Midterm

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Midterm
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Midterm

• Multiple choice on scantron/bring 2 pencil
• Major concepts moreso than details
• Reviewing LECTURES is key ?PPT files
• background extra in Chapters 1, 3-4, 9, 20 in
  Longley et al.
• Will not include
• Web Sites of the Week (WSWs)
• Labs
• Learning Assessment/Practice Questions on class
  web site

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GIS Data CaptureGetting the Map into the
ComputerChapter 9, Longley et al.
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Overview

• Introduction
• Primary data capture
• Secondary data capture
• Data transfer
• Capturing attribute data
• Managing a data capture project
• Error and accuracy

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Data Collection

• Can be most expensive GIS activity
• Many diverse sources
• Two broad types of collection
• Data capture (direct collection)
• Data transfer
• Two broad capture methods
• Primary (direct measurement)
• Secondary (indirect derivation)

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Data Collection Techniques
Field/Raster Object/Vector
Primary Digital remote sensing images GPS measurements including VGI
Primary Digital aerial photographs Survey measurements
Secondary Scanned maps Topographic surveys
Secondary DEMs from maps Toponymy data sets from atlases
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Stages in Data Collection Projects
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Primary Data Capture

• Capture specifically for GIS use
• Raster remote sensing
• e.g., SPOT and IKONOS satellites and aerial
  photography, echosounding at sea
• Passive and active sensors
• Resolution is key consideration
• Spatial
• Spectral, Acoustic
• Temporal

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(No Transcript)
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Vector Primary Data Capture

• Surveying
• Locations of objects determines by angle and
  distance measurements from known locations
• Uses expensive field equipment and crews
• Most accurate method for large scale, small areas
• GPS
• Collection of satellites used to fix actual
  locations on Earths surface
• Differential GPS used to improve accuracy

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Total Station
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GPS Handhelds
text
geographic coordinates
photos
video
audio
Bluetooth, WiFi
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cell towers /- 500 m Google db of tower locations
Wi-Fi /- 30 m Skyhook servers and db
GPS /- 10 m iPhone uses reference network
Graphic courtesy of Wired, Feb. 2009
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Power to the PeopleVGI PPGIS

• Volunteered Geographic Information
• Wikimapia.org
• Openstreetmap.org
• Aka crowdsourcing
• Public Participation GIS
• GEO 599, Fall 2007
• Papers still online at dusk.geo.orst.edu/virtual/

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Example A Boon for International Development
Agencies
Kinshasa, Democratic Republic of Congo
Robert Soden, www.developmentseed.org
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International Development, Humanitarian Relief
Mogadishu, Somalia
Robert Soden, www.developmentseed.org
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Haiti Disaster, MapAction.org
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Citizen Sensors
UCLA Center for Embedded Networked Sensing,
http//peir.cens.ucla.edu
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Societal Issues(privacy, surveillance,
ethics)e.g., Google StreetView
Early and late May 2008
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More surveillance (electronic, video, biological,
chemical) integrated into national system
From Chris Peterson, Foresight Institute As
presented at OSCON 2008, Portland
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From Chris Peterson, Foresight Institute As
presented at OSCON 2008, Portland
Graphic Gina Miller
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Sewer monitoring has begun
The test doesnt screen people directly but
instead seeks out evidence of illicit drug abuse
in drug residues and metabolites excreted in
urine and flushed toward municipal sewage
treatment plants.
From Chris Peterson, Foresight Institute As
presented at OSCON 2008, Portland
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Secondary Geographic Data Capture

• Data collected for other purposes, then converted
  for use in GIS
• Raster conversion
• Scanning of maps, aerial photographs, documents,
  etc.
• Important scanning parameters are spatial and
  spectral (bit depth) resolution

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Scanner
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Vector Secondary Data Capture

• Collection of vector objects from maps,
  photographs, plans, etc.
• Photogrammetry the science and technology of
  making measurements from photographs, etc.
• Digitizing
• Manual (table)
• Heads-up and vectorization

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Digitizer
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GEOCODING

• spatial information ---gt digital form
• capturing the map (digitizing, scanning)
• sometimes also capturing the attributes
• mapematical calculation, e.g.,
• address matching

WSW
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The Role of Error

• Map and attribute data errors are the data
  producer's responsibility,
• GIS user must understand error.
• Accuracy and precision of map and attribute data
  in a GIS affect all other operations, especially
  when maps are compared across scales.

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Accuracy

• closeness to TRUE values
• results, computations, or estimates
• compromise on infinite complexity
• generalization of the real world
• difficult to identify a TRUE value
• e.g., accuracy of a contour
• Does not exist in real world
• Compare to other sources

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Accuracy (cont.)

• accuracy of the database accuracy of the
  products computed from database
• e.g., accuracy of a slope, aspect, or watershed
  computed from a DEM

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Positional Accuracy

• typical UTM coordinate pair might be
• Easting 579124.349 m
• Northing 5194732.247 m
• If the database was digitized from a 124,000 map
  sheet, the last four digits in each coordinate
  (units, tenths, hundredths, thousandths) would be
  questionable

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Positional Accuracy
A useful rule of thumb is that positions measured
from maps are accurate to about 0.5 mm on the
map. Multiplying this by the scale of the map
gives the corresponding distance on the ground.
 
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Testing Positional Accuracy

• Use an independent source of higher accuracy
• find a larger scale map (cartographically
  speaking)
• use GPS
• Use internal evidence
• digitized polygons that are unclosed, lines that
  overshoot or undershoot nodes, etc. are
  indications of inaccuracy
• sizes of gaps, overshoots, etc. may be a measure
  of positional accuracy

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Precision

• not the same as accuracy!
• repeatability vs. truth
• not closeness of results, but number of decimal
  places or significant digits in a measurement
• A GIS works at high precision, usually much
  higher than the accuracy of the data themselves

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Accuracy vs. Precision
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Accuracy vs. Precision
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Components of Data Quality

• positional accuracy
• attribute accuracy
• logical consistency
• completeness
• lineage