1. Geographic Information Systems Applications in Natural Resource Management Chapter 3 Acquiring, Creating, and Editing GIS Databases Michael G. Wing & Pete Bettinger

2. Chapter 3 Objectives Methods to acquire GIS databases, particularly via the Internet Methods to create new GIS databases Processes to edit existing GIS databases Types and sources of error potentially associated with GIS databases

3. Four general cases of GIS databases at most organizations The data necessary for project work  Don’t exist  Do exist but were created for other general uses and may not be completely suitable for your project  Do exist but were created for other specific uses and may not be completely suitable for your project  The data are in place and in good order for your project!

4. Typically, you’ll have to acquire data Hire a contractor to create or edit GIS data Use a GPS or other device to capture data Modifying an existing database Create a new GIS database  Digitizing  Using/buying data from another organization  Downloading data from the Internet

5. Internet acquisition Now a standard method of making data available Many federal and state organizations make spatial data available  The U.S. Government is the largest producer of spatial data in the world  Manual of Federal Geographic Data Products Freedom of information act allows the public some access to data created by public agencies Use these data to save time

6. Oregon Data Sources Oregon Geospatial Data Clearinghouse (OGDC)  Formerly the SSCGIS  Demonstration OSU College of Forestry Bureau of Land Management (BLM)* National Forest Offices (SNF) StreamNet* Oregon Department of Fish and Wildlife (ODFW)* Environmental Protection Agency (EPA)*

7. National Data Sources US Geological Survey (USGS)* Bureau of Land Management (BLM)* National Park Service (NPS)*

8. Acquisition processes Using an Internet browser to select and download FTP- File Transfer Protocol Transfer on hardware  Tape  External harddrive  CD or DVD  USB key  Floppy

9. Creating GIS databases We’ve covered techniques in Chapter 1  Digitizing  Remote Sensing Satellite imagery LIDAR  Laser range finder  Total station  Scanning  GPS

10. Figure 3.1. Measurement reference points for the Daniel Pickett forest to enable digitizing of additional landscape features or the creation of new GIS databases. Roads Stands Reference points (with associated X and Y coordinates)

11. Figure 3.2. A landslide drawn on a map with a regular sharpened pencil (upper left), a marker (upper right), a sharpened pencil, yet in a sloppy manner – the landslide area is not closed (lower left), a marker, yet in a sloppy manner - the landslide area is barely closed (lower right).

12. (a) (b) (c) Figure 3.3. A timber stand (a) in vector format, from the Brown Tract, scanned (b) or converted to a raster format using 25 m grid cells, then converted back to vector format (c) by connecting lines to the center of each grid cell.

13. Editing GIS databases Reasons for editing  Changing a spatial projection  Edge matching GIS databases to other databases  Generalization and transformation processes necessary to convert a GIS database to a specific format or resolution In natural resources, updates may occur annually to keep pace with timber inventory  Growth, disturbance, harvesting

14. Updating processes Can be laborious and error-prone Verification protocols can help  Assures that data variables are reasonable or meet some standard  Should be in place for spatial and attribute characteristics  Should involve multiple people

15. Figure 3.4. A generalized process for updating a forest inventory GIS database. Delineate changes to be made to inventory Inventory forester Check data for mistakes and omissions Digitize changes to spatial data, encode inventory Integrate into GIS database Information systems analysts Check data for mistakes and omissions Check data for mistakes and omissions Check data for mistakes and omissions maps, data files GIS databases maps, data files GIS databases maps, data files Verification process #1 Verification process #2 Verification process #3 Verification process #4

16. Editing attributes Attributes are the values used to describe landscape features in a GIS database  Fields, variables, columns, data, etc. Attributes may need to be updated overtime  Vegetation type, basal area, age, volume (mbf) Easy to make mistakes, particularly with major updates Verification processes can check whether values are in the appropriate range

17. Editing spatial position As the locations or shapes of spatial features change, their coordinates will need to be changed within the GIS database Editing procedures for this purpose vary widely among software products  Typically, a database is first made “editable”  The user then makes edits Points, lines, and/or polygons moved, copied, created, or deleted  The edits are saved  Often, a time-consuming procedure

18. Consistency in spatial position When updating or creating new data, inconsistencies may result as the data are incorporated into existing databases

19. Inconsistency Roads Timber stands Figure 3.6. Spatial inconsistency between a timber stand GIS database and a roads GIS database. Figure 3.5. A timber stand drawn more precisely (top) and less precisely (bottom). Note that the lines on the south and eastern portion of the figures are different.

20. Error in GIS databases Errors arise from many sources:  Editing, encoding, hardware, and others Three primary sources of error in GIS data  Systematic  Human  Random

21. Systematic errors Caused by problems in the processes and/or tools used to measure spatial locations or attribute data Sometimes called cumulative errors since they add up during data collection Sometimes called instrumental Can be removed if identified and quantified

22. Human errors Sometimes called gross errors or blunders As the name suggests, these are introduced through carelessness or other inattention Verification processes can be used to control human errors Data collection and editing protocols can also assist in limiting human errors

23. Random errors An almost unavoidable by-product of measuring and describing landscape data No matter how careful we are in data collection procedures, there will almost always be some slight variance from the true measurement Random errors are the errors that remain after systematic and human errors have been removed

24. Managing random errors We assume that random errors follow a normal (Gaussian) distribution  They cluster around a mean value or center Least squares adjustments can distribute and minimize the error among all measurements in a feature More frequently, and especially in forestry, we assume that random errors will cancel each other out  For this reason, random errors are sometimes called compensating errors

25. Types of errors in GIS databases Positional errors occur when things are in the wrong place Can result from poor registration or inaccurate coordinate input during the digitizing process Are sometimes handled with accuracy statements: “90 percent of landscape features are within 150 meters of their true position” A root mean square error (RMSE) is sometimes used to set or describe an accuracy standard  A RMSE assesses the error between a mapped point and its on-the-ground (true) equivalent

26. Digitized road segment Real-world representation #1 Real-world representation #2 Real-world representation #3 Figure 3.7. Uncertainty of the local shape of a road segment (after Schneider 2001).

27. Other types of errors Attribute errors  Incorrect values assigned to features  Can result from keyboard entry  Verification processes can help alleviate these Computational errors  Can be introduced during procedures Generalization Vector-to-raster transformations Interpolations  Results should be carefully considered to judge appropriateness of procedures