1. State of the Art and Future Trends in Geoinformatics Gerhard Navratil navratil@geoinfo.tuwien.ac.at

2. 2/34 Gerhard Navratil Contents How to determine State of the Art? GIS: The Early Years Framework Changes Changes in Research Questions Future Challenges

3. 3/34 Gerhard Navratil How to Determine State of the Art? How to Determine Future Trends? Look at industry solutions? Look at publications in journals? Look at presentations in conferences? Look at the development of knowledge! Try to extrapolate!

4. 4/34 Gerhard Navratil GIS: The Early Years 1960‘s: First Steps of GIS –Computers slow –Storage media slow and expensive (tapes) –No graphical out put Nixdorf 820, 1968 (Christian Giersing )

5. 5/34 Gerhard Navratil Early Maps (Marble et al. 1984)

6. 6/34 Gerhard Navratil Early Topics Data storage Networks and topology Attribute modelling Required functionality User interface Graphical output

7. 7/34 Gerhard Navratil Example: Geometry Representation –Vector: Spaghetti, Topology (1980‘s) –Raster: Simple concept, easy to print, scanned maps Efficient storage –Databases save space (relational DB) (Codd 1969) Problems of data combination –Map algebra (Tomlin, 1990) –Line intersection problem

8. 8/34 Gerhard Navratil Example: Line Intersection It Makes Me so Cross (Douglas, 1974) –Task: General purpose FORTRAN routine to decide if two line segments intersect –5 pages of text, 21 special cases It Doesn‘t Make Me Nearly as Cross (Saalfeld, 1987) –New representation (point-vector) –determine r, r' – intersect if both in [0,1]

9. 9/34 Gerhard Navratil What Happened? Implementation led to problems First solution Improvement by different approach More elegant solution  -improvements?

10. 10/34 Gerhard Navratil Framework Changes (80‘s/90‘s) Increasing amount of computing power (from exclusive equipment to ubiquitous infrastructure) Standard graphical user interfaces  GIS on standard office PC‘s

11. 11/34 Gerhard Navratil Problem: Data Supply Main data sources: Scanned maps (outdated) Measurements (slow, expensive) Satellite images (low resolution, expensive) Aerial photography (required digitizing, expensive)  Standard Data Suppliers (e.g., Ordnance Survey)

12. 12/34 Gerhard Navratil Advantages of Standard Data Sources Well developed data capture processes  known quality Clear understanding of the limits of the data (At least some) Liability issues solved

13. 13/34 Gerhard Navratil Disadvantages of Standard Data Sources Standard products with defined quality – only limited options Dependency on a single data provider Market power of producers  Data quality discussed from producer perspective only

14. 14/34 Gerhard Navratil Software Small number of commercial GIS: ESRI Intergraph Siemens MapInfo (Erdas) Almost no independent products (mainly GRASS and Spring)

15. 15/34 Gerhard Navratil Recent Changes New communication technology (Internet, mobile phones, WLAN) Abundant data: –Volunteered Geographic Information (VGI) –Satellite images –Laser Scanning/Digital Photogrammetry Software producing communities (open source software)

16. 16/34 Gerhard Navratil New Tools/Environments GNSS: Positioning information is available high level of quality Smartphones (mobile, bi-directional access to data) Google Earth, Google Maps, Microsoft Bing

17. 17/34 Gerhard Navratil Changes in Research Questions Quality of the new data? Users are no experts  Communication with lay people Data used during execution of a process, not during planning – changes?

18. 18/34 Gerhard Navratil Research Questions on Data (1) Understanding the processes that produce the data –Quality checks? Consistency? Updates? –Data processing steps? Understanding the communities providing the data –What is the incentive? –What is the task for which the data is needed? –Knowledge level of data producers?

19. 19/34 Gerhard Navratil Research Questions on Data (2) Limitations of the data set? –Scale of the data capture? –What is the quality? Is it uniform? Connection between different data sets? –Different communities collecting similar data in the same region? –Similar communities collecting similar data in neighbouring region?

20. 20/34 Gerhard Navratil Research Questions on Users What is the information needed by the user? –Required level of quality? –Required additional information? How to best communicate the information? –Graphical or Verbal or Oral? –User-oriented or as a map? –Level of redundancy?

21. 21/34 Gerhard Navratil Example: OpenStreetMap (1) Data provided by –Communities –Organizations (e.g., Ordnance Survey) –Private persons Data collected by –GPS-tracks –Digitizing aerial images Teheran (OSM, 2011)

22. 22/34 Gerhard Navratil Example: OpenStreetMap (2) Free to use (License: Creative Commons) Usable for routing and mapping Available for large parts of the world Public Transport in Berlin (Melchior Moos, 2008)

23. 23/34 Gerhard Navratil Example: OpenStreetMap (3) User tasks –Cartography (professionals/amateurs) –Navigation (routing) Assessing the quality is difficult –Attribute accuracy in international context? –Completeness? In comparison to what? NAVTEQ/TeleAtlas- data?

24. 24/34 Gerhard Navratil Example: OpenStreetMap (4) Classification in different countries, e.g., highway = tertiary (Wikipedia) (Google Earth)

25. 25/34 Gerhard Navratil Emerging Research Fields Semantics of data Assessment of data quality for VGI User interfaces Processes and time

26. 26/34 Gerhard Navratil Semantics of Data (1) Data from different sources – what happens when we combine them? –Different communities use different classifications – land cover vs. land use? –Comparing apples and oranges? (Comber, 2007) (Wikipedia)

27. 27/34 Gerhard Navratil Semantics of Data (2) Current tool: Ontologies Research questions: Semantics of processes Vagueness Translation of terms between domains Trust in semantic quality of VGI

28. 28/34 Gerhard Navratil Assessment of Data Quality (1) Easy for result of single observation (quality of equipment) Difficult if –Data collected during extended period e.g., land management –Data collected by vast number of people e.g., VGI

29. 29/34 Gerhard Navratil Assessment of Data Quality (2) Ideas for quality assessment in land management: Geometrical quality of cadastral boundaries: Compare data set with original surveys (Navratil et al. 2010) Compare the data sets with orthophotos Result: Varying quality – how to communicate? A: deviations between a few cm and 150m

30. 30/34 Gerhard Navratil User Interfaces New impulses for interfaces from Google Earth, smartphones, etc. How to exert this? How to exploit the new hardware? e.g., smartphones, tablets 2D or 3D? When to use what? Virtual reality or mixed reality? Applications? Benefits? Realization?

31. 31/34 Gerhard Navratil Processes and Time (1) Data are not static – reality changes constantly  Data are connected to the date of collection Data describe/are influenced by processes e.g., sensor networks Consistency checks require combination of processes and data e.g., differential equations (Hofer & Frank 2009)

32. 32/34 Gerhard Navratil Processes and Time (2) Task are described by Location Duration Prerequisites Coordination of tasks requires Start and end location of tasks Duration of navigation between different locations

33. 33/34 Gerhard Navratil Conclusions (1) Finding research topics requires Understand the recent developments Detect changes in the framework Find the consequences of these changes Look for missing links

34. 34/34 Gerhard Navratil Conclusions (2) Future key research topics are Semantics of data Assessment of data quality for VGI User interfaces Processes and time