Geographic Information Systems (GIS) provide not only powerful functions to store, manage, and visualize geospatial data, but also advanced analytical tools to reveal some otherwise less obvious yet significant spatial patterns and relationships between environmental variables. Furthermore, GIS enable the development of geospatial models that utilize GIS data and functionality to simulate spatial relations, interactions, and processes, hence to test hypothesis, improve our understandings, and provide supports for decision making. GIS-based spatial modeling approaches have been used in a variety of  scientific investigations and practical applications, including:

• natural resource management
• ecosystem protection
• utility management
• transportation management
• urban planning

Recent geospatial practices have seen two significant trends:

1. the exponential increase in data volume (geospatial and non-geospatial) and rapid decrease in data resolution (spatial and temporal) due to the improvement of data collecting technologies (e.g., remote sensing and GPS) and the adoption of information networks
2. the fast increase in complexity and sophistication of geospatial analysis and models driven by the advancement not only in geospatial science but also in other fields

Computational science, "concerned with constructing mathematical models and numerical solution techniques and using computers to analyze and solve scientific, social scientific, and engineering problems" (Wikipedia), has been adopted in geospatial studies in the form of GeoComputation.

GeoComputation, usually considered to be based on but not limited to GIS, provides analytical and modeling methods and techniques developed in a variety of fields including mathematics, physics, biology, and computer science to solve some complex geospatial problems that traditional GIS are unable to solve. For example, many GeoComputational methods originated from the field of Artificial Intelligence (AI), including Artificial Neural Networks (ANN), Cellular Automata (CA), Agent-based Modeling (ABM), and Genetic Algorithms (GA), have been used in the modeling of land-use and land-cover change, species distribution and movement, human spatial behavior, and traffic. Also, due to the vast volume of data and high complexity of algorithms, computational intensity has become a major bottleneck in modern geospatial information processing, often causing problem-solving processes intractable in terms of computing time or making real-time responses impossible.

GeoComputation therefore includes the study of using the High-Performance Computing (HPC) technology to speed up the process and significantly reduce the computing time. Last but not least, the emerging Cybeinfrastructure allows geospatial practitioners to have easy access to HPC facilities, share data and analytical/modeling services, and form virtual organizations (VO) for communication and collaboration.