gridding

Gridding is the process of converting scattered or irregularly spaced data into a regular grid or raster. This creates a uniform data structure suitable for analysis, visualization, or numerical modeling. In geospatial contexts, gridding produces raster datasets from point measurements such as weather observations or sensor networks, enabling spatial analysis on a consistent grid. Gridding can be performed by interpolation methods, including nearest neighbor, inverse distance weighting, and spline-based approaches such as bicubic interpolation, as well as geostatistical methods like kriging that estimate values based on spatial statistics.

In computational imaging and signal processing, gridding often refers to mapping irregular samples in a transformed domain onto a grid before applying fast algorithms such as the fast Fourier transform. This uses convolutional gridding with a kernel (for example Kaiser-Bessel or sinc) followed by correction to counteract kernel effects. The non-uniform fast Fourier transform (NUFFT) relies on gridding as a key step.

Applications of gridding span several fields. In GIS and environmental science, gridded data underpin digital elevation models and climate or weather models. In astronomy, gridding is used to assemble images from irregularly sampled interferometric data. Gridding also appears in medical imaging and other disciplines where measurements taken at irregular locations must be represented on a regular lattice for processing.

Key considerations include the choice of grid resolution, coordinate reference systems or projections, and how edge effects and smoothing influence results. High-resolution grids capture more detail but require more storage and computation, while coarse grids may obscure local variation. The selected gridding method should balance accuracy, efficiency, and the intended analysis or visualization goals.