FN Archimer Export Format
PT J
TI Spatially balanced sampling designs for environmental surveys
BT 
AF Kermorvant, Claire
   D’Amico, Frank
   Bru, Noëlle
   Caill-Milly, Nathalie
   Robertson, Blair
AS 1:1;2:1;3:1;4:2;5:3;
FF 1:;2:;3:;4:PDG-ODE-LITTORAL-LERAR;5:;
C1 Laboratoire de Mathématiques et de leurs Applications de Pau – MIRACNRS/Univ Pau & Pays Adour/E2S UPPA, Anglet, France
   Ifremer, Laboratoire Environnement Ressources d’Arcachon,Anglet, France
   School of Mathematics and StatisticsUniversity of CanterburyChristchurch, New Zealand
C2 UNIV PAU & PAYS ADOUR, FRANCE
   IFREMER, FRANCE
   UNIV CANTERBURY, NEW ZEALAND
SI ANGLET
SE PDG-ODE-LITTORAL-LERAR
IN WOS Ifremer UPR
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 1.903
TC 19
UR https://archimer.ifremer.fr/doc/00509/62063/66268.pdf
LA English
DT Article
CR EVALUATION STOCK PALOURDES BASSIN D'ARCACHON
DE ;BAS;GRTS;LPM;Probabilistic sampling;Spatially balanced
AB Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs. 
PY 2019
PD AUG
SO Environmental Monitoring And Assessment
SN 0167-6369
PU Springer Science and Business Media LLC
VL 191
IS 8
UT 000477957100001
DI 10.1007/s10661-019-7666-y
ID 62063
ER

EF