The Evolution of SDI Geospatial Data Clearinghouses

The Evolution of SDI Geospatial Data Clearinghouses

Caitlin Kelly Maurie (The Pennsylvania State University, USA)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-010-3.ch124
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Geospatial data and the technologies that drive them have altered the landscape of our understanding of the world around us. The data, software and services related to geospatial information have given us the opportunity to visualize existing phenomena, to understand connections, and to address problems from environmental management to emergency response. From the everpresent Google Earth images we are shown in our televised weather reports to the 3D flyovers of war zones on the news, geospatial information is everywhere. In the decade or so since U.S. President William Clinton set the stage by announcing the establishment of the National Spatial Data Infrastructure (NSDI), the concept of the geospatial data clearinghouse has shifted dramatically to fulfill the increasing need to streamline government processes, increase collaboration, and to meet the demands of data users and data developers (Clinton, 1994). The announcement of the NSDI gave birth to a Global Spatial Data Infrastructure (GSDI) movement that would be supported by a network of SDIs or geospatial data clearinghouses from local, state, and national levels. From this point on, the evolution of the geospatial data clearinghouse has been rapid and punctuated with challenges to both the developer and the user. From the earliest incarnations of these now pervasive resources as simple FTP data transfer sites to the latest developments in Internet Map Services and real time data services, geospatial data clearinghouses have provided the backbone for the exponential growth of Geographic Information Systems (GIS). In this section, the authors will examine the background of the geospatial data clearinghouse movement, address the basic phases of clearinghouse development, and review the trends that have taken the world’s clearinghouses from FTP to Internet Map Services and beyond.
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The Spatial Data Infrastructure Movement

No discussion of SDIs and geospatial data clearinghouses would be complete without a brief introduction to the history of the movement.

The growth of geospatial data clearinghouse movement can trace its origins to the spatial data infrastructure initiatives of the 1990s when spatial data sharing began in earnest. In the United States an effort to organize spatial data and develop standards for sharing data began as the NSDI. First envisioned in 1993, the concept of the coordinated data model set forth the ideas and goals of widespread sharing of data and resources (National Research Council, 1993). By 1995, the United States had developed a plan for data sharing and established a gateway by which participants could register their metadata holdings through a centralized source (FGDC95). Sharing data through this gateway required developing metadata to an accepted standard and utilized the Z39.50 protocol—both of which will be described in the next section.

The spatial data infrastructure concept as it has evolved has, at its core, the premise that sharing data eliminates redundancy, enhances opportunities for cooperative efforts, and facilitates collaboration. In addition, the SDI movement also has two additional advantages. First, it allows a more effective and efficient interaction with geospatial data and, second, it helps to stimulate the market for the geospatial industry (Bernard, 2002). The general approach to developing an SDI is to first understand how and where geospatial data is created. Most SDIs or geospatial clearinghouses base their first level data collection efforts on framework data (FGDC95). Framework data is created by government agencies—local, state, federal, or regional for the purpose of conducting their business such as development and maintenance of roads, levying taxes, monitoring streams, or creating land use ordinances. These business practices translate themselves, in the geopspatial data world, into transportation network data, parcel or cadastral data, water quality data, aerial photographs, or interpreted satellite imagery. Other organizations can then build upon this framework data to create watershed assessments, economic development plans, or biodiversity and habitat maps. This pyramid of data sharing—from local to national—has been the cornerstone of the original concept of the SDI and considered a fundamental key to building an SDI (Rajabifard & Williamson, 2001).

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