1. Fundamentals of GIS Materials by Austin Troy © 2008 Lecture 23: Data Quality and Documentation By Austin Troy University of Vermont NR 143

2. Fundamentals of GIS Materials by Austin Troy © 2008 Data Quality Accuracy + Precision = Quality Error = fn(accuracy, precision) Cost vs. quality tradeoff

3. Fundamentals of GIS Scale? Too little detail Too much detail? Materials by Austin Troy © 2008 Image source: Esri

4. Fundamentals of GIS Materials by Austin Troy © 2008 Accuracy “the degree to which information on a map or in a digital database matches true or accepted values.” From Kenneth E. Foote and Donald J. Huebner http://www.colorado.edu/geography/gcraft/notes/error/error_f.html Reflection of how close a measurement represent the actual quantity measured and of the number and severity of errors in a dataset or map. Image source: http://oopslist.com/

5. Fundamentals of GIS Materials by Austin Troy © 2008 Precision Hence, a measurement down to a fraction of a cm is more precise than a measurement to a cm However, data with a high level of precision can still be inaccurate—this is due to errors Each application requires a different level of precision Intensity or level of preciseness, or exactitude in measurements. The more precise a measurement is, the smaller the unit which you intend to measure

6. Fundamentals of GIS Materials by Austin Troy © 2008 Random and Systematic error Error can be systematic or random Systematic error can be rectified if discovered, because its source is understood Image source: http://oopslist.com/

7. Fundamentals of GIS Materials by Austin Troy © 2008 Random and Systematic error Systematic errors affect accuracy, but are usually independent of precision; data can use highly precise methods but still be inaccurate due to systematic error Accurate and precise: no systematic, little random error inaccurate and precise: little random error but significant systematic error Accurate and imprecise: no systematic, but considerable random error inaccurate and imprecise: both types of error

8. Fundamentals of GIS Materials by Austin Troy © 2008 Measurement of Accuracy Positional accuracy is often stated as a confidence interval: e.g. 104.2 cm +/-.01 means true value lies between 104.21 and 104.19 104.19104.2104.21 104.22 104.18

9. Fundamentals of GIS Materials by Austin Troy © 2008 Measurement of Accuracy One of the key measurements of positional accuracy is root mean squared error (MSE); equals squared difference between observed and expected value for observation i divided by total number of observations, summed across each observation i This is just a standardized measure of error—how close the predicted measure is to observed

10. Fundamentals of GIS Materials by Austin Troy © 2008 Positional Accuracy Positional accuracy standards specify that acceptable positional error varies with scale Data can have high level of precision but still be positionally inaccurate Positional error is inversely related to precision and to amount of processing positional error precision positional error processing effort

11. Fundamentals of GIS Materials by Austin Troy © 2008 Accuracy is tied to scale

12. Fundamentals of GIS Materials by Austin Troy © 2008 Positional Error Standards Different agencies have different standards for positional error Example: USGS horizontal positional requirements state that 90% of all points must be within 1/30th of an inch for maps at a scale of 1:20,000 or larger, and 1/50th of an inch for maps at scales smaller than 1:20,000

13. Fundamentals of GIS Materials by Austin Troy © 2008 Positional Error Standards USGS Accuracy standards on the ground: 1:4,800 ± 13.33 feet 1:10,000 ± 27.78 feet 1:12,000 ± 33.33 feet 1:24,000 ± 40.00 feet 1:63,360 ± 105.60 feet 1:100,000 ± 166.67 feet Hence, a point on a map represents the center of a spatial probability distribution of its possible locationsprobability distribution Thanks to Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

14. Fundamentals of GIS Materials by Austin Troy © 2008 Attribute Accuracy Attribute accuracy and precision refer to quality of non-spatial, attribute data Precision for numeric data means lots of digits Quantitative measurement errors: e.g. truncation A common error is to measure a phenomenon in only one phase of a temporal cycle:  Bird counts  River flows  Average weather metrics  Soil moisture $1200.25 vs. $1200

15. Fundamentals of GIS Materials by Austin Troy © 2008 Categorical Attributes Accuracy refers to amount of misclassification of categorical data The chance for misclassification grows as number of possible classes increases; accuracy is a function of precision, or number of classes If just classifying as “land and water”, that is not very precise, and not likely to result in an error

16. Fundamentals of GIS Materials by Austin Troy © 2008 Other measures of data quality Logical consistency Completeness Data currency/timeliness Accessibility These apply to both attribute and positional data Image source: http://oopslist.com/

17. Fundamentals of GIS Materials by Austin Troy © 2008 Example of Currency and Timeliness

18. Fundamentals of GIS Shelburne Road: 1937Shelburne Road: 2003

19. Fundamentals of GIS New North End and Colchester: 2003 New North End and Colchester: Historic

20. Fundamentals of GIS Materials by Austin Troy © 2008 Some common sources of error Numerical processing (math operations, data type, rounding, etc) Geocoding (e.g. rural address matching and street interpolation) Topological errors from digitizing (overshoots, dangling nodes, slivers, etc) Automated classification steps, like unsupervised or supervised land cover classification in remote sensing, can result in processing errors

21. Fundamentals of GIS Materials by Austin Troy © 2008 Error propagation and cascading Propagation: where one error leads to another (single step) Cascading: Refers to when errors are allowed to propagate unchecked from one layer to the next and on to the final set of products or recommendations (multi-step) Cascading error can be managed to a certain extent by conducting “sensitivity analysis” Image source: http://oopslist.com/

22. Fundamentals of GIS Materials by Austin Troy © 2008 Conflation When one layer is better in one way and another is better in another and you wish to get the best of both Way of reconciling best geometric and attribute features from two layers into a new one Very commonly used for case where one layer has better attribute accuracy or completeness and another has better geometric accuracy or resolution Also used where newer layer is produced for some theme but is has lower resolution than older one

23. Fundamentals of GIS Materials by Austin Troy © 2008 Two general types of Conflation Attribute conflation: transferring attributes from an attribute rich layer to features in an attribute poor layer Feature conflation: improvement of features in one layer based on coordinates and shapes in another, often called rubber sheeting. User either transforms all features or specifies certain features to be kept fixed

24. Fundamentals of GIS Materials by Austin Troy © 2008 Conflation example Source: Stanley Dalal, GIS cafe

25. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata To avoid many of these errors, good documentation of source data is needed Metadata is data documentation, or “data about data” Ideally, the metadata describes the data according to federally recognized standards of accuracystandards of accuracy Almost all state, local and federal agencies are required to provide metadata with geodata they make

26. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata The federal geographic data committee (FGDC) is a federal entity that developed a “Content Standard for Digital Geospatial Metadata” in 1998, which is a model for all spatial data users to followFGDC Purpose is: “to provide a common set of terminology and definitions for the documentation of digital geospatial data.” All federal agencies are required to use these standards

27. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata Some roles/purposes of metadata: 1.Information retrieval, cataloguing, querying and searching for data electronically. 2.Describing fitness for use (applicability) and documenting the usability and quality of data. 3.Describing how to transfer, access or process data 4.Documenting all relevant characteristics of data needed to use it 5.Data permanence; creates institutional memory; advertises an organization’s research (generate partnerships)

28. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata Metadata usually include sections similar to these

29. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata Critical components usually break down into: 1. Dataset identification, overview 2. Data quality 3. Spatial reference information 4. Data definition 5. Administrative information (distribution) 6. Meta-metadata

30. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata 1. Data identification, overview & administrative info: General info: name and brief ID of dataset and owner organization, geographic domain, general description/summary of content, data model used to represent spatial features, intent of production, language used, reference to more detailed documents, if applicable Constraints on access and use This is usually where info on currency is found

31. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata 2. Data quality should address: Positional accuracy Attribute accuracy Logical consistency Completeness Lineage Processing steps

32. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata 3. Spatial reference should include: horizontal coordinate system (e.g. State Plane) Includes projection used, scale factors, longitude of central meridian, latitude of projection origin, distance units Geodetic model (e.g. NAD 83), ellipsoid, semi-major axis

33. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata 4. Data definition, also known as “Entity and Attribute Information,” should include: Entity types (e.g. point, polygon, raster) Information about each attribute, including label, definition, domain of values Sometimes will include a data dictionary, or description of attribute codes, while sometimes it will reference a document with those codes if they are too long and complex

34. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata 5. Data distribution info usually includes: Name, address, phone, email of contact person and organization Liability information Ordering information, including online and ordering by other media; usually includes fees

35. Fundamentals of GIS Materials by Austin Troy © 2008 Documentation and Metadata 6. Metadata reference, or meta-metadata This is data about the metadata Contains information on When metadata updated Who made it What standard was used What constraints apply to the metadata

36. Fundamentals of GIS Materials by Austin Troy © 2008 Metadata in ArcGIS Arc GIS allows you to display, import and export metadata in and to a variety of Metadata formats: It defaults to FGDC ESRI which looks like:

37. Fundamentals of GIS Materials by Austin Troy © 2008 Metadata in ArcGIS XML is the most flexible form because its tag structure allows it to be used in programming; tags can be called as variables or can be created through form interfaces; allows for compatibility across platforms and programs

38. Fundamentals of GIS Materials by Austin Troy © 2008 Metadata in ArcGIS In the past, complete metadata was only available as text; you had to create most embedded metadata tags yourself. Today many state and nationwide datasets come with complete embedded metadata including full attribute codes E.g. NEDs, NLCD, all VCGI data

39. Fundamentals of GIS Materials by Austin Troy © 2008 Metadata in ArcGIS Can edit, import, and export metadata in multiple formats allowing help with proper sharing of data. Can also make templates to save time in repeat documentation of big data sets See NPS metadata extension for cool utilities