2. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 1 Lecture 13 Error and uncertainty Outline – terminology, types and sources – why is it important? – handling error and uncertainty

3. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 2 Introduction GIS, great tool but what about error? – data quality, error and uncertainty? – error propagation? – confidence in GIS outputs? NCGIA Initiative I- 1 – major research initiative? – dropped because too hard? Be careful, be aware, be upfront...

4. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 3 Terminology Various (often confused terms) in use: –error –uncertainty –accuracy –precision –data quality

5. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 4 Error and uncertainty Error –wrong or mistaken –degree of inaccuracy in a calculation  e.g. 2% error Uncertainty –lack of knowledge about level of error –unreliable

6. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 5 Accuracy vs. Precision Imprecise Precise InaccurateAccurate 1 43 2 YO! 4

7. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 6 Question… What does accuracy and precision mean for GIS co-ordinate systems?

8. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 7 Quality Data quality –degree of excellence –general term for how good the data is –takes all other definitions into account  error  uncertainty  precision  accuracy

9. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 8 Types and sources of error Group 1 - obvious sources: –age of data and areal coverage –map scale and density of observations Group 2 - variation and measurement: –positional error –attribute uncertainty –generalisation Group 3 - processing errors: –numerical computing errors –faulty topological analyses –interpolation errors

10. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 9 Northallerton circa 1867 Northallerton circa 1999 Age of data

11. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 10 Scale of data Global DEM European DEM National DEM Local DEM

12. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 11 Digitiser error Manual digitising –significant source of positional error Source map error –scale related generalisation –line thickness Operator error –under/overshoot –time related boredom factor

13. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 12 Regular shift original digitised

14. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 13 Distortion and edge-effects original digitised

15. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 14 Systematic and random errors original digitised

16. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 15 Obvious and hidden errors original digitised

17. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 16 Vector to raster conversion error coding errors –cell size  majority class  central point –grid orientation topological mismatch errors –cell size –grid orientation

18. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 17 Fine rasterCoarse raster Effects of raster size

19. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 18 OriginalOriginal raster TiltedShifted Effects of grid orientation

20. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 19 Attribute uncertainty Uncertainty regarding characteristics (descriptors, attributes, etc.) of geographical entities Types: –imprecise (numeric) or vague (descriptive) –mixed up –plain wrong! Sources: –source document –misinterpretation (human error) –database error

21. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 20 Imprecise and vague 505.9 238.4 500 240 500-510 230-240

22. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 21 Mixed up 238.4 505.9 238.4 505.9

23. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 22 Just plain wrong...! 238.4 505.9 100.3 982.3

24. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 23 Generalisation Scale-related cartographic generalisation –simplification of reality by cartographer to meet restrictions of:  map scale and physical size  effective communication and message –can result in:  reduction, alteration, omission and simplification of map elements  passed on to GIS through digitising

25. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 24 1:3M 1:500,000 1:25,000 1:10,000 Cartographic generalisation City of Sapporo, Japan

26. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 25 Question… An appreciation of error and uncertainty is important because…

27. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 26 Handling error and uncertainty Must learn to cope with error and uncertainty in GIS applications –minimise risk of erroneous results –minimise risk to life/property/environment More research needed: – mathematical models – procedures for handling data error and propagation – empirical investigation of data error and effects – procedures for using output data uncertainty estimates – incorporation as standard GIS tools

28. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 27 Question… What error handling facilities are their in proprietary GIS packages like ArcGIS?

29. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 28 Basic error handling Awareness –knowledge of types, sources and effects Minimisation –use of best available data –correct choices of data model/method Communication –to end user!

30. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 29 Question… How can error be communicated to end users?

31. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 30 Quantifying error Sensitivity analyses –Jacknifing  leave-one-out analysis  repeat analysis leaving out one data layer  test for the significance of each data layer –Bootstrapping  Monte Carlo simulation  adds random noise to data layers  Simulates the effect error/uncertainty

32. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 31 Conclusions Many types and sources of error that we need to be aware of Environmental data is particularly prone because of high spatio-temporal variability Few GIS tools for handling error and uncertainty… and fewer still in proprietary packages Need to communicate potential error and uncertainty to end users

33. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 32 Practical Error in off-the-shelf datasets Task: Assess the error in land cover data Data: The following datasets are provided for the Leeds area… –Streets and buildings (1:10,000 OS LandLine data) –25m resolution land cover data (ITE LCM90)

34. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 33 Practical Steps: 1.Display OS LandLine data over ITE LCM90 data using ArcMap. You can also add the OS 1:50,000 colour raster image and set transparency = 70%. 2.From your knowledge of the area identify areas of erroneous classification 3.What might these errors be due to?

35. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 34 Learning outcomes Familiarity with error in classified satellite imagery Familiarity with ITE land cover map 1990 (LCM90) data Experience with new GRID functions

36. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 35 Useful web links The Geographer’s Craft – lecture on error –http://www.colorado.edu/geography/gcraft/notes/error/e rror_f.htmlhttp://www.colorado.edu/geography/gcraft/notes/error/e rror_f.html GIGO –http://www.geoplace.com/gw/2000/1000/1000gar.asphttp://www.geoplace.com/gw/2000/1000/1000gar.asp Disaster waiting to happen –http://www.osmose.com/utilities/articles_press_releases /data_quality/http://www.osmose.com/utilities/articles_press_releases /data_quality/

37. Week 16 GEOG2750 – Earth Observation and GIS of the Physical Environment 36 Next week… Interpolating environmental datasets –creating surfaces from points – interpolation basics – interpolation methods – common problems Practical: Interpolating surfaces from point data