midterm. multiple choice on scantron/bring #2 pencil major concepts moreso than details reviewing...
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Midterm
MidtermMultiple choice on scantron/bring #2 pencilMajor concepts moreso than detailsReviewing 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
GIS Data Capture:Getting the Map into the Computer
Chapter 9, Longley et al.
Overview
IntroductionPrimary data captureSecondary data captureData transferCapturing attribute dataManaging a data capture projectError and accuracy
Data Collection
Can be most expensive GIS activityMany diverse sourcesTwo broad types of collection
Data capture (direct collection)Data transfer
Two broad capture methodsPrimary (direct measurement)Secondary (indirect derivation)
Data Collection Techniques Field/Raster Object/
VectorPrimary Digital remote
sensing imagesGPS measurements including VGI
Digital aerial photographs
Survey measurements
Secondary Scanned maps Topographic surveys
DEMs from maps
Toponymy data sets from atlases
Stages in Data Collection Projects
Planning
Preparation
Collection / TransferEditing / Improvement
Evaluation
Primary Data Capture
Capture specifically for GIS useRaster – remote sensing
e.g., SPOT and IKONOS satellites and aerial photography, echosounding at seaPassive and active sensors
Resolution is key considerationSpatialSpectral, AcousticTemporal
Vector Primary Data Capture
SurveyingLocations of objects determines by angle and distance measurements from known locationsUses expensive field equipment and crewsMost accurate method for large scale, small areas
GPSCollection of satellites used to fix actual locations on Earth’s surfaceDifferential GPS used to improve accuracy
Total Station
GPS “Handhelds”geographic coordinates text
photos
audiovideo
Bluetooth, WiFi
cell towers+/- 500 mGoogle db of
tower locations
Graphic courtesy of Wired, Feb. 2009
Wi-Fi+/- 30 mSkyhook
servers and dbGPS+/- 10 m
iPhone uses reference network
“Power to the People:”VGI & PPGIS“Volunteered Geographic Information”
Wikimapia.orgOpenstreetmap.orgAka “crowdsourcing”
“Public Participation GIS”GEO 599, Fall 2007Papers still online at dusk.geo.orst.edu/virtual/
Example:
A Boon for International Development Agencies
Robert Soden, www.developmentseed.org
Kinshasa, Democratic Republic of Congo
International Development, Humanitarian Relief
Robert Soden, www.developmentseed.org
Mogadishu, Somalia
Haiti Disaster, MapAction.org
UCLA Center for Embedded Networked Sensing, http://peir.cens.ucla.edu
“Citizen Sensors”
Societal Issues(privacy, surveillance, ethics)
e.g., Google StreetView
Google Maps Mania Blog
Early and late May 2008
More surveillance(electronic, video, biological,
chemical)integrated into national system
From Chris Peterson, Foresight Institute
As presented at OSCON 2008, Portland
Graphic: Gina MillerFrom Chris Peterson, Foresight InstituteAs presented at OSCON 2008, Portland
Sewer monitoring has begun
“The test doesn’t 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 InstituteAs presented at OSCON 2008, Portland
Secondary Geographic Data Capture
Data collected for other purposes, then converted for use in GISRaster conversion
Scanning of maps, aerial photographs, documents, etc.Important scanning parameters are spatial and spectral (bit depth) resolution
Scanner
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
Digitizer
GEOCODING
spatial information ---> digital formcapturing the map (digitizing, scanning)sometimes also capturing the attributes“mapematical” calculation, e.g.,
address matchingWSW
The Role of ErrorMap 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.
Accuracycloseness to TRUE values
results, computations, or estimates
compromise on “infinite complexity”
generalization of the real worlddifficult to identify a TRUE value
e.g., accuracy of a contourDoes not exist in real worldCompare to other sources
Accuracy (cont.)accuracy of the database = accuracy of the products computed from databasee.g., accuracy of a slope, aspect, or watershed computed from a DEM
Positional Accuracytypical UTM coordinate pair might be:Easting 579124.349 mNorthing 5194732.247 mIf the database was digitized from a 1:24,000 map sheet, the last four digits in each coordinate (units, tenths, hundredths, thousandths) would be questionable
Map scale Ground distance corresponding to 0.5 mm map distance
1:1250 62.5 cm
1:2500 1.25 m
1:5000 2.5 m
1:10,000 5 m
1:24,000 12 m
1:50,000 25 m
1:100,000 50 m
1:250,000 125 m
1:1,000,000 500 m
1:10,000,000 5 km
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.
Positional Accuracy
Testing Positional AccuracyUse 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 inaccuracysizes of gaps, overshoots, etc. may be a measure of positional accuracy
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
Precision
Accuracy vs. PrecisionAccuracy vs. Precision
High AccuracyLow Precision
Low AccuracyHigh Precision
Many darts in reproduceable clusters, but not in the bullseye.
Darts are near the bullseye (the "true value"), but there aren't very many clusters of them (not reproduceable).
Accuracy vs. PrecisionAccuracy vs. Precision
High AccuracyLow Precision
Low AccuracyHigh Precision
Many darts in reproduceable clusters, but not in the bullseye.
Darts are near the bullseye (the "true value"), but there aren't very many clusters of them (not reproduceable).
Components of Data Quality
positional accuracyattribute accuracylogical consistencycompletenesslineage
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