This community innovation has been accepted at the 2026 DHIS2 Annual Conference and will be included a session.
WorldPop: Global and local population mapping
Population data at local levels are a fundamental component of healthcare planning, health interventions and health information systems. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. This lack of recent and reliable small area population data means that services are distributed inequitably, resources are wasted and people are left out of decision making processes. WorldPop (www.worldpop) is an interdisciplinary applied research group at the University of Southampton focusing primarily on supporting improvement of the spatial demographic evidence base and the use of these data for health and development applications. The group partners with stakeholders to co-develop methods for integrating satellite, survey, cellphone and other geospatial data to fill population data gaps, build trust and ensure adoption and use of the outputs. WorldPop’s new ‘Global2’ small area 2015-2030 age/sex-structured population estimates are available for all countries in DHIS2, providing an alternative source of demographic data where census or official estimates do not meet needs. The presentation will provide an overview of the Global2 data, including how the data are built and how it can be used, as well as learning resources and collaboration opportunities. Example use cases for vaccine microplans, health metric denominators, resource allocation and disease modelling with governments and UN agencies will be highlighted.
Primary Author: Andrew Tatem
Keywords:
Population, Mapping, Demography, Satellite, GIS, Geospatial, Statistical modelling, National statistics, Denominator, Small area estimation, Health metrics