Abstract
Advances in remote sensing, dasymetric mapping techniques, and the ever-increasing availability of spatial datasets have enhanced global human population distribution databases. These datasets demonstrate an enormous improvement over the conventional use of choropleth maps to represent population distribution and are vital for analysis and planning purposes including humanitarian response, disease mapping, risk analysis, and evacuation modeling. Dasymetric mapping techniques have been employed to address spatial mismatch, but also to develop finer resolution population distributions in areas of the world where subnational census data are coarse or non-existent. One such implementation is the LandScan Global model which provides a 30 arc-second global population distribution based on ancillary datasets such as land cover, slope, proximity to roads, and settlement locations. This work will review the current state of the LandScan model, future innovations aimed at increasing spatial and demographic resolution, and situate LandScan within the landscape of other global population distribution datasets.
