ORST-2
Spatial Data Handling
for Global Change Research

Explicit Summary Statement

This proposed research challenge addresses the persisent, age-old problem of how best to subdivide the Earth into the appropriate areal cells for mapping and spatial analysis.

General Description/Justification

Indeed, a critical component of global change research is the integration of multiple data sources, often of differing spatial and temporal resolutions. The sheer volume of data alone creates unprecedented challenges for carrying out fundamental data handling operations, such as archiving, storing, and transforming to a common format and resolution. Specifically requirements include:

  • survey sampling needs consistent, preferably equal area, networking for multiple resolutions
  • storage and retrieval from Earth observing satellites need to be hierarchical, regular, and efficient
  • dynamic atmospheric modeling needs regularity, directional uniformity, and highest angular resoultion In this regard, global grids from hexagonal tessellations derived from icosahedron or octahedron hold great potential. Equally important, there are deeper issues regarding the scientific use of such massive datasets both singly and in combination.

    Developing a set of compatible global data models that together encompass the uses and users of data among the global change community is an important research topic, as data models are the foundation upon which a spatial data infrastructure is built. The following list of research questions are only meant to illustrate both the breadth and depth of research that UCGIS institutions could jointly undertake:

    Research Questions/Topics

    1. How do we integrate the object and field approaches to global change research at different spatial and temporal scales?

    2. Can we devise globally "uniform" surface tesselations with such desirable properties as equal area tiles nearly identical in shape? Can such tesselations be partitioned recursively to form a hierarchy of spatial resolutions and hierarchical data grids that are their duals? Is this a viable approach to attacking the data intercomparability problem in global change research?

    3. Can we devise models for the entire earth, treating the earth as a three dimensional spheroidal solid, including the atmosphere, thereby integrating all environmental data sources into "grand models".

    4. How do we best include temporal resolution in global data models? Can time always be treated as an attribute, or do we need to develop four-dimensional data models?

    5. In theory, there is no limit to the resolution of hexagonal grid cells. What are the implications and benefits of hexagonal grids for the collection, storage, and analysis of global data and what are the impediments to a broader acceptance of them?

    Web Bibliography

    Discrete Global Grids, a web book edited by Goodchild and Kimerling, NCGIA, Santa Barbara, CA, 2002

    EPA's Environmental Monitoring and Assessment Program (EMAP)

    Terra Cognita Lab Publications

    Last updated on May 17, 2002.

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