1. Geographic Information Systems
Data Quality

2. Practical issues Legal issues Theoretical issues
1. Necessity
Practical issues
Legal issues
Theoretical issues
                

3. Necessity ..
Accuracy
- A measure of how closely data match the true value or descriptions
Precision
- A measure of how exactly data are measured and stored
              

4. Necessity ..
Error
- The deviation between the measured value and the true value of a feature
Uncertainty
- A lack of confidence in the use of data due to incomplete knowledge                

5. Inherent errors Operational errors
2. Sources of Error
Inherent errors
Operational errors

6. 2. (1) Inherent Errors
Errors contained in source data, and they cannot be eliminated
Map creation
- Projection, generalization, etc.
Map availability
- Age of map, area coverage, map scale, map
format, map accuracy, and map accessibility, etc.

7. Map Projection

8. Generalization

9. Age of Map Northallerton circa 1999 Northallerton circa 1867

10. SUFFOLK COUNTY PARCELS
Area Coverage
NASSAU COUNTY BASEMAP
SUFFOLK COUNTY PARCELS
Many data sets do not have a uniform coverage of information
Arthur Lembo, Jr. Cornell University

11. Map Scale City of Sapporo, Japan 1:3Mil 1:500,000 1:25,000 1:10,000

12. Map Format

13. Map Accuracy Standards
1:1,200 ± 3.33 feet
1:2,400 ± 6.67 feet
1:4,800 ± feet
1:10,000 ± feet
1:12,000 ± feet
1:24,000 ± feet
1:63,360 ± feet
1:100,000 ± feet

14. Map Accessibility

15. 2. (2) Operational Errors
Errors introduced during data entry and manipulation
Inherent errors may be enhanced by operational errors
Data entry
Data storage
Data manipulation

16. Operational Errors .. Data entry Data storage - Numeric precision
- Locational precision
Data manipulation
- Sampling/interpolation
- Conversion
- Overlay
Output accuracy can only be as accurate as the least accurate individual layer – “GIGO”

17. Operational Errors ..
Data entry

18. Data Entry Errors

19. Sampling/Interpolation

20. Conversion - Raster to Vector
Vector data converted to raster with 10’ grid cells
Raster data converted back to vector, using 10’ grid cells
Vector Data
of Buildings
Arthur Lembo, Jr. Cornell University

21. Overlay - Sliver
In the following example, there are two polygons. When we overlay the two of them, the resulting polygon has not only the logical intersection between the two polygons, but also many small polygons that are probably due more to the fact that the representation of the polygon boundaries are slightly different. These smaller, or sliver polygons, represent spatial errors in the data.
Arthur Lembo, Jr. Cornell University

22. 3. Data Quality Assessment
Data quality: "Fitness for use”
Data quality assessment: "Truth in labeling”
- It is data producer's responsibility to provide
detailed information about the data
- It is users' responsibility to make their judgment
of "fitness for use"

23. Metadata Data about data Federal Geographic Data Committee (FGDC)
- Content Standard for Digital Geospatial Metadata
- U.S. National Committee for Digital Cartographic
Data Standards (NCDCDS)

24. Components of Data Quality
Positional accuracy
Attribute accuracy
Logical consistence
Completeness
Lineage

25. Closeness of coordinates to the true position
Positional Accuracy
Closeness of coordinates to the true position

26. Attribute Accuracy Closeness of attribute values to their true value
Locational accuracy and attribute accuracy are closely related to each other
- correct attributes but wrong locations
- correct locations but wrong attributes

27. Logical Accuracy
How well the relationships between data elements are maintained
- geometry-attribute link consistency
- topological consistency

28. Completeness The proportion of data available for the area of interest
The level of classification for different categories of attributes

29. Attribute Completeness
- High density
- Residential Low density
Urban or build-up - Commer/indust
Agricultural - Pasture
- Row crop
- Small grains
Rangeland
Forest land - Deciduous forest
- Evergreen forest
- mixed forest
Water - Open water

30. Lineage Accuracy
Data sources and the process steps used to produce the data
- Data sources, methods of compilation, dates of data, map projection used, audit trails, etc

31. Readings
Chapter 4