The Application of a Geographically Weighted Principal Component Analysis for Exploring Twenty-three Years of Goat Population Change across Mongolia

Abstract

The dzud are extreme weather events in Mongolia of deep snow, severe cold, or other conditions that render forage unavailable or inaccessible, which in turn results in extensive livestock deaths. Mongolia is economically vulnerable to extreme events due to an increase in nonprofessional herders and the livestock population, brought about by a deregularized industry. Thus, it is hugely informative to try to understand the spatial and temporal trends of livestock population change. To this end, annual livestock census data are exploited and a geographically weighted principal component analysis (GWPCA) is applied to goat data recorded from 1990 to 2012 in 341 regions. This application of GWPCA to temporal data is novel and is able to account for both temporal and spatial patterns in goat population change. Furthermore, the GWPCA methodology is extended to simultaneously optimize the number of components to retain and the kernel bandwidth. In doing so, this study not only advances the GWPCA method but provides a useful insight into the spatiotemporal variations of the Mongolian goat population.