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Alternative Methods of Analysis
Aside from the tests conducted by George and Blakely, very little work has been done to determine appropriate cup spacing and grid dimensions needed for an unbiased, efficient sampling method. Linear interpolation, often in one direction, has been frequently employed to estimate unknown values. Because ground patterns are three dimensional, it may be more appropriate to treat these data as spatial, and to use spatial statistical tools for estimation, prediction, and experimental design.
Spatial statistical methods take into account the location of an observation as well as the value of the observation to obtain more accurate estimates of unknown values (Isaaks and Srivastava 1989). This information is used to build a model for prediction and sampling improvements. Spatial data such as those obtained from drop tests are usually correlated. Modeling this correlation structure improves accuracy of estimation and prediction. Spatial statistics is a relatively new field and has proven more accurate for estimating spatial data. Using these methods for drop testing may lead to a greater understanding of our aerial capabilities and improve our wildland firefighting.
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