Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 357622
Title The Data Uncertainty Engine (DUE): a software tool for assessing and simulating uncertain environmental variables
Author(s) Brown, J.D.; Heuvelink, G.B.M.
Source Computers and Geosciences 33 (2007)2. - ISSN 0098-3004 - p. 172 - 190.
Department(s) Land Dynamics
Publication type Refereed Article in a scientific journal
Publication year 2007
Keyword(s) modeling error - rejection - beta - propagation - prediction - poisson - gamma - gis
Abstract This paper describes a software tool for: (1) assessing uncertainties in environmental data; and (2) generating realisations of uncertain data for use in uncertainty propagation analyses: the ¿Data Uncertainty Engine (DUE)¿. Data may be imported into DUE from file or from a database, and are represented in DUE as objects whose positions and attribute values may be uncertain. Objects supported by DUE include spatial vectors, spatial rasters, time-series of spatial data, simple time-series and objects that are constant in space and time. Attributes supported by DUE include continuous numerical variables (e.g. rainfall), discrete numerical variables (e.g. bird counts) and categorical variables (e.g. land-cover). Once data are imported, an uncertainty model can be developed for the positional and attribute uncertainties of environmental objects. This is currently limited to probability models, but confidence intervals and scenarios will be provided in the future. Using DUE, the spatial and temporal patterns of uncertainty (autocorrelation), as well as cross-correlations between related inputs, can be incorporated into an uncertainty analysis. Alongside expert judgement, sample data may be used to help estimate uncertainties, and to reduce the uncertainty of the simulated output by ensuring each realisation reproduces the sample data. Most importantly, DUE provides a conceptual framework for structuring an uncertainty analysis, allowing users without direct experience of uncertainty methods to develop realistic uncertainty models for their data.
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