WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data and methods can be found in Tatem et al, a description of the modelling methods used found in Stevens et al, and access to modelling code here. The 'Individual countries 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. Mosaiced 1km resolution global datasets from this project can be downloaded from the ‘Global mosaics 2000-2020’ link below. These global efforts necessarily involved some shortcuts for consistency. Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing methods and time periods are still available for download here: Individual countries and Whole Continent
The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al. and James et al..
The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of pregnancies to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age, age-specific fertility rates, still births and abortions to map the estimated distributions of pregnancies for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al and James et al..
East–Southeast Asia is currently one of the fastest urbanizing regions in the world, with countries such as China climbing from 20 to 50% urbanized in just a few decades. However, spatially-and temporally-detailed information on regional-scale changes in urban land or population distribution have not previously been available; previous efforts have been either sample-based, focused on one country, or drawn conclusions from datasets with substantial temporal/spatial mismatch and variability in urban definitions. In collaboration with the World Bank and University of Wisconsin-Madison, WorldPop used consistent methodology, satellite imagery and census data for >1000 agglomerations in the East–Southeast Asian region to map population changes between 2000 and 2010. The data are available here and described in detail in Schneider et al, and this report.
Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing methods and time periods are still available for download here: Individual countries and Whole continent.
Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. WorldPop develops methods for the integration of geolocated cluster sample data from household surveys with geospatial covariates in Bayesian geostatistical modelling frameworks to map key development and health indicators at high spatial resolution. These include indicators relating to poverty, literacy, sanitation, maternal and newborn health, contraceptive use and vaccination coverage, among others. Details of methods used and outputs can be found in Utazi et al 1, Utazi et al 2, Steele et al, Bosco et al, Ruktanonchai et al and Tatem et al.
The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country’s economic prospects and health burdens. WorldPop’s assembly of subnational population pyramid data has here been used to produce continent-wide subnational scale dependency ratio datasets, with full details found in Pezzulo et al.
Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. Quantifying and modeling human migration can contribute towards a better understanding of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. WorldPop has worked to pair census microdata from multiple low and middle income countries with additional geospatial datasets to develop models for internal migration flows, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. These have been applied to map internal migration for all low and middle income countries, with the datasets available to download here, and methods described in Sorichetta et al and Garcia et al.
Knowing where people are is critical for accurate impact assessments and intervention planning, particularly those focused on population health, food security, climate change, conflicts, and natural disasters. WorldPop has demonstrated how data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. Methods are described in Deville et al and zu Erbach-Schoenberg et al, and datasets representing estimated monthly population distributions for France and Portugal are available to download here.
The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers and analysts to rely on scheduled flight seat capacity data or simple models of flow. WorldPop has developed models of global air passenger flows, with annual flow methods described in Huang et al, seasonal flows described in Mao et al, and the output estimates available to download here.
Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end WorldPop has produced an open access archive of 3 and 30 arc-second resolution gridded data, which are available to download here and are described further in Lloyd et al 2019 and also Lloyd et al 2017.
Changing populations are often accompanied by changing built-settlement landscapes. Here, small area population data and a limited set of environmental covariates have been combined with machine learning methods and dynamically-limited growth curves to annually interpolate (from 2000 to 2014) and annually project (from 2015 to 2020) the presence of built-settlements across the globe at 100m resolution. These annual built-settlement maps were then used to inform the WorldPop "Global per country 2000-2020" population datasets. An overview of the built-settlement growth modeling framework can be found in Nieves et al.
Grid-cell surface areas