The resulting adjustment factor is multiplied by each administrative unit census value for the target year. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, please visit: įor information about how to use HDX to access these datasets, please visit: Īdjustments to match the census population with the UN estimates are applied at the national level. The resulting maps are the most detailed and actionable tools available for aid and research organizations. Then we work with our partners at Columbia University to overlay general population estimates based on publicly available census data and other population statistics. To create our high-resolution maps, we use machine learning techniques to identify buildings from commercially available satellite images. High-resolution population maps are crucial for many planning tasks, from urban planning 1 to preparing humanitarian actions 2 and effective disaster response 3.Creative Commons Attribution International Given the rapid population growth in many regions of the world 4 and the increasing rate at which populations shift in response to environmental and social changes, it is important to maintain accurate, up-to-date maps. Unfortunately, census data are often only available at very coarse spatial resolution (e.g., one aggregate number for a district with hundreds or even thousands of km 2) and therefore not suitable as a basis for local planning: whether for sustainable land use and infrastructure management or for targeted disaster relief, planners need to know in more detail where the people are. The problem is especially prevalent in developing countries in the global south, where humanitarian actions are more often needed yet census data availability and quality are limited. Remote sensing products and other openly available geographical datasets like OpenStreetMap (OSM) can serve as auxiliary, high-resolution evidence to create fine-grained population density maps 5. Yet, the design of effective population density models 6 that combine such data sources with low-resolution census counts remains a challenge. Generally speaking, two different approaches have been employed for population mapping 7: bottom-up and top-down. Bottom-up methods 7, 8 start from local surveys of population density, collected at a number of sample locations, and attempt to generalize from detailed but sparse samples to the unobserved regions to cover larger areas. Researchers have proposed different ways to locally measure population density, such as counting the (average) number of people per rooftop area 7, 8 or, if more resources are available for the local survey, specific average densities for different types of residential zones (urban-, rural-, and non-residential) 7, 9. A main drawback of bottom-up methods is that local surveys will necessarily remain extremely sparse and can hardly provide enough data points to scale population mapping up to the country level. On the contrary, top-down approaches 6, 10 rely on census data, which ensures complete coverage at the expense of much lower spatial resolution, in some cases down to a single head count per large district. The task then becomes to disaggregate that data to a much finer resolution, often a regular grid. Top-down approaches 6, 11 commonly use dasymetric disaggregation to redistribute the known, spatially coarse population counts for census areas on the order of many km 2 across smaller spatial units 12-for instance square blocks of size 100 × 100 m-with the help of auxiliary variables that covary with population density.
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