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.
Foundational ontologies are tools for knowledge representation at a general level. They introduce hierarchies of concepts and relationships between these concepts. Foundational ontologies provide a common base for building more specific domain ontologies, which describe knowledge in expert domains. Foundational ontologies formally define categories and relations, improving the consistency of domain ontologies and facilitating their integration and knowledge exchange. Foundational ontologies structure knowledge in a hierarchy where at a highest level are defined universal entities and individuals, which instantiate the previous ones, and continuants and occurents, depending on their persistence in time. They also propose a set of abstract relationships from which more specific ones can be derived to relate concepts. Different foundational ontologies have been developed, proposing different knowledge organisations. In geospatial science, an important aspect of ontologies is the representation of spatial regions and spatial relationships. Regions can be built as point-sets or from atomic regions that correspond to elementary geospatial entities. The former approach facilitates quantitative reasoning in geometrical spaces while the latter is more appropriate for qualitative reasoning and the definition of high-level relationships. However, the representation must take into account the perception of boundaries and the possible vagueness of the concepts.
Uncertainty and error are integral parts of science and technology, including GIS&T, as they are of most human endeavors. They are important characteristics of knowledge, which is very seldom perfect. Error and uncertainty both affect our understanding of the present and the past, and our expectations from the future. ‘Uncertainty’ is sometimes used as the umbrella term for a number of related concepts, of which ‘error’ is the most important in GIS and in most other data-intensive fields. Very often, uncertainty is the result of error (or suspected error). As concepts, both uncertainty and error are complex, each having several different versions, interpretations, and kinds of impacts on the quality of GIS products, and on the uses and decisions that users may make on their basis. This section provides an overview of the kinds of uncertainty and common sources of error in GIS&T, the role of a number of additional related concepts in refining our understanding of different forms of imperfect knowledge, the problems of uncertainty and error in the context of decision-making, especially regarding actions with important future consequences, and some standard as well as more exploratory approaches to handling uncertainties about the future. While uncertainty and error are in general undesirable, they may also point to unsuspected aspects of an issue and thus help generate new insights.