Geospatial predictive models often require mapping of predefined concepts or categories with various conditioning factors in a given space. This chapter discusses various aspects of uncertainty in predictive modeling by characterizing different typologies of classification uncertainty. It argues that understanding uncertainty semantics is a perquisite for efficient handling and management of predictive models.
3. Typologies Of Classification Uncertainty
While there is increasing awareness of uncertainty, and its aspects and dimensions in predictive as well as classificatory schemes, little agreement exists among experts on how to characterize them. Many typologies of uncertainty have been suggested from risk analysis perspective, which often overlaps and builds on each other (Ferson & R.Ginzburg,1996; Linkov & Burmistrov2003; Regan et al., 2002). These typologies make distinctions between variability and lack of knowledge at the parameter and model level. However, from the geographic information perspective, the ontological specification of imperfection of geographic data provides some key vocabularies and taxonomies to deal with spatial uncertainties (Duckham et al., 2001; Worboys & Clementini, 2001). Such ontology distinguishes between inaccuracy (i.e., errors or commission or omission) and imprecision, which arises from limitations on the granularity of the schema or levels of detail obtainable for an observation under which the observation is made (Worboys, 1998). The concept “vagueness” refers to indeterminate boundary-line cases or “inexact concepts”.
Key Terms in this Chapter
Frame of Discernment: The set of all the possible sets of the hypotheses or class categories.
Inaccuracy: Lack of correlation between observation and representations of reality.
Imprecision: Lack of specificity or lack of detail in a representation.
Vagueness: A special type of imprecision that represents borderline cases of a concept.
Nonspecifity: A form of uncertainty which implies lack of specificity in evidential claims which can be represented as function of cardinality of undiscerned alternatives.
Mixel: A specialized type of pixel whose area is subdivided among more than one class.
Indiscernibility: A type of imprecision resulting from our inability to distinguish some elements in reality.