NSU MA Graduate Research Helps Assess Poverty in Zambia
Owen Siyoto, an MA graduate of the Big Data Analytics & Artificial Intelligence Program at the NSU Mechanics and Mathematics Department, completed a thesis applying the learning transfer and ridge regression concepts to satellite imagery to predict poverty in Zambia. He assessed the Zambia Welfare Index using survey data, satellite imagery, and machine learning methods and yielded results comparable to similar work done in other countries.
The learning transfer concept is gaining popularity for those working with Google satellite images with day and night light. Siyoto used a Convolutional Neural Network (CNN) that was previously trained to predict the well-being of an area based on the intensity of light in the image. In addition, the neural network made it possible to identify the types of buildings and roads to highlight the characteristics of the rich and poor areas of Zambia. Using this data, another algorithm was trained to estimate the poverty level of an area according to a set of characteristics.
With the successful application of learning transfer and ridge regression, the MA graduate was not only able to assess the level of well-being of the country as a whole, but he demonstrated that the positive results from his model were not a statistical accident. In addition, the research provides statistics that governments and other stakeholders can use to solve the problem of poverty. Siyoto argues that this data would be nearly impossible to obtain at lower administrative levels due to the cost and machine learning technologies make the data available almost free of charge.