the input subsystem geog 370 instructor: christine erlien
TRANSCRIPT
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The Input Subsystem
GEOG 370
Instructor: Christine Erlien
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Building a GIS database
Data selection– Quality– Cost – Input method
Data acquisition
Data transformation
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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
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GIS Data: Primary & Secondary Sources
Secondary data sources
– Outside data providers• Government• Third party vendor
– Format conversion often required
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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
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Third Party Vendors
ESRI
TeleAtlas Map Databases
DeLorme Street Atlas & Topo Usa
GeoCommunity Data Bundles
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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)
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Digitizing w/ digitizing tablet
http://www.calmit.unl.edu/geog412/Digitizing.pdf
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Input Devices : Small format digitizer
http://www.digitizerpro.com/calcomp.htm
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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
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Digitizing Tablet
Puck– Connected to tablet– Records locations from map– Crosshair feature locator– Buttons indicate beginning/ending of
lines/polygons, left/right polygons
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Also called “heads-up” digitizing
On-screen digitizing w/ PC
http://www.esri.com/news/arcnews/winter0102articles/epas-clean-water.html
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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
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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
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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
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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/
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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)
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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
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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
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Vector raster
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http://www.yale.edu/gis/serv_r2v.htm
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Raster Vector
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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)
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From Fundamentals of Geographic Information Systems, Demers (2005)
Coordinate transformations
Input Output
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Coordinate transformations
http://www.progonos.com/furuti/MapProj/Normal/CartHow/cartHow.html
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Reference Frameworks & Transformations Primary processes for manipulating
graphics– Translation
– Scale change
– Rotation
With these types of graphical manipulation all necessary transformations
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Translation
Relocation of origin on Cartesian surface (X, Y offset values)
From Fundamentals of Geographic Information Systems, Demers (2005)
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Scale Change
From Fundamentals of Geographic Information Systems, Demers (2005)
X & Y coordinates are multiplied by a scale factor
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Rotation
From Fundamentals of Geographic Information Systems, Demers (2005)
Angular displacement
Used in projection & inverse projection processes
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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
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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
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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
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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)
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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
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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!
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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
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Presence/absence method
From Fundamentals of Geographic Information Systems, Demers (2005)
Decisions made based on whether entity exists within a grid cellEasyBest method for coding points & lines
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Centroid of cell method
From Fundamentals of Geographic Information Systems, Demers (2005)
Entity recorded for call only if portion occurs at center of grid cellIntense computationally Should be restricted to polygonal entities
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Dominant type method
From Fundamentals of Geographic Information Systems, Demers (2005)
Entity recorded if occupies > 50% grid cellIntense computationally Can be problematic with detailed/complex maps
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Percent occurrence method
From Fundamentals of Geographic Information Systems, Demers (2005)
Used only for polygonal dataEach attribute separate coverage greater detailIntense for either computational or visual approach
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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
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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
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GPS Data Input
Supports development of highly accurate geodetic control
Links field data collection to locations
Cost & accuracy vary
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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
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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
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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