GIS Data Quality

Lecture Outline

• Accuracy

• Precision• Error

– Error Sources

• Using Multiple Data Sets Together

– Completeness

– Compatibility

– Consistency

– Applicability

– Error Propagation

• Recognizing and Avoiding Error

• Metadata

Accuracy and Precision

• Accuracy: the degree to which information on a map or in a digital database matches true or accepted values

• Precision: the level of measurement and exactness of description in a GIS database

• Error: inaccuracy and imprecision of data

• Accuracy and Precision can be applied to both spatial and non-spatial data

• Spatial – often refers to scale of map from which data is derivedcan also refer to GPS data

• Non-spatial – describes level of detail in the attribute data

Accuracy and Precision

AccurateImprecise

InaccuratePrecise

InaccurateImprecise

AccuratePrecise

Sources of Inaccuracy and Imprecision

• Obvious Sources of Error

– Age of Data

– Areal Coverage

– Map Scale

– Density of Observations

– Relevance (use of “surrogate” data)

– Data Format

– Accessibility

– Cost

• Error from Natural Variation or Original Measurements

– Positional Accuracy

– Accuracy of Content

– Variation in the Data

• Processing Errors

– Numerical Errors

– Topological Errors

– Classification and Generalization

– Digitizing and Geocoding

Scale Effects on Position

Scale1:12,5001:25,0001:50,000

1:100,0001:250,0001:1,000,00

0

Horizontal AccuracyHorizontal Accuracy

9.5 m9.5 m

12.7 m12.7 m

25.4 m25.4 m

50.8 m50.8 m

126.9 m126.9 m

507.9 m507.9 m

From: US National Map Accuracy StandardsFrom: US National Map Accuracy Standards

Error Sources Associated With Digitizing

Spatial Data Error

• Location errors– Example: a schoolhouse is located 30 feet away from its

marked location on a map– A 300 meter contour line is offset 5 meters to the

northwest– A satellite image pixel is located 2.4 meters away from its

actual location on the ground

• Attribute errors– A schoolhouse is incorrectly labeled as a church– A 300 meter contour line is actually supposed to be a 310

meter contour line– A 300 meter contour line actually represents an elevation

of 302 meters– A classified satellite image pixel is labeled forest when it is

actually a field

• One data point – error/accuracy can be easily defined.

• Data sets/maps – error/accuracy must be summarized.

• How is accuracy determined and summarized?– Very accurate data must be collected (sampled) about a

subset of the full dataset/map.– This accurate sample is then compared with the original

data– A summary is created that compares these 2 datasets

(the sample with the same measurements from the original data)

Spatial Data Error

Spatial Data Error

• Locational data accuracy can be summarized with Root Mean Square Error (RMSE).– A kind of average of the distance points/pixels are

represented from their actual location on the ground.

• Locational data can also be summarized in other ways:

• For example:– For horizontal data, the USGS uses the US National

Mapping Accuracy Standards:– 90% of all measurable points are within 1/50 of an

inch for maps of spatial scale less than or equal to 1:20,000, and within 1/30 of an inch for maps of spatial scale greater than 1:20,000.

Error• Error is unbiased when the error is in

‘random’ directions– GPS data– Human error in surveying points

• Error is biased when there is systematic variation in accuracy within a geographic data set– Example: GIS tech mistypes coordinate

values when entering control points to register map to digitizing tabletall coordinate data from this map is

systematically offset (biased)• Example: the wrong datum is being used

Error when Using Multiple Data Sets

• Error Propagation – one error leads to another

– using a mis-registered point to register another layer

– additive effect

– E.g., what happens if layer digitized with a spatial bias problem is used as the spatial reference to create another, new layer?

• Error Cascading – erroneous, imprecise and inaccurate information will skew a GIS solution when information is combined selectively into new layers

– errors propagate from layer to layer repeatedly

– effect can be additive or multiplicative

Propagation & Cascading

Using Multiple Data Sets

• Four Data Quality Considerations:

– Completeness

• A complete data set will cover the study area and time period in its entirety

• No data set is 100% complete

– Compatible

• Data sets must be compatible with one another

• Scale, data capture methods, etc.

– Consistency

• There must be consistency between and within data sets

• Data development, data capture methods

– Applicability

• Data must be appropriate for your intended use

Documenting Your Data – Metadata

• Metadata - data about data– Used to document all aspects of a data set– Allows the user to determine the usefulness of data set– Organizations want to maintain their investment– To share information about available data

• Data catalogs & clearinghouses– To aid data transfer & appropriate use

• Metadata standards set by the Federal Geographic Data Committee (FGDC)http://www.fgdc.gov

• All data distributed on the web and by sanctioned data distributors should have FGDC-compliant metadata

Metadata in ArcGIS

• Visible in ArcCatalog• Contained in the .xml part of a

shapefile

Reminders

• Case study #7 will be on Friday (Oct. 5th)• Mid-term study guide will be posted online on

Friday (Oct. 5th)• Mid-term review will be on Monday (Oct. 8th)

– Come with questions

• Mid-term exam will be next Wednesday (Oct. 10)

• Lab 3 is due next Friday (Oct. 12th)– This was written incorrectly in the Lab 3 document