Managing Uncertainty in Geospatial Predictive Models

Managing Uncertainty in Geospatial Predictive Models

Iftikhar U. Sikder (Cleveland State University, USA)
DOI: 10.4018/978-1-4666-2038-4.ch086
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Abstract

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.
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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”.

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