Transcript lecture 10 data qual.. - Dycker@control

DATA QUALITY AND
ERROR
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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
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GIS - great tool for spatial data analysis and
display
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question: what about error?
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data quality, error and uncertainty
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error propagation
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confidence in GIS outputs
be careful, be aware, be upfront
TERMINOLOGY
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various (often confused terms) in use:
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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
Imprecise
Precision
• level of exactness
associated with
measurement
Precise
Inaccurate
Accurate
1
2
3
4
DATA QUALITY
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degree of excellence in data
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general term for how good the data is
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takes all other definitions into account
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error
uncertainty
precision
accuracy
DATA QUALITY
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based on the following elements:
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positional accuracy
attribute accuracy
logical consistency
data completeness
POSITIONAL ACCURACY
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spatial: deviance from true position (horizontal or vertical)
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general rule: be within the best possible data resolution
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i.e: for scale of 1:50,000, error can be no more than
25m
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can be measured in root mean square error (RMS) measure of the average distance between the true and
estimated location
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temporal: difference from actual
time and/or date
ATTRIBUTE ACCURACY
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classification and measurement accuracy
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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
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rated in terms of % correct
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in a database, forest types are grouped and placed
within a boundary
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in reality - no solid boundary where only pine trees grow
on one side and spruce on the other
ATTRIBUTE ACCURACY
LOGICAL CONSISTENCY
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presence of contradictory relationships in the database
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non-spatial
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crimes recorded at place of occurrence, others at
place where report taken
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data for one country is for 2000, another for 2001
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data uses different source or estimation technique for
different years
LOGICAL CONSISTENCY
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spatial
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overshoots and gaps in road networks or parcel
polygons
Good logical consistency
COMPLETENESS
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reliability concept
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partially a function of the criteria for including features
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are all instances of a feature the GIS claims to
include, in fact, there?
when does a road become a track?
simply put, how much data is missing?
SOURCES OF ERROR
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sources of error:
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data collection and input
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human processing
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actual changes
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data manipulation
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data output
DATA COLLECTION AND INPUT
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inherent instability of phenomena itself
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random variation of most phenomena (i.e. leaf size)
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edges may not be sharp boundaries (i.e. forest
edges)
description of source data
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data source
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name, date of collection, method of collection, date of
last modification, producer, reference, scale,
projection
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inclusion of metadata
DATA COLLECTION AND INPUT
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instrument inaccuracies:
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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
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attribute measuring instruments
DATA COLLECTION AND INPUT
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model used to represent data
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e.g. choice of datum, classification system
data encoding and entry
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e.g. keying or digitizing errors
original
digitised
DATA COLLECTION AND INPUT
Attribute uncertainty
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uncertainty regarding characteristics (descriptors,
attributes, etc.) of geographical entities
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types: imprecise or vague, mixed up, plain wrong
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sources: source document, misinterpretation, database
error
505.9
500
500-510
505.9
238.4
238.4
240
230-240
238.4
505.9
HUMAN PROCESSING
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misinterpretation (i.e.
photos), spatial and
attribute
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effects of classification
(nominal/ordinal/
interval)
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Global
DEM
Nation
al DEM
European
DEM
effects of scale change
and generalization
Scale of data
Local
DEM
HUMAN PROCESSING
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generalization - simplification of reality by cartographer
to meet restrictions of map scale and physical size,
effective communication and message
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can result in:
reduction, alteration,
omission and
simplification of map
elements
1:10,000
1:500,000
1:25,000
City of Sapporo,
Japan
ACTUAL CHANGES
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gradual natural changes: river courses, glacier
recession
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catastrophic changes: fires, floods, landslides
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seasonal and daily changes: lake/sea/river levels
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man-made: urban development, new roads
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attribute change: forest growth (height), discontinued
trail/roads, road surfacing
ACTUAL CHANGES
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age of data
Northallerton circa 1999
Northallerton circa 1867
DATA MANIPULATION
vector to raster conversion errors
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coding and topological mismatch errors:
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cell size (majority class and central point)
Fine raster
Coarse raster
DATA MANIPULATION
vector to raster conversion errors
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coding and topological mismatch errors:
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grid orientation
Original
Tilted
Original raster
Shifted
DATA MANIPULATION
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compounding effects of processing and analysis of
multiple layers
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if two layers each have correctness of 90%, the
accuracy of the resulting overlay is around 81%
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density of observations - TIN modeling and interpolation
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inappropriate or inadequate class intervals or inputs for
models
DATA OUTPUT
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scaling accuracies
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detail on scale bar and scale type
error caused by inaccuracy of the output devices:
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resolution of computer screen or printer
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colour palettes: intended colours don’t match from
screen to printer
DATA OUTPUT USE
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information may be incorrectly understood
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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:
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mathematical models
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procedures for handling data error and propagation
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empirical investigation of data error and effects
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procedures for using output data uncertainty estimates
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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!