Poverty Mapping TechniquesHow many people live in poverty? The answer a search engine might give overlooks the complexity of the issue. A great deal of poverty data comes from the World Bank, which still relies on household surveys. These household surveys can be very inaccurate, and statistics like these are critical in the fight against poverty. Thankfully, many organizations are working on creating better poverty mapping techniques to help fight global poverty.

The Need for Poverty Mapping Techniques

Governments, private companies and NGOs must know who needs help, what works and how much they need in order to fight poverty. With more accurate data, aid programs can be rolled out more effectively, directly targeting populations who need it the most. Accurate data also determines the effectiveness of aid or other interventions, which helps agencies discover what works. It is important for the missions of many agencies to have accurate data on poverty, but methods for collecting this data are flawed.

One issue with current data collection is the amount of data available. The World Bank is a leader in the fight against global poverty, and it compiles many official statistics on poverty rates. Historically, the main way the World Bank typically measures poverty is through household surveys. However, these surveys do not reach as many people as they should. For lower-income countries, an annual investment of $1 billion would be required to expand these surveys to generate consistent, accurate data.

Not only are these surveys too narrow, but they are also not frequent enough. Surveys typically happen every few years and even every decade in some countries with lower capacities. Between 2002 and 2012, no poverty data was collected from 29 countries.

The Problems with Current Poverty Mapping Techniques

The most common surveying method employed by the World Bank is the household survey. Unfortunately, household surveys have built-in inaccuracies and miss many people, usually some of the poorest. This method tries to measure poverty by sending surveys to households, but these surveys are ill-suited to measure an atypical home environment. Many people trying to avoid poverty live in open households, whose membership is usually in flux. These households operate to reduce poverty collectively in ways that a typical survey cannot easily measure. When data from these households is not interpreted differently from other household data, overall data on poverty can be skewed.

Satellites Mapping Poverty

This dearth of accurate data was the inspiration for a team of Stanford researchers. Marchall Burke, David Lobell and Stefano Ermon have spent the better half of the last decade creating better poverty mapping techniques. The solution they are working on now is satellite mapping.

The team has used artificial intelligence to map poverty using publicly available satellite imagery. The system examines poverty by analyzing the wealth of assets in a given area as seen from space. By indexing images of wealthy areas and poor areas, the program can identify levels of poverty in other areas. It uses a variety of factors like lighting at night, roofing, infrastructure, roads and other easily recognizable traits to do so. Utilizing deep learning, the program is able to correlate factors and create an idea of poverty in an area with fairly high accuracy. The model explains about 70% of asset wealth variation at the village level. This means the model can predict more accurately than other attempts at mapping poverty using higher resolution imagery and mobile phone mapping. The ability to distinguish poverty at a village level also means that the program can identify levels of poverty in places that surveys never go, with much less cost and time required.

Household surveys have become obsolete compared to more modern and effective methods. Better poverty mapping techniques like the Stanford researchers’ will enable organizations to fight poverty with a greater level of accuracy, which will make this decade of poverty-fighting more efficient than the last.

– Brett Muni
Photo: Flickr