DATA QUALITY AND ERROR

Terminology, types and sources Importance Handling error and uncertainty

DATA QUALITY

GIGO: garbage in, garbage out

Because it’s in the computer, don’t mean it’s right

Accept there will always be errors in GIS

INTRODUCTION

• GIS - great tool for spatial data analysis and display

• question: what about error?

data quality, error and uncertainty

error propagation

confidence in GIS outputs

• be careful, be aware, be upfront

TERMINOLOGY

• various (often confused terms) in use: error uncertainty accuracy precision data quality

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

Accuracy and Precision

Accuracy• extent of system-wide

bias in measurement process

Precision• level of exactness

associated with measurement

Imprecise

Precise

Inaccurate

Accurate1

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2

DATA QUALITY

• degree of excellence in data

• general term for how good the data is

• takes all other definitions into account error uncertainty precision accuracy

DATA QUALITY

• based on the following elements: positional accuracy attribute accuracy logical consistency data completeness

POSITIONAL ACCURACY

• spatial: deviance from true position (horizontal or vertical)

• general rule: be within the best possible data resolution

i.e: for scale of 1:50,000, error can be no more than 25m

• can be measured in root mean square error (RMS) - measure of the average distance between the true and estimated location

• temporal: difference from actual time and/or date

ATTRIBUTE ACCURACY

• classification and measurement accuracy

a feature is what the GIS thinks it to be

i.e. a railroad is a railroad and not a road

i.e. a soil sample agrees with the type mapped

• rated in terms of % correct

• in a database, forest types are grouped and placed within a boundary

• in reality - no solid boundary where only pine trees grow on one side and spruce on the other

ATTRIBUTE ACCURACY

LOGICAL CONSISTENCY

• presence of contradictory relationships in the database

• non-spatial

crimes recorded at place of occurrence, others at place where report taken

data for one country is for 2000, another for 2001

data uses different source or estimation technique for different years

LOGICAL CONSISTENCY

• spatial overshoots and gaps in road networks or parcel

polygons

Good logical consistency

COMPLETENESS

• reliability concept

are all instances of a feature the GIS claims to include, in fact, there?

• partially a function of the criteria for including features

when does a road become a track?

• simply put, how much data is missing?

SOURCES OF ERROR

• sources of error:

data collection and input

human processing

actual changes

data manipulation

data output

DATA COLLECTION AND INPUT

• inherent instability of phenomena itself

random variation of most phenomena (i.e. leaf size)

edges may not be sharp boundaries (i.e. forest edges)

• description of source data

data source

name, date of collection, method of collection, date of last modification, producer, reference, scale, projection

inclusion of metadata

DATA COLLECTION AND INPUT

• instrument inaccuracies:

satellite/air photo/GPS/spatial surveying e.g. resolution and/or accuracy of digitizing equipment

thinnest visible line: 0.1 - 0.2 mm

at scale of 1:20,000 - 6.5 - 12.8 feet

anything smaller, not able to capture

attribute measuring instruments

DATA COLLECTION AND INPUT

• model used to represent data

e.g. choice of datum, classification system

• data encoding and entry

e.g. keying or digitizing errors

original

digitised

DATA COLLECTION AND INPUT

Attribute uncertainty

• uncertainty regarding characteristics (descriptors, attributes, etc.) of geographical entities

• types: imprecise or vague, mixed up, plain wrong

• sources: source document, misinterpretation, database error

505.9

238.4

500

240

500-510

230-240

238.4

505.9238.4

505.9

HUMAN PROCESSING

• misinterpretation (i.e. photos), spatial and attribute

• effects of classification (nominal/ordinal/ interval)

• effects of scale change and generalization

Scale of dataScale of data

Global DEM

European DEM

National DEM

Local DEM

HUMAN PROCESSING

• generalization - simplification of reality by cartographer to meet restrictions of map scale and physical size, effective communication and message

1:500,000

1:25,000

1:10,000

City of Sapporo, Japan

• can result in: reduction, alteration, omission and simplification of map elements

ACTUAL CHANGES

• gradual natural changes: river courses, glacier recession

• catastrophic changes: fires, floods, landslides

• seasonal and daily changes: lake/sea/river levels

• man-made: urban development, new roads

• attribute change: forest growth (height), discontinued trail/roads, road surfacing

ACTUAL CHANGES

• age of data

Northallerton circa 1867

Northallerton circa 1999

DATA MANIPULATION

vector to raster conversion errors

• coding and topological mismatch errors:

cell size (majority class and central point)

Fine raster Coarse raster

DATA MANIPULATION

vector to raster conversion errors

• coding and topological mismatch errors:

grid orientation

Original Original raster

Tilted Shifted

DATA MANIPULATION

• compounding effects of processing and analysis of multiple layers

if two layers each have correctness of 90%, the accuracy of the resulting overlay is around 81%

• density of observations - TIN modeling and interpolation

• inappropriate or inadequate class intervals or inputs for models

DATA OUTPUT

• scaling accuracies

detail on scale bar and scale type

• error caused by inaccuracy of the output devices:

resolution of computer screen or printer

colour palettes: intended colours don’t match from screen to printer

DATA OUTPUT USE

• information may be incorrectly understood

• information may be inappropriately used

HANDLING ERROR

• 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

HANDLING ERROR

• Awareness knowledge of types, sources and effects

• Minimization use of best available data correct choices of data model/method

• Communication to end user!