Spatial data can be represented in vector or raster form. The vector spatial data model is coordinate-based and represents geographic features as points, lines, and polygons. The raster spatial data model is pixel-based and represents geographic phenomena as an organized matrix of cells. Each model possesses advantages, disadvantages, and tradeoffs in how data can be manipulated, analyzed, and rendered. As a result, GIS professionals often need to work between data models to achieve their analytical goals. Vector-to-raster and raster-to-vector conversions are fundamental spatial data manipulation processes used to transform one model of spatial data representation into the other to extend the utility of a spatial dataset. Vector-to-raster conversion, also known as rasterization, is the process of converting vector points, lines, and polygons into a surface of gridded cells or pixels. Advanced rasterization techniques, such as spatial interpolation and density mapping, can be used to predict raster surfaces at unsampled locations based on known values of nearby vector spatial data inputs. Raster-to-vector conversion, also known as vectorization, is the process of converting gridded cell- or pixel-based data into vector points, lines, and polygons. While powerful, these conversion processes also have implications for geographic accuracy and potential feature loss.
Gridding is the act of taking a field of measurements and discretizing it into a regular tessellation, often either a lattice of squares or hexagons. Gridding can either discretize continuous phenomena or aggregate discrete instances; in either case, gridding serves conceptually to assist analysis, for example in finding local minima or maxima (i.e., "hotspots"). The process of gridding often involves interpolation, which is the rational estimation of unknown data values within the bounds of known values. Contouring refers to the creation of isolines throughout a data surface, often one represented by a grid. This section describes gridding, interpolation, and contouring, highlighting a few example methods by which interpolation is frequently done in the geospatial analysis.