Poverty remains one of the most significant global challenges of this century. It is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability, and one of the largest causes of lost human potential. Adding to the challenge, poverty remains difficult to measure in those countries where it is most prevalent; census data in most low- and middle-income countries are often outdated or altogether unavailable. With traditional means falling short, the WorldPop/Flowminder team has taken a highly important step in mapping the distribution of poverty by combining anonymised data from mobile phones and satellite imagery data to create high resolution, dynamic maps of poverty indicators.

The full article is available at the following link: http://www.flowminder.org/what-we-do/data-science-analysis/publications/mapping-poverty-using-mobile-phone-and-satellite-data/

The researchers at Flowminder and the WorldPop Program at University of Southampton, have worked together with Telenor Research and mobile phone company Grameenphone to examine rates of poverty and its distribution across Bangladesh – analysing a range of information relating to mobile phone usage. The team found that by combining mobile data and geospatial data from satellites, they were able to produce poverty predictions which are comparable with those made from traditional sources, but with significant advantages.

Lead author Dr Jessica Steele explains: “The advantage of using mobile phone data is that it provides us with information which is continually updated, can be interrogated in a variety of ways and can track changes on an ongoing basis. Paired with satellite data that has similar features, it can give a much more dynamic view of poverty and its geographic spread.”

Every time a person uses a mobile it sends information to a receiving tower and gives an approximate location of where they are. It also contains information about levels of data usage, numbers of texts sent, times calls were made and their duration. It can reveal how much and how far people are travelling, as well as the type of phone they are using – i.e. basic mobile device, or smart phone.

This kind of anonymised data helps build a picture of poverty. For example, monthly credit consumption on mobiles, and the proportion of people in an area using them, can indicate household access to financial resources – while movements of mobiles and their use of networks provide information on individuals’ economic opportunities.

Similarly, remote sensing from satellites can indicate the living conditions of communities. Researchers in WorldPop/Flowminder have, for several years, undertaken research on how data on rainfall, temperature and vegetation reveals information about agricultural productivity, while how far people live from roads and cities and whether they can light their homes may reflect a community’s access to markets and information.

Dr Steele adds: “Satellite data can provide us with excellent information about living conditions in rural areas, but in tightly packed cities it’s more difficult. It’s the reverse for mobiles – more masts in cities means more information, contrasted with the countryside where mobile receiving towers can be thinly spread.”

Crafting targeted solutions to poverty requires knowing where affected people live. “Such data improve the understanding of the causes of poverty, enable improved allocation of resources for poverty alleviation programmes, and are a critical component for monitoring poverty rates over time”

Understanding modern poverty requires looking beyond the traditional census. Populations are mobile and the world changes rapidly, rendering poverty estimates extremely time sensitive. Household survey data is time-consuming and expensive, and completing a detailed census is often beyond the means of those countries that need it most. This technique empowers lower-income countries to understand their challenges and mobilize resources accordingly.

Not only does this method provide current information, it allows for a fuller understanding of the poverty landscape. This combination of spatial detail and frequent, repeated measurements may help distinguish between the temporarily poor and the chronically poor, as well as monitoring economic shocks. This more complete and nuanced understanding can inform evidence-based strategies to combat poverty. The greater understanding governments and relief organisations have of the full picture of poverty, the better equipped they are to create targeted, context-specific solutions.

This research is expanding to other countries, as it has potential to inform at regional, national, and international levels. Eradicating poverty in all its forms remains a major challenge and the first target of the United Nations Sustainable Development Goals. To eradicate poverty, it is crucial that information is available to identify the most vulnerable in society. These data promote informed decision making in resource allocation, poverty alleviation programs, and policies.

Findings from the study, funded by the Bill & Melinda Gates Foundation, are published in the Journal of the Royal Society Interface. http://rsif.royalsocietypublishing.org/content/14/127/20160690