data accuracy

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

data accuracy

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