Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator.
Two versions of each species distribution are produced. In the first version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates (dasymetric weighting). In the second version (AW), animal numbers are distributed homogeneously with equal densities within their census polygons (areal weighting) to provide spatial data layers free of any assumptions linking them to other spatial variables.
In the DA distribution models different animal densities are assigned to different pixels within a given census polygon. Spatial modeling is based on Random Forests models (RF), a machine-learning technique recently shown to provided more accurate gap-filling and disaggregation of livestock data than did the multivariate regression methods used in previous versions of Gridded Livestock of the World.
In contrast, the AW models simply spread individuals of a census polygon evenly, and the density of animals in each pixel corresponds to the average number of animals per km2 of suitable land in the census unit.
The AW models are free of the influence of other spatial predictor variables, at the cost of displaying cruder distribution patterns, especially in large census areas containing a wide range of different environmental, land-use and farming conditions. In polygons where input census data were missing, the AW model simply includes the aggregated predictions of the DA models, and a separate layer is provided for the user that distinguishes between predictions and census observations.
For each species, comprehensive metadata are provided on the input census data for each country (e.g. year, resolution and source), as well as goodness-of-fit metrics of the models by continent and by the size of the administrative unit from which the census data came. This enables users to assess the quality of the estimates for each combination of species, country and size of census unit.