Aug 22, 2016 09:23 AM EDT By Anita Valencia, UniversityHerald Reporter

Scientists viewed Earth from a satellite and managed to identify the poorest areas of the world.

The journey to eliminate poverty begins with identifying the areas. However, the current studies suggest door-to-door survey which is a time-consuming process. The new method can help the United Nations and many organizations to collect more supporting data of impoverished areas.

How scientists use satellite view to identify the poverty; nightlights and daytime imagery can predict the growth of a city

In the study, Standford University researchers use three parameters: nighttime lightings, daytime imagery and the consumption expenditure opted from survey. They examined African countries including Tanzania, Nigeria, Uganda, Rwanda and Malawi.

According to these scientists, the expenditure can help to identify the asset of a household. The night time luminosity view has been used in many studies but the estimation is 'rough'.

Neal Jean, one of the authors of this study explained, "In Africa, a lot of these places that are the most poor are actually just uniformly dark at night."

To better differentiate the poorest areas from the well-developed places, scientists combine the nightlight data with daytime imagery.

This technique can 'identify features in the higher-resolution daytime imagery that are correlated with economic development', National Geographics wrote.

Satellite-view of poverty can support household data, map the poverty in low budget

This method will enable organizations to easily identify which areas actually need funds the most. Based on the daytime imagery, it can accurately predict the poorest areas.

Another benefit in using satellite-view to identify poverty is how cheap it is. In fact, it is 'nearly costless', the scientists wrote in their paper.

Scientists hope to replace the traditional method of door-to-door surveys in the future. As for now, the high-res snapshots are only able to support the household data like asset wealth but unable to predict the poverty variations.

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