Science is increasingly driven by data, and spatial data underpin the science directions laid out in the 2007 U.S. Geological Survey (USGS) Science Strategy. A robust framework of spatial data, metadata, tools, and a user community that is interactively connected to use spatial data in an efficient and flexible way—known as a spatial data infrastructure (SDI)—must be available for scientists and managers to find, use, and share spatial data both within and beyond the USGS. In the opinion of the Committee on Spatial Data Enabling USGS Strategic Science in the 21st Century, an SDI is so important for supporting the six Science Strategy directions that it could have had its own chapter in the Science Strategy report as an underpinning of the six directions. The committee hopes that this report can serve as the “missing chapter” of that important document.
The charge for this study is to describe a vision for an SDI for the USGS and create a roadmap for executing that vision starting from the current state of the SDI at the agency (see Box S.1). It is not within the scope of this study to design an SDI or to present an exhaustive list of recommended datasets for the SDI. Those activities will be the work of the USGS if it chooses to move forward with the plan outlined in this report, and some of this work is already in progress through efforts such as the USGS Council on Data Integration and other agency initiatives.
It is important to note the distinction between an SDI at the USGS and
the broader and more ambitious goal of a National Spatial Data Infrastructure1 (NSDI). The NSDI is the work of the Federal Geographic Data Committee (FGDC, 2011), and the USGS is an important contributor to this multi-partner effort. The purpose of this study was specifically to provide an SDI roadmap to support the USGS Science Strategy (USGS, 2007), therefore this report focuses on how an SDI can support science within the agency. By extension, a functional SDI at the USGS will be a key component of the NSDI to support science, analysis, and decision requirements in other federal agencies, state, local, and tribal governments, academe, and the private sector.
The USGS recently dissolved the four core disciplines of water, geology, biology, and geography and reorganized around the missions outlined in the landmark 2007 Science Strategy (USGS, 2007). The reorganization is important for SDI development because it establishes an Associate Directorship for Core Science Systems, which includes the National Geospatial Program. Because the Science Strategy outlines future science directions for the USGS, the present committee adopted the six directions in the Science Strategy—ecosystems, energy and minerals, climate and land-use change, environmental health, water, and natural hazards—as the focus of its report for optimizing an SDI. The members selected for this committee were identified to address each of the directions in the Science Strategy.
There are not likely to be any surprises in our definition of an optimal vision for an SDI at the USGS. Much has been written and debated publicly on the subject, and the agency has recently held workshops to review the concepts. A focus on vision and execution—to define a roadmap as called for in the third item of the Statement of Task (Box S.1)—is the USGS’s primary need with regard to an SDI. Although it is neither appropriate nor feasible for the committee to recommend changes in the organizational structure of the USGS, there are critical elements of successful SDI implementation that pertain to the entire organization that are appropriate to highlight, and these are described in this report.
There is no established, validated process for developing an SDI, and past efforts have produced mixed results. However, past efforts yielded lessons that can provide valuable guidance for the USGS. The committee chose to look at lessons learned in several types of organizations to gain the broadest perspective possible. The committee examined 14 entities in the following five categories: USGS analogues in other countries, multinational organizations, U.S. public and
1Executive Order 12906, published in 1994 and amended in 2003, initiated the development of a coordinated
Statement of Task
This study will examine progress made in establishing spatial data infrastructures and the challenges faced by those infrastructures, within the context of the National Spatial Data Infrastructure. The study will examine the role that the USGS can play in continuing to ensure access to high quality geospatial data and support its use in scientific analyses and decision-making through a spatial data infrastructure (SDI) construct.
The committee will undertake three main tasks:
1. Identify existing knowledge and document lessons learned during previous efforts to develop SDIs and their support of scientific endeavors;
2. Develop a vision for optimizing an SDI to organize, integrate, access, and use scientific data; and
3. Create a roadmap to guide the USGS in accomplishing the vision within the scope of the USGS Science Strategy.
private institutions, large discipline-specific organizations, and spatial data at the USGS.
Successful implementation of an SDI depends on an agency’s roadmap and strategy, organizational leadership and culture, standardization, technical competence, funding and contracting, workforce competence, and cooperation and partnerships. SDI roadmaps that were well developed and consistently reviewed and updated were the ones that were most successful. Roadmaps that were essentially well-written business plans clearly articulated the merit of an SDI and the community that it would serve. Organizational leadership and culture influence how roadmaps and strategic goals are carried out on a daily basis and probably determine the success or failure of SDI implementation. Establishing standards for the data community is critical for SDI success. Implementation was more seamless and effective in organizations that incorporated the needs of the user community to develop and improve standards, and ones that also accepted the need for data and information products to conform with consensus standards developed by domestic and international standards bodies. Standards should serve the widest range of user types possible.
Technology and tools of the underlying database structure will need to adapt constantly in anticipation of data types beyond the current set, such as multispectral data and an expansion of data layers. Funding and contracting mechanisms affect SDI development. One key factor was adequate funding—not overfunding or underfunding—for carrying out critical activities. Workforce competence is another contributing factor: successful SDI implementation requires the presence throughout the organization of well-trained and respected professionals who understand the technology. Finally, partnerships with state and federal agencies
are essential for SDI implementation and for the long-term sustainability of an SDI.
A VISION FOR OPTIMIZING A U.S. GEOLOGICAL SURVEY SPATIAL DATA INFRASTRUCTURE
The USGS has the unique role and responsibility of acquiring, preserving, and archiving geospatial data on a national scale. As envisioned, an optimal SDI at the USGS would need to include data acquisition, data standards, modern data management services, and a set of key application services essential for supporting USGS in addressing scientific questions and questions of societal impact. The SDI would also need to consider the importance of data sharing and data discovery and would need flexible methods of preserving geospatial data across extended time frames and through numerous changes and updates because the ability to document and analyze temporal changes on a national scale is of immense scientific and societal value. Thus, data acquisition, data discovery, data sharing, and data archiving form the core vision for an effective USGS SDI. The committee believes that the effort to implement an SDI can be best framed by the phrase “discover and share for the long term” and hopes that this phrase can become the mantra for spatial data handling throughout the USGS.
First, data discovery is an important task of the USGS, and it will need to ensure the discoverability of prime datasets that were acquired in each division. Once prime datasets have been identified and indexed, there is a need to make them searchable and accessible in a corporate data management system. That will require the development of new institutional policies and series of standards on metadata and data discovery and will require compliance with new policies and standards.
Second, data sharing is a critical task that requires data to be structurally and semantically interoperable so that they can be shared and integrated with other datasets in the USGS, across the nation, and with international partners. As a multidisciplinary organization, the USGS will need to be able to readily combine and synthesize data from various disciplines to contribute to its cross-domain missions. The USGS is also a major international player and will need to collaborate with international partners to address data standardization and to comply with international protocols.
Third, the USGS has the responsibility for maintaining data for the long term. The third fundamental component of a USGS SDI is an effective institutional strategy for data archiving.
Carrying out the vision for an SDI at the USGS requires synergistic partnerships with agencies and organizations that have already contributed substantially to the SDI. A judicious selection of partners will enable the USGS to leverage
its limited resources while adopting best practices and furthering interagency standardization.
Finally, the success of implementing a vision for such a large program as the USGS SDI will depend in large part to its leadership. There is a need for empowered leadership and for USGS ownership of a comprehensive and reliable national dataset. Supportive leadership will be critical for developing carefully planned, staffed, budgeted, and executed governance and policies.
A SPATIAL DATA INFRASTRUCTURE ROADMAP
A well-designed SDI program that is based on best practices and focused on the agency’s mission will have a high probability of success provided there is adequate planning and execution. To that end, the committee proposes a roadmap for SDI implementation that divides it into three broad phases: (1) preparation and planning; (2) design, development, and testing; and (3) rollout and refinement. The committee proposes some general steps in each phase to assist the USGS in carrying out its task in implementing an effective SDI.
Programmatic preparations and plans are critical in the first phase of SDI implementation. A first critical step is the appointment of key leaders and personnel for envisioning, establishing, and carrying out the vision for an effective SDI. The leadership team will need to determine and define SDI system requirements (based on the six directions in the USGS Science Strategy and with consideration of user needs in other agencies, local governments, academe, and the public), determine the organizational structure of the SDI, identify goals, establish timeframes and milestones, and develop performance metrics. Once the initial planning is complete, it will be important to announce a general outline for implementing the SDI program; communication and outreach will play a decisive role in its success.
The second phase would entail designing, developing, and testing the SDI program. Once standards are determined, the next steps are process identification and development and software development. The former identifies common and documented processes that can enable the SDI to function smoothly across the USGS, and the latter establishes tools for discovery, management, recording, archiving, and sharing of data. With standards, processes, and software in place for an SDI prototype, a training development program would be needed to allow staff to become acquainted with the prototype. The training program would be crucial for providing technical training and support and for building organizational support and buy-in at all levels. After the prototype is introduced, it would be beneficial to unveil a pilot program on a small scale within the USGS to test how well the prototype works and to identify and rectify glitches.
The third and final phase in implementing an SDI will need to include a process for rolling out the SDI throughout the USGS and a process for fine-tuning the
program. To ensure that people are properly informed and trained, an institution-wide training program will need to be in place before the SDI is unveiled, and retraining will need to be offered periodically for users to understand the system as it develops. The SDI program will need to implement follow-up metrics to determine how well it is being executed to meet its strategic goals. On the basis of findings gathered with those metrics, there will need to be a process for making adjustments to serve users and fulfill USGS priorities.
A series of organizational and technical considerations are necessary for following the roadmap. It is important that SDI implementation have high priority for USGS leadership. A designated senior SDI staff officer will need support from all levels of leadership—from senior managers to the USGS director—and would need to be given commensurate authority to develop and deploy standards. Implementation of an SDI is a major program for establishing a geospatial base for USGS professional staff and outside users, and it would need to be viewed as such by the Survey. The incentive structure for scientists may need to be modified to reward sharing of spatial data. The USGS will need to consider expanding partnerships of five kinds: strategic partnerships with agencies such as the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation; data partnerships with agencies such as the Census Bureau; standards partnerships; academic partnerships; and technology partnerships with the commercial sector.
Among the technical considerations, supporting the diverse science workflows will require the Survey to evaluate its current information-technology infrastructure to ensure that it is aligned with the USGS Science Strategy. In light of that assessment, the USGS can implement robust enterprise data management, begin the transition to using the Web as a computing platform, and ultimately implement a comprehensive, long-term knowledge-management infrastructure that supports end-to-end spatial data management, including the collection, integration, maintenance, and delivery of multidisciplinary scientific data.