Fighting poverty with Big Data
We’re back with another edition of our Great Data segment, where we cover Big Data developments that are making a positive impact around the world. This time around, we would like to introduce the notion of fighting poverty with Big Data. Pointedly, how Big Data is responsible for the creation of detailed and low-cost poverty maps and what the implications of this are.
1.2 billion people around the world live in extreme poverty
Let us set the scene. Even though conditions are improving, there are still 1.2 billion people around the world that live in extreme poverty. This equates to not having access to basic needs such as food, healthcare, and education. Primarily, it is Asia, the Caribbean, and sub-Saharan Africa that suffer from these conditions. The study in question focuses on Senegal, a country on Africa’s west coast.
Creating precise poverty maps faster, and at a lower cost
So, how exactly are organizations fighting poverty with Big Data? Well, both governments and organizations believe that accurate and timely information about living conditions is vital in order to improve them. One initiative dedicated to improving this information involves the creation of time-efficient and low-cost poverty maps.
Developed with the aid of the Bill and Melinda Gates Foundation, head-researchers Neeti Pokhriyal and Damien Christophe Jacques have developed a more precise method of collecting data. This involves combining satellite imagery with anonymous data from cellphone records. In turn, this means that researchers gain access to geospatial data such as infrastructure and cellphone data explaining social interactions. As a result, researchers can create a more precise map in a passive and therefore time-efficient and low-cost manner. The GIF below shows the difference in detail between traditional methods and those utilized Big Data collection.
How are the maps going to help?
As mentioned, timely and accurate information is necessary in order to know what problems need to be attacked first. However, these new mapping techniques will not necessarily replace traditional methods(that include surveys and census), as they provide useful qualitative information. Rather, the new methods will fill in the gaps, both when it comes to detail and timeliness. This is possible due to the less interactive nature of the new methods, allowing for them to be updated more frequently. Additionally, the new maps are also able to cover remote regions as well as those suffering from war and conflict. Basically, the new maps have improved accessibility.
Ultimately, by using Big Data to create poverty maps, researchers have access to real-time information at a substantially lower cost. This allows policymakers to make more informed decisions, hopefully accelerating the fight against poverty.
To read more about this project, visit the University of Buffalo’s webpage.
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