School of Geography FACULTY OF ENVIRONMENT School of Geography FACULTY OF ENVIRONMENT GEOG5060 GIS & Environment Dr Steve Carver

School of Geography FACULTY OF ENVIRONMENT Outline: terminology, types and sources of error why is it important? Lecture 1: Error and uncertainty

School of Geography FACULTY OF ENVIRONMENT 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...

School of Geography FACULTY OF ENVIRONMENT Terminology Various (often confused terms) in use: error uncertainty accuracy precision data quality

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Accuracy vs. Precision Imprecise Precise InaccurateAccurate YO! 4

School of Geography FACULTY OF ENVIRONMENT Question… What does accuracy and precision mean for GIS co-ordinate systems?

School of Geography FACULTY OF ENVIRONMENT Quality Data quality degree of excellence general term for how good the data is takes all other definitions into account error uncertainty precision accuracy

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Northallerton circa 1867 Northallerton circa 1999 Age of data

School of Geography FACULTY OF ENVIRONMENT Scale of data Global DEM European DEM National DEM Local DEM

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Regular shift original digitised

School of Geography FACULTY OF ENVIRONMENT Distortion and edge-effects original digitised

School of Geography FACULTY OF ENVIRONMENT Systematic and random errors original digitised

School of Geography FACULTY OF ENVIRONMENT Obvious and hidden errors original digitised

School of Geography FACULTY OF ENVIRONMENT Vector to raster conversion error coding errors cell size majority class central point grid orientation topological mismatch errors cell size grid orientation

School of Geography FACULTY OF ENVIRONMENT Lecture 3GEOG GIS and Environment18 Effects of raster size

School of Geography FACULTY OF ENVIRONMENT Effects of grid orientation

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Imprecise and vague

School of Geography FACULTY OF ENVIRONMENT Mixed up

School of Geography FACULTY OF ENVIRONMENT Just plain wrong...!

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Cartographic generalisation 1:3M 1:500,000 1:25,000 1:10,000 City of Sapporo, Japan

School of Geography FACULTY OF ENVIRONMENT Question… An appreciation of error and uncertainty is important because…

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Question… What error handling facilities are their in proprietary GIS packages like Arc/Info?

School of Geography FACULTY OF ENVIRONMENT 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!

School of Geography FACULTY OF ENVIRONMENT Question… How can error be communicated to end users?

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT 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 times 6. create composite results map to: assess sensitivity of result to random noise derive confidence limits

School of Geography FACULTY OF ENVIRONMENT Credibility regions

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Boolean AND Inclusive AND Exclusive AND Exclusive/Inclusive ANDInclusive/Exclusive AND Epsilon modelling

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT Workshop Handling error and uncertainty in GIS demonstration of jacknifing and bootstrapping methods issues of legality and liability?

School of Geography FACULTY OF ENVIRONMENT 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

School of Geography FACULTY OF ENVIRONMENT 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