Assessing the Robustness of Web Feature Services Necessary to Satisfy the Requirements of Coastal Management Applications

Master of Science, Geography, Oregon State University, Spring, 2012
Geographic Information Science Certificate

Graduate committee: D. Wright, J. Jones, D. Hart (external from U. of Wisconsin Sea Grant Institute)

Jennifer Bauer
College of Earth, Ocean, and Atmospheric Sciences, Oregon State Univ
Corvallis, OR 97331-5503
jrbauer1204-at-gmail.com

Abstract.
Ever expanding pressures on the health and productivity of our oceans and coasts from threats such as coastal development and climate change are stressing the need to consider the full spectrum of factors, scales, datasets, opinions, and trade-offs for current and future coastal management actions (Guerry 2009; McLeod and Leslie 2009; Rosenberg and Sandifer 2009). Web-based GIS tools including coastal web atlases (CWAs) and geospatial web services are being rapidly developed to assist managers, decision-makers, and scientists with the creation, implementation, and evaluation of coastal management options. Numerous CWAs are incorporating web feature service (WFS) standards into their websites to provide critical datasets on-the-fly for use in spatial decision support tools to assist with management and policy decisions. However, with this increased use of WFSs in CWAs it’s critical to understand how the various components used to create robust WFSs can affect its performance and ability to successfully execute complex spatial queries that are utilized to assess management options. A subset of county land parcel data from Wisconsin was utilized to assess how various software, hardware, and data characteristics can affect WFSs overall robustness, and how these components impact its ability to execute accurate, timely complex spatial queries consistently and their ability to meet the demands of managers, decision-makers, and scientists. Results suggest that WFSs, with varying levels of robustness, can successfully perform accurate, reliable spatial queries on datasets to extract relevant information pertaining to coastal management concerns, which could impact CWAs by increasing their functionality and range of potential applications provided to users.

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