Academic journal article URISA Journal

Desirable Characteristics of an Online Data Commons for Spatially Referenced, Locally Generated Data from Disparate Contributors

Academic journal article URISA Journal

Desirable Characteristics of an Online Data Commons for Spatially Referenced, Locally Generated Data from Disparate Contributors

Article excerpt



A significant body of spatially referenced, locally produced, small-scale data developed for specific local purposes exists on the hard drives and backup systems of individuals, nonprofit groups, private associations, universities, private companies, and other nongovernmental organizations across the United States. Spatially referenced data, as the term is used here, is data that refers to a particular physical location. Examples might include a university botany class project that locates and catalogs all the trees more than 15 feet tall in a small town; a homeowners' association that monitors the water quality and plant growth of the lake on which members' properties are located; a land trust that records environmental easements; or a historical museum that ties its photographic images to their physical locations, among many others.

In all these cases, the data gathered by these small local originators could be of great value to others if its existence were known. At present, however, very little of this data is available from a practical perspective to other scientific researchers and potential users. It is, for all intents and purposes, completely or partially "invisible."

While much emphasis has shifted in recent years to providing geospatial services, there still is a strong need for service developers to be able to find and exploit existing geographic data that would make those services more effective and efficient. Many efforts at the national and state levels are being made to make government-generated spatially referenced data available to the public. In the United States and in other countries around the world, initiatives are under way to make geographic information more freely available to scientists and to the general public. In English-speaking countries, for example, UK Location ( uk) in the United Kingdom, the Atlas of Canada (http://atlas., and Geoscience Australia (www. provide open access to some government-generated spatially referenced data. In the United States, initiatives such as the National Map (, the National Atlas (www., and the geospatial section of (http:// serve similar functions. These U.S. sites contain a wider array of data than many other national portals because the U.S. federal government cannot hold copyright on materials it generates, and because some state governments make their state-level data visible through these gateways. Efforts also are under way to make international sharing of large datasets more viable, especially with regard to divergent approaches to data licensing and use rights (Onsrud et al. 2010). GEOSS Data Collection of Open Resources for Everyone (GEOSS Data-CORE 2014) is an example of an international initiative to support open access to geographic data gathered by governments across nine societal benefit areas (GEOSS 2014).

Similarly, disciplinary and special purpose repositories exist to capture large sets of spatially referenced data. Examples include PANGAEA ( and OneGeology (http://

Google Maps, Google Earth, Virtual Earth, and Open Street Maps provide structured environments where the user may take advantage of a data-gathering and display infrastructure to contribute data or volunteer effort to a commercial or open-data environment. In these information infrastructure environments, legal and data management issues as well as data format issues are closely controlled by the infrastructure system provider. These are not infrastructure environments for depositing or finding diverse geographic datasets, and this article does not address such environments.

We conclude that no gateway exists analogous to the Global Earth Observation System of Systems (GEOSS) that could provide more visible and efficient access to millions of spatially referenced datasets drawn from disparate locally generated sources. …

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