GEOG5060 GIS & Environment

1. School of Geography FACULTY OF ENVIRONMENT GEOG5060 GIS & Environment Dr Steve Carver Email: S.J.Carver@leeds.ac.uk

2. Lecture 1: Error and uncertainty Outline: terminology, types and sources of error why is it important?

3. 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. Terminology • Various (often confused terms) in use: • error • uncertainty • accuracy • precision • data quality

5. 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. 4 YO! Accuracy vs. Precision Inaccurate Accurate 1 2 Imprecise 3 4 Precise

7. Question… What does accuracy and precision mean for GIS co-ordinate systems?

8. 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. 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. Age of data Northallerton circa 1999 Northallerton circa 1867

11. Global DEM National DEM European DEM Local DEM Scale of data

12. 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. original digitised Regular shift

14. original digitised Distortion and edge-effects

15. original digitised Systematic and random errors

16. original digitised Obvious and hidden errors

17. Vector to raster conversion error • coding errors • cell size • majority class • central point • grid orientation • topological mismatch errors • cell size • grid orientation

18. Fine raster Coarse raster Effects of raster size GEOG5060 - GIS and Environment

19. Original Original raster Tilted Shifted Effects of grid orientation

20. 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. Imprecise and vague 505.9 500 500-510 238.4 240 230-240

22. Mixed up 505.9 238.4 238.4 505.9

23. Just plain wrong...! 505.9 100.3 238.4 982.3

24. 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. Cartographic generalisation 1:3M 1:10,000 1:500,000 1:25,000 City of Sapporo, Japan

26. Question… An appreciation of error and uncertainty is important because…

27. 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. Question… What error handling facilities are their in proprietary GIS packages like Arc/Info?

29. 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. Question… How can error be communicated to end users?

31. 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. Monte Carlo simulation 1. inputs characterised by error model 2. add random ‘noise’ to input 3. run GIS operations on randomised data 4. store results 5. re-run steps 2 thru 4 100 times 6. create composite results map to: • assess sensitivity of result to random noise • derive confidence limits

33. Credibility regions

34. Coping with uncertainty Epsilon model: 1. inputs characterised by error model 2. use required confidence limit to define buffer distance and buffer inputs 3. run GIS operations on buffered data 4. store results

35. Epsilon modelling Boolean AND Exclusive AND Inclusive AND Exclusive/Inclusive AND Inclusive/Exclusive AND

36. 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

37. Workshop • Handling error and uncertainty in GIS • demonstration of jacknifing and bootstrapping methods • issues of legality and liability?

38. Practical • Monte Carlo simulation • Sea level rise and coastal re-alignment/inundation • Use different resolution terrain models (OS Landform Profile and Panorama) to assess risk of coastal flooding via reclassification • Error modelling based on Monte Carlo simulation • Produce maps of 100, 95 and 80% credibility regions

39. Next week… • Grid-based modelling • linking models to GIS • basics of cartographic modelling • modelling in Arc/Info GRID • Workshop: Constructing models in GRID • Practical: Facilities location using GRID