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Data from the World Resources Institute includes: percentage tree-cover, population density and tree cover, share of wood in fuel consumption, etc. SoilGrids1km - soil property and class maps SoilGrids1km is a collection of updatable soil property and class maps of the world at a resolution of 1 km produced using state-of-the-art model-based ...
The meaningfulness of the study data, or power, is indicated by the weight (size) of the box. More meaningful data, such as those from studies with greater sample sizes and smaller confidence intervals, is indicated by a larger sized box than data from less meaningful studies, and they contribute to the pooled result to a greater degree.
Volume can be calculated from the metrics recorded in a plot sample. For example, if a tree was measured to be 20m tall and with a DBH of 19 cm using previous measured tree data a volume could be approximated according to species. Such a table has been constructed by Josef Pollanschütz [5] in Austria. Volume of tree = BA X h x f pollanschutz
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population, and statisticians attempt to collect ...
Data quality assurance; Data set; Data-snooping bias; Data stream clustering; Data transformation (statistics) Data visualization; DataDetective – software; Dataplot – software; Davies–Bouldin index; Davis distribution; De Finetti's game; De Finetti's theorem; DeFries–Fulker regression; de Moivre's law; De Moivre–Laplace theorem ...
In bootstrap-resamples, the 'population' is in fact the sample, and this is known; hence the quality of inference of the 'true' sample from resampled data (resampled → sample) is measurable. More formally, the bootstrap works by treating inference of the true probability distribution J , given the original data, as being analogous to an ...
There are four main types of tree inventory. [2] They are specific problem inventory, partial inventory, complete inventory, and cover type inventory. A specific problem inventory gathers data on one particular problem such as looking at the effects of Hemlock Woolly Adelgid or Dutch elm disease.
As most tree based algorithms use linear splits, using an ensemble of a set of trees works better than using a single tree on data that has nonlinear properties (i.e. most real world distributions). Working well with non-linear data is a huge advantage because other data mining techniques such as single decision trees do not handle this as well.