English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The problem of choosing spatial sampling designs for investigating unobservedspatial phenomenon S arises in many contexts, for example in identifyinghouseholds to select for a prevalence survey to study disease burden andheterogeneity in a study region D. We studied randomised inhibitory spatialsampling designs to address the problem of spatial prediction whilst takingaccount of the need to estimate covariance structure. Two specific classes ofdesign are inhibitory designs and inhibitory designs plus close pairs. In aninhibitory design, any pair of sample locations must be separated by at leastan inhibition distance {$\delta$}. In an inhibitory plus close pairs design, n- k sample locations in an inhibitory design with inhibition distance{$\delta$} are augmented by k locations each positioned close to one of therandomly selected n - k locations in the inhibitory design, uniformlydistributed within a disc of radius {$\zeta$}. We present simulation resultsfor the Matern class of covariance structures. When the nugget variance isnon-negligible, inhibitory plus close pairs designs demonstrate improvedpredictive efficiency over designs without close pairs. We illustrate how thesefindings can be applied to the design of a rolling Malaria Indicator Surveythat forms part of an ongoing large-scale, five-year malaria transmissionreduction project in Malawi.

Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure