The World Bank in 2012 estimated that over 330 million Africans were living below the poverty level, a position which they tag as; surviving on $1.90 per day.
Despite this knowledge, it has proved near impossible to create appropriate programs that target poor communities and alleviate their sufferings.
The reason for this inability varies. For one, countries are reluctant to report on their own inequality, mainly due to instances of corruption on the part of the government or other individuals higher up in the community.
For instance, World Bank attempted to conduct population surveys centered around poverty between 2000 and 2010. Out of 59 African countries, only 39 countries completed less than two of the population studies. Of that number, 14 reported no data at all, and the information that was actually gathered from the rest will never reach the public domain.
In other to have more concise and open data, researchers have attempted other means of measuring poverty using alternative data sets, inclusive of; data gathered via social media, web search queries, and mobile network usage.
More traditional methods of data collection like going from house to house proved to be too expensive and could even sometimes turn dangerous in the cases of violence or civil unrest in the country.
A new way of measuring poverty has, however, been uncovered by researchers. It is a combination of measurements of artificial light pollution with artificial intelligence.
Simply, they tell which areas are wealthier by the amount and type of light they are using. Higher luminosity would mean more infrastructure, development, and wealth whereas areas with frequent blackouts would signify poverty.
They combine that information with an algorithm that closes data gaps based on patterns it learns from looking at satellite shots and get a picture of what areas in a country are below the poverty line.
A study which involved five African countries; Nigeria, Tanzania, Uganda, Malawi, and Rwanda, using this method of satellite imagery from space for measuring poverty remotely was published in Science.
Further work on this method of measuring poverty will solve the problem of lack of data which makes distributing needed funds in poor communities difficult in Africa. Impoverished Africans will be easier to get to with lower blockades of bureaucracy and less of a chance that the funds will be reduced due to corruption.