at the same level, or one above the other Data on subsurface layers and their thickness

26. Point clouds 3D point clouds are acquired using LIDAR (light detection and ranging) Airborne or ground-based

27. The time component Same region, same themes, different dates: Allows computation of change Trajectories give the locations at certain times for moving objects

28. Level of aggregation Income of an individual Average income in a municipality Average income in a province Average income in a country Higher level of aggregation

29. Various aggregations in the Netherlands Provinces (12) Municipalities (441) COROP regions (40) Water districts (39) Economic-geographic regions (129) 4, 5, or 6 digit postal codes Macro-regions (4 of 5; provinces joined) Labor exchange district (127), planning region (43), nodal region (80),...

30. Aggregation: dangers MAUP: modifiable areal unit problem 0 - 1 2 - 4 5 - Located occurrences of a rare disease clustering?

31. Aggregation: dangers MAUP: modifiable areal unit problem 0 - 1 2 - 4 5 - Aggregation boundaries have got nothing to do with mapped theme Located occurrences of a rare disease clustering?

32. Aggregation: dangers Not enough aggregation: privacy violations (e.g. AIDS-cases with complete postal code) Correction for population spread is necessary in case of data on people 0 - 1 2 - 4 5 - Located occurrences of a rare disease clustering?

33. Huntington’s disease, 1800-1900

34. Summary Geographic data is geometry, attribute, and time Data is coded in a reference system Attribute data is usually on one of the standard scales of measurement Classification of interval and ratio data is needed for mapping (isoline or choropleth) and histograms The object view and field view exist Geographic data has a dimension (point, line, area), but this may depend on scale and application Data is often spatially aggregated