1. Data Sources Sources, integration, quality, error, uncertainty

2. Data acquisition Land surveying (geodesy), GPS Aerial photography Satellite images Laser altimetry Digitizing of paper maps Scanning of paper maps Statistical data (e.g. from census bureaus) Surface and soil measurements

3. Land measurement, GPS Measuring devices: theodolites, distance measurers, range finders, GPS (for angles, distances and location) Reference systems: –RD-net (Rijksdriehoeksmeting) –GPS-kernnet (415 points) Field sketches Important for attributes (street names, which crops exactly, etc.) and for verification GPS: precision of up to a few centimeters –Based on 24 satellites –3D coordinates

4. Aerial photography Most important source for the Topographic Survey (TDN) Aerial photos are digitized by hand, and interpreted by the eye Precision (resolution) of ~15 cm

5. digital air photo, 15 cm resolution

6. Satellite images Measured electromagnetic radiation (reflection) Of types of surface coverage the reflected wave lengths are known approximately (for instance, vegetation reflects much infra-red) Also called: remote sensing Resolution Landsat: 30x30 m; SPOT 20x20 m or 10x10 monochromatic EROS pan: 1.8 m, IKONOS: 0.82 m, QuickBird: 0.60 m Spatial, temporal, spectral resolution

7. Landsat Thematic Mapper, 30 m resolution, Cape Breton Island

8. IKONOS, 82 cm (Singapore)

9. QuickBird, 60 cm

10. Laser altimetry (LIDAR data) For elevation data, gives 3D point cloud Precision ~20cm Correction is needed

11. Correction of laser altimetry data Filtering elevation data to remove towns

12. Digitizing of paper maps

13. Paper maps are the most important source for digital data Redraw bounding lines, add attributes With digitizer tablet or table, or heads-up digitising Line mode or stream mode After digitizing the topology must be added

14. Scanning of paper maps Convert a map by a scanner into a pixel image Automatic interpretation difficult and error- prone  checking and correction necessary Vector scanning exists too

15. Statistical data E.g. questionnaires and interviews Human-geographic of economic-geographic: number of dogs per 1000 households, income, political preference Usually collected by the CBS, Census Bureau, or by marketing bureaus Usually mapped as a choropleth map (administrative regions with shades-of-a- color-coded meaning, classified)

16. Soil measurements For non-visible data like pollution, temperature, soil type Choose sampling strategy –Ideal: random sampling –Practice: sampling in easily accessible areas –Additional samples in ‘interesting’ areas 0 0 0 0 0 5 0 0 9

18. Data source for applications Buy existing data files, provided they are available and the quality is sufficient Services to keep information up-to-date, e.g. maintain roads and road blocks in vehicle navigation systems

19. Data integration Convert data from two different sources in order to compare, and make analysis possible Same date of sources desirable Same level of aggregation desirable (highest level determines the level of comparison)

20. Integration of not aggregated and aggregated data

21. Edge matching Integration of digitized data sets based on adjacent map sheets Idea: create seamless digital data set

22. Data quality, I Precision: number of known decimals, depending on measuring device Accuracy: absense of systematic bias (No faulty fine- tuning) precise accurate both neither

23. Data quality, II Validitity: degree in which data is relevant for an application (complex geographic variables) E.g.: income for well-being; temperature for good weather Reliability: up-to-date, not old for purpose E.g.: data of last week is out-of-date for temperature, but not for land cover

24. Geometric and topological quality Absence of error Presence of consistenty

25. Sources of geometrical and topological errors Digitizing Integration Generalization Raster-vector conversion Edge-matching

26. Mismatch of boundaries of different themes

27. Digitizing errors

28. Other sources and problems Wrong attachment of geometry and attributes Missing attribute data Uncertainty at classification of satellite images Clouds in satellite images, shadows in aerial photos Unknown quality (e.g. precision) of paper maps used for digitizing: missing metadata Deforming of paper maps

29. Dealing with error/uncertainty Provide metadata (when data collected, how, what equipment) Visualize uncertainty E.g. classification of satellite images for land cover grass: 0.86 forest: 0.08 water: 0.03 … grass: 0.34 forest: 0.31 water: 0.25 … Confusion: 1 - (p max - p second )