Development of models that estimate spatial patterns, distributions, or values from geographic and environmental data.
Methods can include spatial statistics, machine learning, interpolation, spatial regression, environmental variables, and uncertainty analysis.
Applications include ecological modeling, environmental prediction, resource analysis, and geographic research.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.