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GIS
databases evolve constantly. From paper
maps through the digital conversion process
to data maintained in a database, GIS data
are being constantly transformed. Maintaining
the integrity and accuracy of these data
through a well-designed quality assurance
(QA) plan that integrates the data conversion
and maintenance phases is key in implementing
a successful GIS project.
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Poor
data negate the usefulness of the technology.
Sophisticated software and advanced hardware cannot
accomplish anything without specific, reliable,
accurate geographic data. GIS technology requires
clean data. To maximize the quality of GIS data,
a quality assurance plan must be integrated with
all aspects of the GIS project.
The fundamentals of quality assurance never change.
Completeness, validity, logical consistency, physical
consistency, referential integrity, and positional
accuracy are the cornerstones of the QA plan.
All well-designed QA strategies must coexist within
the processes that create and maintain the data
and must incorporate key elements from the classic
QA categories. If QA is not integrated within
the GIS project, QA procedures can themselves
become sources of error.
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Categories of Quality Assurance
Completeness means the data adhere to the design. All data must conform to a known standard for topology, table structure, precision, projection, and other data model specific requirements.
Validity measures the attribute accuracy of the database. Each attribute must have a defined domain and range. The domain is the set of all legal values for the attribute. The range is the set of values within which the data must fall.
Logical consistency measures the interaction between the values of two or more functionally related attributes. If the value of one attribute changes, the values of functionally related attributes must also change. For example, in a database in which the attribute SLOPE and the attribute LANDUSE are related, if LANDUSE value is "water," then SLOPE must be 0, as any other value for SLOPE would be illogical.
Physical consistency measures the topological correctness and geographic extent of the database. For example, the requirement that all electrical transformers in an electrical distribution database's GIS have annotation-denoting phasing placed within 15 feet of the transformer object is one that describes a physically consistent spatial requirement.
Referential integrity measures the associativity of related tables based upon their primary and foreign key relationships. Primary and foreign keys must exist and must be associated sets of data in the tables given predefined rules for each table.
Positional accuracy measures how well each spatial object's position in the database matches reality. Positional error can be introduced in many ways. Incorrect cartographic interpretation, through insufficient densification of vertices in line segments, or digital storage precision inadequacies are just a couple sources of positional inaccuracies. These errors can be random, systematic, and/or cumulative in nature. Positional accuracy must always be qualified because the map is a representation of reality.
Automated QA Visual inspection of GIS data is reinforced by automated QA methods. GIS databases can be automatically checked for adherence to database design, attribute accuracy, logical consistency, and referential integrity.
Automated QA must occur in conjunction with visual inspection. Automated quality assurance allows quick inspection of large amounts of data. It will report inconsistencies in the database that may not appear during the visual inspection process. Both random and systematic errors are detected using automated QA procedures. Once again, the feedback loop has to be short in order to correct any flawed data conversion processes.
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