A team of researchers from the Complexity Science Hub (CSH) and Central European University (CEU) created more-detailed poverty maps using computational tools that bring together survey information, and data and images provided by public sources such as Google and Meta (Facebook).
The poverty maps for Sierra Leone and Uganda, two Sub-Saharan African countries with extreme poverty, will be presented early May at the Web Conference 2023 in Texas, US.
For years, policymakers, planners, and researchers have relied on surveys and census data to track and respond to poverty. “Gathering this information, however, can be time-consuming and expensive,” explains research fellow Lisette Espín-Noboa, a postdoc at CSH and CEU and first author of the study. “And a census may not include hard-to-reach areas.”
“If you ask why we need high-resolution poverty maps, the answer is simple. These incredibly detailed poverty maps can identify places in need that otherwise wouldn’t be captured in maps using only census data,” adds CEU associate professor Márton Karsai, co-author of the study.
“In regions with a lack of direct information, it is challenging to estimate the socio-economic status of people. In our study we focused on two countries, Uganda and Sierra Leone, where high-resolution, large-scale estimates are not available,” points out professor János Kertész, head of the Department of Network and Data Science at CEU and co-author of the study.
In the first step, the team gathered demographic data from two surveys. “In particular, we focused on the housing characteristics questionnaire. This helps estimate household wealth by considering the quality and quantity of available facilities or assets at home,” say the researchers.
For both countries, International Wealth Index (IWI) scores were calculated. IWI indicates how well a home possesses a basic set of assets. The lower the value, the lower the quality of housing. And the higher the value, the wealthier the household.
Second, Espín-Noboa and colleagues compiled a second dataset of satellite imagery and geographic information system data provided by crowdsourcing and social media sources, like Google and Meta (Facebook).
Over 900 features were extracted that offer insight into the economic status of the population, the infrastructure development of the area, and other wealth indicators. “This data shows, for instance, how many antennas are located in a certain area, or the number of people visiting the same area. It can also indicate how many Facebook users own an iPhone,” explains Espín-Noboa.