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 416740
Title Multiphase sampling using expected value of information
Author(s) Bruin, S. de; Ballari, D.E.; Bregt, A.K.
Source In: Proceedings of the 7th International Symposium on Spatial Data Quality, Raising awareness of Spatial Data Quality, Coimbra, Portugal, 12 - 14 October, 2011. - Coimbra, Portugal : University of Coimbra - p. 65 - 70.
Event Coimbra, Portugal : University of Coimbra Proceedings of the 7th International Symposium on Spatial Data Quality, Raising awareness of Spatial Data Quality, Coimbra, Portuga, 2011-10-12/2011-10-14
Department(s) Laboratory of Geo-information Science and Remote Sensing
Centre Geo-information
PE&RC
Publication type Contribution in proceedings
Publication year 2011
Abstract This paper explores multiphase or infill sampling to reduce uncertainty after an initial sample has been taken and analysed to produce a map of the probability of some hazard. New observations are iteratively added by maximising the global expected value of information of the points. This is equivalent to minimisation of global misclassification costs. The method accounts for measurement error and different costs of type I and type II errors. Constraints imposed by a mobile sensor web can be accommodated using cost distances rather than Euclidean distances to decide which sensor moves to the next sample location. Calculations become demanding when multiple sensors move simultaneously. In that case, a genetic algorithm can be used to find sets of suitable new measurement locations. The method was implemented using R software for statistical computing and contributed libraries and it is demonstrated using a synthetic data set.
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