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Handling uncertain spatial data in monitoring and control problems

Banff International Research Station for Mathematical Innovation and Discovery

In many ecological problems spatial data are collected to satisfy the requirement that the population spatial distributions can be reconstructed with high accuracy. The situation, however, may be different when reconstruction of spatial distributions is required in the context of monitoring and control (M&C) protocol. In my talk I will argue that the M&C protocol can be thought of as a data filter as its application transforms the original dataset and it often results in a spatial distribution with essentially different properties. That transformation may, in turn, alleviate negative impact of uncertainty on the accuracy of results when spatial distributions are reconstructed from data with measurement errors. While original data are affected by the measurement errors, the filtered data may or may not be affected depending on the filter definition. In some cases, there is no need to ask for more accurate data collection as measurement errors will be `eliminated' by application of the M&C protocol. Meanwhile, it will also be shown in the talk that if inherent uncertainty presents in the model, the M&C data filter may become useless, no matter how accurate the data are. This is a joint work with John Ellis and Wenxin Zhang.

Mathematics; Partial differential equations; Biology and other natural sciences; Mathematical biology

video/mp4

Author affiliation: University of Birmingham

2020-01-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/73411

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/