Gridded Livestock

Caution: Two versions of each species distribution are produced. In the first version, 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, 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. Resource Watch displays the dasymetric weighting product. The dasymetric version is recommended for applications where spatial detail matters more than concerns about circularity in the analytical workflow in relation to spatial predictor covariates. However, the authors warn potential users against over-interpretation of spatial accuracy of the dasymetric product. When circularity concerns are more important than detailed spatial resolution, it is recommended that the area weighted versions be used. Users can find both versions of the data on the source website, and again Resource Watch displays the dasymetric weighting data divided by the area per pixel, resulting in animal densities or animals per square kilometer. Resource Watch shows only a subset of the data set. For access to the full data set and additional information, see the Learn More link.

Due to the nature of the livestock subnational census statistics, the age and spatial resolution of the census statistics vary by age and by livestock animal. The oldest subnational census data are for the Democratic Republic of the Congo (1994), whilst some countries, for example Turkey, have very recent data (2014). The average spatial resolution (ASR) varies from country to country, with countries such as Italy and Thailand having very detailed subnational data (ASR<10 km2 and a mean area<100 km2), and countries such as Russia or South Africa with very coarse subnational census data (ASR > 250 km2 and mean area > 62 500 km2). Decisions regarding the use of this version of GLW over smaller spatial extents should be taken in relation to the ASR of the underlying census data. For example, analyses of GLW data in Brazil, Spain or Thailand would be appropriate because their respective ASRs are small relative to the size of the country. In contrast, we would discourage the country-level use of GLW data in countries such as Russia or Mali, because the ASR values are particularly high (>250 km2).