1. Data Quality Data quality Related terms:
a measure of how well the GIS data represents the target domain
Related terms:
Data uncertainty
Data error
Data accuracy

2. Data Quality Micro level components Macro level components
factors that pertain to the individual data elements
Macro level components
factors that pertain to the data set as a whole

3. Data Quality Micro level components positional accuracy
attribute accuracy
logical consistency
resolution

4. Data Quality Positional (spatial) accuracy
difference between location of an object as it is described in the data and its actual location
bias: systematic error
precision: standard deviation of error

5. Data Quality ‘truth’ data High bias: a systematic error
Low bias: a random error

6. Data Quality ‘truth’ data
High precision: all errors about the same distance
Low precision: errors vary greatly in distance

7. Data Quality Attribute accuracy
are geographic objects identified correctly
invalid attribute values
missing attribute values
‘mixed-up’ attribute values

8. Data Quality Logical consistency
logically consistent relationships between geographic objects
e.g. if a lake edge forms the boundary of a state, the lake boundary line should be identical to the state boundary line

9. Data Quality
Logical inconsistency among different data, due to generalization
Water
Not PA
Land PA

10. Data Quality Resolution
raster: length of a side of a grid cell in real world units
vector: size of the smallest geographic object represented (minimum mapping unit)

11. Data Quality
Macro level components
completeness
time
lineage

12. Data Quality Completeness coverage classification verification
proportion of data available for the area of interest
classification
how well the classification is able to represent the data
verification
amount and distribution of field measurements or other independent sources of information that were used to develop the data

13. Data Quality Completeness coverage
proportion of data available for the area of interest
Area of interest
Area of data availability

14. Data Quality Completeness classification
how well the classification is able to represent the data
Grains Orchards
Agriculture
Forest
Urban
Water
Deciduous Coniferous

15. Data Quality
Time
commonly, the date of the source material used to create the data
some data do not change significantly over the time data usage (elevation data)
other data can change rapidly (demographic data and land use)

16. Data Quality
Lineage
the history of a data set: the source data and processing steps used to produce the data
each data source and processing step introduces a level of error into the final data product
lineage should be encoded in documentation detailing how the data was produced and who did it

17. Data Quality Sources of error
Error in spatial data cannot be completely eliminated, but it can be managed
trade-off between cost of creating and maintaining data and level of error

18. Data Quality Sources of error data collection data input data storage
data manipulation
data output
use of results

19. Data Quality Sources of error data collection
error in field data collection
errors in existing maps used for digital data creation

20. Data Quality Sources of error data input
inaccuracies in digitizing (operator and equipment)
discretization of geographic entities (e.g. vector digitizing of forest ‘edge’
error in attribute entry

21. Data Quality
Sources of error
data storage
numerical precision

22. Data Quality Sources of error data manipulation
error propagation in multiple overlay operations
‘sliver’ polygons

23. Data Quality
Data manipulation: sliver polygons
Water
Not PA
Land PA

24. Data Quality
Vector to Raster Conversion

25. Data Quality
Raster to Vector Conversion
Original

26. Data Quality Sources of error data output
scaling inaccuracies (printer dpi)
instability of the medium

27. Data Quality Sources of error use of results misinterpretation of data
no acknowledgement of data uncertainty

28. Data Quality Metadata data about data
data quality is described in the metadata
standards for metadata and data sharing developed by National Committee for Cartographic Data Standards (NCDC) and, currently, Federal Geographic Data Committee (FGDC)
PASDA example

29. Data Quality
Metadata
digital spatial data that is derived from USGS paper maps conform to National Map Accuracy Standards (NMAP)
in place since 1940s
as set of accuracy ‘requirements’ that all published maps conform to