The number one United Nations Sustainable Development Goal is to eliminate poverty, leaving nobody behind. Researchers in the United Kingdom are harnessing the large-scale data-processing power of Microsoft Azure to map the location of every person on Earth to provide the accurate population statistics needed to achieve this international humanitarian goal.
The WorldPop research team at the University of Southampton, U.K., provides critical data for tracking the UN Sustainable Development Goals by counting every person on Earth, where they are and who they are. The team does this using novel data science techniques and cloud computing to combine large datasets drawn from census, surveys, satellite, GIS and other sources to provide governments and NGOs with extremely detailed spatial and temporal mappings—some with resolutions down to 100 meters square. “The datasets can be so large and complex that it’s impractical or impossible to build them on a single workstation,” says Andy Tatem, a professor of geography and environment at the University of Southampton and the director of the WorldPop initiative. “But now our researchers are able to cut them down to size with the compute clusters and parallel computing that Microsoft Azure provides.”
WorldPop Research Fellow Jessica Steele has the computing power she needs to analyze how poverty and gender inequality are related to how people live and move. “Poverty is absolutely gendered,“ she says. “We know women are more likely to be poor and more susceptible to falling into poverty.”
Azure is helping Steele achieve more when analyzing large population datasets. “Running statistical models of poverty is a very iterative process. Being able to parallelize and speed up the process using Azure makes that iteration process shorter. This lets us get results back faster, talk to team members more quickly, and make decisions about how to move forward,” she says.
“At WorldPop, we’re shaving as much as 90 percent off our calculation run-times using Microsoft Azure,” Tatem says. “This frees us to focus more on data science, to improve the quality of our population mapping and ultimately to help governments and aid providers target poverty issues more efficiently and effectively.”