1. The Input Subsystem GEOG 370 Instructor: Christine Erlien

2. Building a GIS database Data selection –Quality –Cost –Input method Data acquisition Data transformation

3. GIS Data: Primary & Secondary Sources Primary data sources –Created “in house” Through your own or your team’s field data collection By transforming data from sources not yet available digitally For use by the same organization –High level quality control –Often customized for specific project/application –Costly

4. GIS Data: Primary & Secondary Sources Secondary data sources –Outside data providers Government Third party vendor –Format conversion often required

5. Government data providers U.S. Census Bureau –TIGERTIGER U.S. Geological Survey –Imagery, DEMs, DRGs, DLGs Natural Resource Conservation Service –STATSGO (U.S. General Soil Map)STATSGO National Oceanic & Atmospheric Agency –Coastal management –Oil & chemical spills –Coral reef conservation

6. Third Party Vendors ESRI TeleAtlas Map Databases TeleAtlas DeLorme Street Atlas & Topo Usa DeLorme GeoCommunity Data Bundles GeoCommunity

7. Input Devices Manual input devices –Digitizing Transforms information from analog format (e.g., paper, Mylar)  digital format for computer storage & display Vector data capture Methods –Digitizing tabletDigitizing tablet –On screen digitizing using PCOn screen digitizing using PC –GPS Vector data capture –Scanners Vector & raster data capture (depends on scanner type)

8. Digitizing w/ digitizing tablet http://www.calmit.unl.edu/geog412/Digitizing.pdf

9. Input Devices : Small format digitizer http://www.digitizerpro.com/calcomp.htm

10. Digitizing Tablet Electronically active table surface –Fine grid of wires acts as a Cartesian coordinate system –Small & large formats available http://www.calmit.unl.edu/geog412/Digitizing.pdf

11. Digitizing Tablet Puck –Connected to tablet –Records locations from map –Crosshair  feature locator –Buttons  indicate beginning/ending of lines/polygons, left/right polygons

12. Also called “heads-up” digitizing On-screen digitizing w/ PC http://www.esri.com/news/arcnews/winter0102articles/epas-clean-water.html

13. Selection & Use of Digitizers Qualities to be aware of –Repeatability –Linearity –Resolution –Skew –Stability Repeatability: Precision; expectation that location data recorded for a single location will be same –Good = 0.001 inch Linearity: Measure of digitizer’s ability to be within a specified distance (tolerance) of the correct value as the puck is move over large distance –Common tolerance level: 0.003 in over 60 in

14. Selection & Use of Digitizers Resolution: Digitizer’s ability to record increments of space –Smaller value  higher resolution For an existing digitizer: Stability: Tendency of reading to change as digitizer warms up Skew: Do the results produced have the intended shape? –Rectangular coordinates input  rectangular output –Some portions of the tablet can wear out

15. Input devices: Scanners Types: –Line-following  vector output Placed on line, moves on small wheels –Requires technician Distance/time intervals dictate coordinates recorded –Problem when line is complex Can get confused (convergence/divergence, color contrast) –Flatbed raster output –Drum scanners Automated but edits require user intervention

16. http://www.liv.ac.uk/abe/students/photoshop/images/f05_scanner.jpg Flatbed scanner & CCD Inexpensive & commonly available Use CCD (charge- coupled device) Output: raster image –Can be converted to vector CCD http://www.nortekonline.com/eng/Product/

17. Input devices: Drum scanner Scans one line at a time Drum rotates & sensor moves perpendicular to direction of rotation Can take longer maps than flatbed Output: raster image –Can be converted to vector From Fundamentals of Geographic Information Systems, Demers (2005)

18. Raster, Vector, or both? Does the project necessitate raster or vector GIS? Is the system you’ll be using capable of converting back & forth? –Most commercial programs are –Need to be aware of the decision rules associated with conversion –Might want to test

19. Conversions Vector  raster “rasterization” –Results good visually –Can be problematic for attribution Edges & raster decision rules (“last come, last coded”) Raster  vector“vectorization” –Blocky-looking –Preserves majority of attribute data

20. Vector  raster

21. http://www.yale.edu/gis/serv_r2v.htm

22. Raster  Vector

23. Reference Frameworks &Transformations Digitizing –Records Cartesian coordinates –Providing projection & zone allows later transformation back to projection –Inverse map projection: 2-D map projection coord.  Decimal Degrees (3-D)

24. From Fundamentals of Geographic Information Systems, Demers (2005) Coordinate transformations InputOutput

25. Coordinate transformations http://www.progonos.com/furuti/MapProj/Normal/CartHow/cartHow.html

26. Reference Frameworks & Transformations Primary processes for manipulating graphics –TranslationTranslation –Scale changeScale change –RotationRotation With these types of graphical manipulation  all necessary transformations

27. Translation Relocation of origin on Cartesian surface (X, Y offset values) From Fundamentals of Geographic Information Systems, Demers (2005)

28. Scale Change From Fundamentals of Geographic Information Systems, Demers (2005) X & Y coordinates are multiplied by a scale factor

29. Rotation From Fundamentals of Geographic Information Systems, Demers (2005) Angular displacement Used in projection & inverse projection processes

30. Map Preparation & Digitizing Map preparation –Have projection, zone, etc. info handy –Identify polygons to digitize & order in which they’ll be digitized –Plan how to track which sections have been digitized –Unroll map several hours in advance –Fasten map firmly Tape shouldn’t be terribly sticky  stretching Location: several inches from edge –Identify tic marks –Set tolerance level appropriate for project

31. Digitizing: Registration Registration points/tic marks –Tell software where your map area is & its coordinates –Should be outside any feature to be digitized –Should be located precisely RMSE: root mean square error –Measure of deviation between known point location & digitized location –Lower  more accurate

32. Digitizing: What to input Define project purpose –Make sure data sources address it Use most accurate maps needed for job –Not necessarily the most accurate existing Keep coverages simple & specific –Input from same map when reasonable Example: USGS topo maps

33. Digitizing: How much to input Line & polygon complexity –Record more points for complex objects than for simple lines –Simple line: 2 points (beginning & end) From Fundamentals of Geographic Information Systems, Demers (2005)

34. Digitizing: Inputs & scale Scale-dependent error: Spatial data error as f(scale of input data) –Lines & symbols take up physical space –Amount of error is related to the scale of the map Example: Same size line/symbol takes up greater amount of space on ground in small- scale map than in large-scale –Amount of error allowable needs to be taken into account in map preparation process

35. Digitizing Methods of Input: Vector Tic marks & sequence Puck keys used to indicate –Points –Lines: beginning & ending –Polygon closure Inputs may be related to software’s data structure –Examples: Nodes, topology –Note: ArcGIS builds topology on-the-fly Attribute data: keyboard entry –Make sure they’re attached to entities!

36. Digitizing Methods of Input: Raster Digitizer records vector & converts to raster Entities & attributes entered at same time Decisions: Raster cell size Whether compaction method is appropriate & which to use How grid cells will represent entities –Class codes & method for assignment Data input method: –Presence/Absence method –Centroid-of-cell method –Dominant type method –Percent occurrence method

37. Presence/absence method From Fundamentals of Geographic Information Systems, Demers (2005) Decisions made based on whether entity exists within a grid cell Easy Best method for coding points & lines

38. Centroid of cell method From Fundamentals of Geographic Information Systems, Demers (2005) Entity recorded for call only if portion occurs at center of grid cell Intense computationally Should be restricted to polygonal entities

39. Dominant type method From Fundamentals of Geographic Information Systems, Demers (2005) Entity recorded if occupies > 50% grid cell Intense computationally Can be problematic with detailed/complex maps

40. Percent occurrence method From Fundamentals of Geographic Information Systems, Demers (2005) Used only for polygonal data Each attribute  separate coverage  greater detail Intense for either computational or visual approach

41. Notes on-- Raster Data Input: Remote Sensing Image processing software as complementary to GIS –GIS not a substitute Each grid cell records electromagnetic radiation Does not need to drive choice of raster data model over vector –Choice should be based on database purpose

42. Raster Data Input: Remote Sensing Aerial photography –Source of base map data for many products  check products 1 st –Distortions caused by scale, relief, tilt Orthophotos/orthophotoquads –Type of aerial photo Corrected for scale, relief, tilt distortion Available in analog & digital formats Satellite Imagery –Requires geometric & radiometric processing Geometric processing: GCPs –Classification & accuracy assessment

43. GPS Data Input Supports development of highly accurate geodetic control Links field data collection to locations Cost & accuracy vary

44. Secondary Data Format conversion often required Datasets may be difficult to find –Result: Data reproduced  costly redundancy Data costs & sensitivity may limit access Need to be aware of vendor’s quality control procedures to be able to judge data quality What type of information included about data? –Scale, resolution, field names & descriptions, codes & meaning –Need enough info to be able to make decisions about whether data use is appropriate

45. Metadata Data about data –Content, quality, condition Component of the GIS data input process  ArcCatalog Why? –Organizations want to maintain their investment –To share information about available data Data catalogs & clearinghouses –To aid data transfer & appropriate use

47. Pulling it all together Data sources –Primary –Secondary Input Methods –Scanners –GPS –Digitizing Digitizing Process –Vector –Raster Using Data –Within & across organizations –Metadata! Raster vs. vector