AM-106 - Error-based Uncertainty

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.
FC-02 - Epistemology
Epistemology is the lens through which we view reality. Different epistemologies interpret the earth and patterns on its surface differently. In effect, epistemology is a belief system about the nature of reality that, in turn, structures our interpretation of the world. Common epistemologies in GIScience include (but are not limited by) positivism and realism. However, many researchers are in effect pragmatists in that they choose the filter that best supports their work and a priori hypotheses. Different epistemologies – or ways of knowing and studying geography – result in different ontologies or classification systems. By understanding the role of epistemology, we can better understand different ways of representing the same phenomena.