Decisions, uncertainty and spatial information

In this paper we review how the uncertainty in spatial information has been characterized. This includes both continuous predictions of spatial variables, and thematic maps of landcover classes. We contend that much work in this area has failed to engage adequately with the decision processes of the end-user of information, and that the engagement of spatial statisticians is essential to achieve this. We examine generalized measures of uncertainty, and those focussed on particular decision models. We conclude that the latter are likely to be the most fruitful, particularly if they emerge from a formal decision analysis. We outline the principles of value of information theory, and suggest that this represents an ideal framework in which to develop measures of uncertainty which can support both the rational collection of data and the interpretation of the resulting information. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).