CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2008; 20:1697–1720 Published online 6June 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cpe.1294 Grid computing of spatial statistics: using the TeraGrid for G * i (d) analysis Shaowen Wang 1, ∗, † , Mary Kathryn Cowles 2 and Marc P. Armstrong 3 1 Department of Geography and National Center for Supercomputing Applications, The University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A. 2 Department of Statistics and Actuarial Science and Department of Biostatistics, The University of Iowa, Iowa City, IA 52242, U.S.A. 3 Department of Geography and Program in Applied Mathematical and Computational Sciences, The University of Iowa, Iowa City, IA 52242, U.S.A. SUMMARY The massive quantities of geographic information that are collected by modern sensing technologies are difficult to use and understand without data reduction methods that summarize distributions and report salient trends. Statistical analyses, therefore, are increasingly being used to analyze large geographic data sets over a broad spectrum of spatial and temporal scales. Computational Grids coordinate the use of distributed computational resources to form a large virtual supercomputer that can be applied to solve computationally intensive problems in science, engineering, and commerce. This paper presents a solution to computing a spatial statistic, G * i (d) using Grids. Our approach is based on a quadtree-based domain decomposition that uses task-scheduling algorithms based on GridShell and Condor. Computational ex- periments carried out on the TeraGrid were designed to evaluate the performance of solution processes. The Grid-based approach to computing values for G * i (d) shows improved performance over the sequential algorithm while also solving larger problem sizes. The solution demonstrated not only advances knowledge about the application of the Grid in spatial statistics applications but also provides insights into the design of Grid middleware for other computationally intensive applications. Copyright © 2008 John Wiley & Sons, Ltd. Received 2 January 2006; Revised 18 June 2007; Accepted 27 November 2007 KEY WORDS: Grid computing; geographic information systems; spatial statistics; quadtree; G ∗ i (d ) statistic ∗ Correspondence to: Shaowen Wang, Department of Geography, University of Illinois at Urbana-Champaign, Room 220 Davenport Hall, 607 South Mathews Avenue, Urbana, IL 61801, U.S.A. † E-mail: shaowen@uiuc.edu Contract/grant sponsor: National Science Foundation; contract/grant number: TG-DMS040004T Copyright 2008 John Wiley & Sons, Ltd.