Collecting Spatio‐Temporal Data

Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey

“…The former aims at inferring population parameters while limiting spatial bias, with sampling strategies of varying degrees of randomness and stratification to ensure coverage. The latter requires the development and training of a model of the data generating process to select sampling locations that will reduce the model uncertainty (Mateu and Müller, 2012a), often simply sampling where the model is the most uncertain about its predictions. MNO is a case of optimal sampling which generally builds on an existing network and hence, existing measurements of the variable of interest (Mateu and Müller, 2012b).…”

Optimising sampling designs for the maximum coverage problem of plume detection

“…In general, spatial sampling design optimisation is not a convex problem -local optima do not need to be global - (Mateu and Müller, 2013a) and thus cannot be solved with common optimisation algorithms. However, Spöck and Pilz (2013) managed for some types of random fields to transform the classical problem of minimal kriging variance to a Bayes linear regression model that could be solved this way, using an approximation of a polar representation of the autocorrelation.…”

“…However, our main interest are the algorithms, so we give an overview over the main groups below. A collection of recent approaches can be found in Mateu and Müller (2013b).…”

Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey

“…The former aims at inferring population parameters while limiting spatial bias, with sampling strategies of varying degrees of randomness and stratification to ensure coverage. The latter requires the development and training of a model of the data generating process to select sampling locations that will reduce the model uncertainty (Mateu and Müller, 2012a), often simply sampling where the model is the most uncertain about its predictions. MNO is a case of optimal sampling which generally builds on an existing network and hence, existing measurements of the variable of interest (Mateu and Müller, 2012b).…”