The largest contributing factor to spatial data uncertainty is error. Error is defined as the departure of a measure from its true value. Uncertainty results from: (1) a lack of knowledge of the extent and of the expression of errors and (2) their propagation through analyses. Understanding error and its sources is key to addressing error-based uncertainty in geospatial practice. This entry presents a sample of issues related to error and error based uncertainty in spatial data. These consist of (1) types of error in spatial data, (2) the special case of scale and its relationship to error and (3) approaches to quantifying error in spatial data.
Scale and generalization are two fundamental, related concepts in geospatial data. Scale has multiple meanings depending on context, both within geographic information science and in other disciplines. Typically it refers to relative proportions between objects in the real world and their representations. Generalization is the act of modifying detail, usually reducing it, in geospatial data. It is often driven by a need to represent data at coarsened resolution, being typically a consequence of reducing representation scale. Multiple computations and graphical modi cation processes can be used to achieve generalization, each introducing increased abstraction to the data, its symbolization, or both.
The raster data model is a widely used method of storing geographic data. The model most commonly takes the form of a grid-like structure that holds values at regularly spaced intervals over the extent of the raster. Rasters are especially well suited for storing continuous data such as temperature and elevation values, but can hold discrete and categorical data such as land use as well. The resolution of a raster is given in linear units (e.g., meters) or angular units (e.g., one arc second) and defines the extent along one side of the grid cell. High (or fine) resolution rasters have comparatively closer spacing and more grid cells than low (or coarse) resolution rasters, and require relatively more memory to store. Active research in the domain is oriented toward improving compression schemes and implementation for alternative cell shapes (such as hexagons), and better supporting multi-resolution raster storage and analysis functions.
Vector data models are a subset of geographic information through which data are encoded as a geometric definition of a feature, organized within a framework that relates this geometry to the spatial location, context, and proximal relationship. Vector data models are often contrasted to raster data models, encoded in a regular grid spanning an extent with a fixed cell size. Vector data rely on geometric primitives, such as points, lines (sequences of linked points), and polygons (closed geometric forms), with variations on these and additional cases tailored to specific applications as they have emerged during the development of GIS. With the ability to define the location, placement, and interval of points that describe each feature, the resolution of the data are determined by the precision of placement of vertices from the sampled reality or when encoded in a processed form of the data. Due to their finite shapes, defined by their exact placement (point), the segment or overall length (line, polyline), or their area (polygon) vector data are commonly used to encode discrete, rather than continuous, features, with associated characteristics stored in an accompanying table of values corresponding to each shape feature.