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Track: Database Design and Automation
Mark White
Research Planning, Inc.
1200 Park Street
Columbia, SC 29201
Telephone: 803-256-7322
Fax: 803-254-6445
E-mail: mark@researchplanning.com
Kara Hastings
Effective Management of GIS Projects Using Quality Control and Quality Assurance Standards
The management of digital data demands the use of quality control and quality assurance (QA/QC) parameters throughout the project to maintain data integrity. With the increased dissemination of digital data, this aspect of GIS management is most important. The quality of digital data can be ensured with management focused in three fundamental areas. The initial project management component is the first phase. QA/QC procedures throughout the project is the second phase, followed by a final quality assurance check prior to data delivery. Managing a project in a geographic information system can be difficult without quality assurance and quality control. Since there can be subtasks, multiple workers, and various forms of output, the margin for error can be quite large. The often poorly defined QA/QC protocol contributes to inconsistencies in data management. From conception to data delivery, a project should pass through a series of checks, which are established control measures. The range of acceptable values
sought by the control measures are quality assurance parameters. Project progress is directly proportional to the level of confidence in the data. This paper discusses quality control measures used to manage data effectively for the production of Environmental Sensitivity Index atlases. These atlases are a compilation of biologic and socioeconomic resources and shoreline characteristics for the purpose of oil spill response. With numerous data sources, complex data structure and digital deliverables, these atlases require strict quality assurance parameters in order to maintain a high level of confidence in the data.
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