Turning data into information: assessing and reporting GIS metadata integrity using integrated computing technologies

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Timothy J. Mulrooney (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: A Geographic Information System (GIS) serves as the tangible and intangible means by which spatially related phenomena can be created, analyzed and rendered. GIS metadata serves as the formal framework to catalog information about a GIS data set. Metadata is independent of the encoded spatial and attribute information. GIS metadata is a subset of electronic metadata which catalogs electronic resources such as web pages and software applications. However, GIS metadata is inherently different than electronic media because each metadata file can be applied to a spatial component that is not implicit with other forms of metadata. Using open source technologies such as R, Perl and PHP, metadata information for large GIS data sets (thousands of layers) can be gleaned quickly and more efficiently than the human element. In doing so, metrics to express the integrity of both the metadata and GIS data can be captured, displayed and compared for use in the decision making process. Supervised and unsupervised techniques allow users and computer algorithms to explore unseen trends about the GIS data not obvious to the human component. The validity of these analyses was tested using a Technology Acceptance Model (TAM). Responses from 40 GIS professionals about the results of this methodology were captured to find a relationship between this technology’s Perceived Ease of Use, Perceived Usefulness, Attitude Towards Using and the Intention to Further use this technology.

Additional Information

Language: English
Date: 2009
Data Mining, Decision Making, Geographic Information Systems, Metadata, Open Source Programming
Metadata $x Geographic information systems.
Geographic information systems $x Data processing.
Data mining.
Open source software.

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