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The Input Subsystem

The Input Subsystem. GEOG 370 Instructor: Christine Erlien. Building a GIS database. Data selection Quality Cost Input method Data acquisition Data transformation. GIS Data: Primary & Secondary Sources. Primary data sources Created “in house”

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The Input Subsystem

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  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 • TIGER • U.S. Geological Survey • Imagery, DEMs, DRGs, DLGs • Natural Resource Conservation Service • STATSGO (U.S. General Soil Map) • National Oceanic & Atmospheric Agency • Coastal management • Oil & chemical spills • Coral reef conservation

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

  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 tablet • On 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. On-screen digitizing w/ PC Also called “heads-up” digitizing 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. 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/ http://www.liv.ac.uk/abe/students/photoshop/images/f05_scanner.jpg

  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. Coordinate transformations Input Output From Fundamentals of Geographic Information Systems, Demers (2005)

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

  26. Reference Frameworks & Transformations • Primary processes for manipulating graphics • Translation • Scale change • Rotation 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 X & Y coordinates are multiplied by a scale factor From Fundamentals of Geographic Information Systems, Demers (2005)

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

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

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

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

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

  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 1st • 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

  46. 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 Raster vs. vector

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