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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 ...

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,...

Collaborative spatial decision-making environments in which group members individually and collectively pursue solutions to semi-structured problems have a unique set of geographic visualization requirements. Group members often pursue diverse ...

When MCMC methods for Bayesian spatiotemporal modeling are applied to large geostatistical problems, challenges arise as a consequence of memory requirements, computing costs, and convergence monitoring. This article describes the parallelization of a ...

Choropleth maps are often used to visualize the spatial distribution of information collected for enumeration units. Such maps, however, are normally produced without considering the effect of uncertainty associated with data, which can contribute to ...

Spatial interpolation is widely used in geographical information systems to create continuous surfaces from discrete data points. The creation of such surfaces, however, can involve considerable computation, especially when large problems are addressed, ...

This paper discusses a new model of parallel evolutionary algorithms (EAs) called the specialized island model (SIM) that can be used to generate a set of diverse non-dominated solutions to multiobjective optimization problems. This model is derived ...

During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an ...

Spatial association measures, when computed for large data sets, have significant computational requirements. Parallel processing is one way to address these requirements. The design of parallel programs, however, requires careful planning since many ...