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A theoretical approach to the use of cyberinfrastructure in geographical analysis

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This paper presents a theoretical approach that has been developed to capture the computational intensity and computing resource requirements of geographical data and analysis methods. These requirements are then transformed into a common framework, a grid-based representation of a spatial computational domain, which supports the efficient use of emerging cyberinfrastructure environments. Two key types of transformational functions (data-centric and operation-centric) are identified and their relationships are explained. The application of the approach is illustrated using two geographical analysis methods: inverse distance weighted interpolation and the [image omitted]   spatial statistic. We describe the underpinnings of these two methods, present their conventional sequential algorithms, and then address their latent parallelism based on a spatial computational domain representation. Through the application of this theoretical approach, the development of domain decomposition methods is decoupled from specific high-performance computer architectures and task scheduling implementations, which makes the design of generic parallel processing solutions feasible for geographical analyses.
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Keywords: Computational transformation; Cyberinfrastructure; Geographical analysis; Parallel processing; Spatial computational domain

Document Type: Research Article

Affiliations: 1: Department of Geography and National Center for Supercomputing Applications, The University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA 2: Department of Geography and Program in Applied Mathematical and Computational Sciences, The University of Iowa, Iowa City, IA 52242

Publication date: February 1, 2009

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