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, laser range finders, GPS (for angles, distances and location), Dutch reference systems: –RD-net (Rijksdriehoeksmeting) –GPS-kernnet (415 points) Field sketches Important for attributes (street names, which crops exactly, etc.) and for verification

4. GPS GPS: precision of up to a few meters (a few centimeters for differential GPS) –Based on 30 satellites –3D coordinates Can also be used for tracking objects: cars, animals, criminals  gives trajectory data

5. 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

6. digital air photo, 15 cm resolution

7. 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, GeoEye: 0.41 m Spatial, temporal, spectral resolution E.g.: RapidEye has 5 equivalent 5 m spatial resolution satellites that cover every point on earth daily

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

9. IKONOS, 82 cm (Singapore)

10. QuickBird, 60 cm

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

12. Correction of laser altimetry data Filtering elevation data to remove towns, trees, cars

13. Digitizing of paper maps

14. Redraw bounding lines, add attributes (same as for aerial photography) With digitizer tablet or table, or heads-up digitising Line mode or stream mode After digitizing the topology must be added, and attributes must be added

15. 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

16. Statistical data Surveys, by questionnaires or interviews Human-geographic of economic-geographic: number of dogs per 1000 households, income, political preference Usually collected by the CBS (NL), Census Bureau (USA), or by marketing bureaus Usually a sample (subset) of the population is interviewed Results are often mapped as a choropleth map (administrative regions with shades-of-a- color-coded meaning, classified)

17. Soil measurements For non-visible data like pollution, temperature, soil type Choose sampling strategy –Ideal: random sampling –Practice: sampling in easily accessible areas (cannot take a soil sample under a building) –Additional samples in ‘interesting’ areas 0 0 0 0 0 5 0 0 9

19. Sensors RFID tags: Radio frequency identification: for tracking objects; can give trajectories Wireless sensor networks –Sensors that have limited computing power and can communicate –Energy consumption problem Smartdust: hypothetical wireless sensor network system

20. Using existing data sets Data collection is expensive: if possible, buy existing data sets (provided they are available and the quality is sufficient  need meta- data)

21. 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)

22. Integration of not aggregated and aggregated data population density life expectancy

23. Data integration and data consistency

24. 105 m111 m

25. Edge matching Integration of digitized data sets based on adjacent map sheets or aerial photos Idea: create seamless digital data set

26. 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

27. 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

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

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

32. Mismatch of boundaries of different themes

33. Digitizing errors

34. 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

35. Dealing with error/uncertainty Errors in data have consequences for e.g. the cost of projects –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 )

36. Dealing with error/uncertainty Errors in data have consequences for e.g. the cost of projects –Provide metadata (when data collected, how, what equipment) –Visualize uncertainty –Provide bounds on the range of outcomes (cost) of an analysis, based on an uncertainty model and Monte-Carlo simulation