Accounting for spatial non-stationarity to estimate population distribution using land use/cover. Case Study: the Lake Naivasha basin, Kenya
Remotely-sensed data can be used to overcome deficiencies in data availability in poorly monitored regions. Reliable estimates of human population densities at different spatial levels are often lacking in developing countries. This study explores the applicability of a geographically-weighted regression (GWR) model for estimating population densities in rural Africa using land use/cover data that have been derived from remote-sensing while accounting for spatial non-stationarity.