1. Lecture 23: Data quality and documentation By Austin Troy ------Using GIS-- Introduction to GIS

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

3. Materials by Austin Troy © 2007 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/

4. Materials by Austin Troy © 2007 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 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

5. Materials by Austin Troy © 2007 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/

6. Materials by Austin Troy © 2007 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 Introduction to GIS 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

7. Materials by Austin Troy © 2007 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 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 Introduction to GIS

8. Materials by Austin Troy © 2007 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

9. Materials by Austin Troy © 2007 Accuracy is tied to scale Introduction to GIS

10. Materials by Austin Troy © 2007 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 Introduction to GIS

11. Materials by Austin Troy © 2007 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 Introduction to GIS See image from U. Colorado showing accuracy standards visually Hence, a point on a map represents the center of a spatial probability distribution of its possible locations probability distribution Thanks to Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder for links

12. Materials by Austin Troy © 2007 Attribute Accuracy Attribute accuracy and precision refer to quality of non-spatial, attribute data Precision for numeric data means lots of digits Example: recording income down to cents, rather than just dollars 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 Introduction to GIS

13. Materials by Austin Troy © 2007 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 Introduction to GIS

14. Materials by Austin Troy © 2007 Other measures of data quality Logical consistency Completeness Data currency/timeliness Accessibility These apply to both attribute and positional data Introduction to GIS Image source: http://oopslist.com/

15. Materials by Austin Troy © 2007 Example of Currency and Timeliness Introduction to GIS

16. Materials by Austin Troy © 2007 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 Introduction to GIS

17. Materials by Austin Troy © 2007 Error propagation and cascading Propagation: where one error leads to another 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 Cascading error can be managed to a certain extent by conducting “sensitivity analysis” Introduction to GIS Image source: http://oopslist.com/

18. Materials by Austin Troy © 2007 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 Introduction to GIS

19. Materials by Austin Troy © 2007 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 Introduction to GIS

20. Materials by Austin Troy © 2007 Attribute conflation More spatially accurate layer is referred to as the base, coordinate or target layer Layer with more accurate attribution is referred to as the reference, or non-base layer TIGER line files: good attribution, poor accuracy; USGS DLGs: opposite. Attribute conflation is frequently used by third party vendors to assign the rich attribute data of TIGER to the positionally accurate DLGs. Nodes are matched by iteratively rubber sheeting the reference layer to the base layer until matching nodes fall within certain tolerance. Then line features are matched up. Introduction to GIS

21. Materials by Austin Troy © 2007 Conflation examples Introduction to GIS Source: Stanley Dalal, GIS cafe

22. Materials by Austin Troy © 2007 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 Introduction to GIS

23. Materials by Austin Troy © 2007 Documentation and Metadata Metadata usually include sections similar to these Introduction to GIS

24. Materials by Austin Troy © 2007 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 Introduction to GIS

25. Materials by Austin Troy © 2007 Documentation and Metadata Some roles of metadata 1.Information retrieval, cataloguing, querying and searching for data electronically. 2.Describing fitness for use 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 Introduction to GIS

26. Materials by Austin Troy © 2007 Documentation and Metadata Critical components usually break down into: Dataset identification, overview Data quality Spatial reference information Data definition Administrative information Meta metadata Introduction to GIS

27. Materials by Austin Troy © 2007 Documentation and Metadata Data identification, overview and 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 Introduction to GIS

28. Materials by Austin Troy © 2007 Documentation and Metadata Data quality should address: Positional accuracy Attribute accuracy Logical consistency Completeness Lineage Processing steps Introduction to GIS

29. Materials by Austin Troy © 2007 Documentation and Metadata 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 Introduction to GIS

30. Materials by Austin Troy © 2007 Documentation and Metadata Data definition, also known as “Entity and Attribute Information,” should include: Entity types (e.g. 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 documents with those codes if they are too long and complex Introduction to GIS

31. Materials by Austin Troy © 2007 Documentation and Metadata 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 Introduction to GIS

32. Materials by Austin Troy © 2007 Documentation and Metadata 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 Introduction to GIS

33. Materials by Austin Troy © 2007 Metadata in Arc GIS 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: Introduction to GIS

34. Materials by Austin Troy © 2007 Metadata in Arc GIS 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 Introduction to GIS

35. Materials by Austin Troy © 2007 Metadata in Arc GIS 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 Introduction to GIS

36. Materials by Austin Troy © 2007 Metadata in Arc GIS Can edit, import, edit and export metadata in multiple formats allowing helping with proper sharing of data. Introduction to GIS Can also make templates to save time in repeat documentation of big data sets See NPS metadata extension for cool utilities