Kriging is an interpolation method that makes predictions at unsampled locations using a linear combination of observations at nearby sampled locations. The influence of each observation on the kriging prediction is based on several factors: 1) its geographical proximity to the unsampled location, 2) the spatial arrangement of all observations (i.e., data configuration, such as clustering of observations in oversampled areas), and 3) the pattern of spatial correlation of the data. The development of kriging models is meaningful only when data are spatially correlated.. Kriging has several advantages over traditional interpolation techniques, such as inverse distance weighting or nearest neighbor: 1) it provides a measure of uncertainty attached to the results (i.e., kriging variance); 2) it accounts for direction-dependent relationships (i.e., spatial anisotropy); 3) weights are assigned to observations based on the spatial correlation of data instead of assumptions made by the analyst for IDW; 4) kriging predictions are not constrained to the range of observations used for interpolation, and 5) data measured over different spatial supports can be combined and change of support, such as downscaling or upscaling, can be conducted.
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.