Leveraging Enhanced Built-up Area Characteristics to Improve Spatial Population Distribution Modelling

Up-to-date and spatially detailed information on human population distribution is increasingly demanded for a broad range of applications such as risk analysis, disease modelling, poverty reduction, human health, sustainable urban development or security-related issues. Population grids such as WorldPop, GWPv4 and GHS-POP represent the state-of-the art in terms of open- and free population distribution datasets available at a global or continental scale; each of them employing different input data sources and different top-down disaggregation methods to assign population counts to a regular grid of fixed spatial resolution. In current population gridding approaches, there are, however, main constrains that arise from the spatial resolution and quality of the two major input data sources: specifically, the accuracy of the derived population grid is largely determined by the quality of the areal census-based population count data and the quality of the spatially explicit human settlements data. Therefore, the improvement of data describing the pattern and properties of human settlements offers a means to enhance human population disaggregation methods. Within this context, the German Aerospace Center (DLR) has developed a new suite of global layers and related analysis tools that accurately describe the built-up environment and its characteristics at a high spatial resolution (