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7/27/2019 Data Accuracy
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MOST FAILED GIS PROJECTS ARE DUE TO
POOR PLANNING AND POOR DATA
QUALITY
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Whenever you work with spatial data (or any data for that matter) youwill deal with some sort of error due to the many steps involved increating spatial data.
Spatial data is just an abstraction of what is really there. Because of
this abstraction, we can expect error due to: How we conceptualize the data in the first place
How we collect the data
How we present the data
Additionally, there are other sources of error such as: Obvious Errors
Errors in natural variation Errors in data processing
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The errors we just discussed are illustrative of the general types ofobvious errors you would encounter when using geospatialinformation. As a geospatial analyst, you will have to give thought asto how to correct those errors before proceeding with a project.
Also, as a geospatial analyst, you should always approach a project
with the obvious sources of error we just discussed firmly on youmind. Therefore, when given a task to perform, and the associateddata, the following should act as a good checklist: Is the data current?
Were the data mapped at the correct scale? Do they have the sameaccuracies?
What is the resolution of the data? Will it support the kinds of analysis we
want to perform? Do we have all the data for the project areas, or is there some data missing?
If we need other data sets, are they available, or will we have trouble gettingthem?
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Positional Accuracy
Attribute Accuracy
Logical Consistency
Resolution
Completeness
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As we previously stated, positional accuracyrelates to the coordinate values for the geographicobjects. But, even positional accuracy is dividedinto two different categories:
Absolute accuracy: refers to the actual X,Ycoordinates of a geographic object. If oneknows the correct position of the geographicobject, they can compare the differences with
the position represented in the geographicdatabase. Typically, absolute accuracy willmeasure the total different between an object,or the difference in the X coordinate and thedifference in the Y coordinate.
Relative accuracy: refers to the displacement of
two or more points on a map (in both thedistance and angle), compared to thedisplacement of those same points in the realworld.
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Even though the USGS quadrangle hasmuch less absolute accuracy than thephotogrammetrically derived map, if tozoom into an area and measure thedistance between two points, the relative
distance, and the angle would be fairlysimilar. In this case, the distance alongTower Road is only about 15 feetdifferent, and the azimuth of the road isvirtually identical.
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Connecticut
New York
New Jersey
Pennsylvania
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Representation ofdata that does notmake sense Road in the water
Contours that crossor end
Features on steep
slopes
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Generalization mayimproperly representsize and shape
Cartographic Asthetics
Entire regions may beeliminated (islands,peninsulas, etc.)
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Fragmentedcoverage of manydeveloping
countries Soils
Vegetation
Must determine
methods foruniformity
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Areal Coverage
Many data sets do not have a uniform coverageof information
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SUFFOLK COUNTY PARCELS
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NASSAU COUNTY BASEMAP
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Positional Accuracy Result of poor field work, media shrinkage and
expansion, poor vectorization (line digitizing)
Correction through rubbersheeting
Accuracy of Content Attribute errors caused by miscoding, or faulty
equipment (thermometer, pH meter)
Sources of Variation in Data: Data entry or output faults
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Digitizing Data: Once again, scale presents a problem with digitized data. On asoil map, drawn at a scale of 1:100,000, a 1 mm wide line (the thickness of a sharppencil) would actually represent 100 meters on the ground. Or, as shown in theexample below, the road edge on the USGS quadrangle is actually 4 meters wide insome spots.
Spatial Analysis: Some GIS functions such as overlay present problems suchambiguous locations, and the concept of sliver polygons. Also, converting datafrom raster to vector format will also introduce errors. Each of the examples areshown in the illustrations below.
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Errors in Digitizing a Map Source errors
Distortion Boundaries drawn on a map have a thickness
1 mm line 1.25 m wide on 1:250 map 100m wide on 1:100000 Estimates show that 10% of a 1:24000 soil map may represent the
boundary lines alone
Digital Representation Curves are approximated by many vertices
Boundaries are not absolute, but should have aconfidence interval
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example, there are two polygons. When we overlay the two of them,the resulting polygon has not only the logical intersection betweenthe two polygons, but also many small polygons that are probablydue more to the fact that the representation of the polygonboundaries are slightly different. These smaller, or silver polygons,represent spatial errors in the data.
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Boundary Problems
Definitely in
Definitely out
Possibly in
Possibly out
Ambiguous (on the digitized border line)
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The purpose of accuracy assessment is to allow apotential user to determine the map's "fitness foruse" for their application Spatial Accuracy
Thematic Accuracy
Topological Accuracy
Temporal Accuracy