Virtual Networks and Poverty Analysis in

Neeti Pokhriyal Wen Dong Computer Science and Engineering Computer Science and Engineering State University of New York at Buffalo State University of New York at Buffalo [email protected] [email protected] Venugopal Govindaraju Computer Science and Engineering State University of New York at Buffalo [email protected]

Abstract 1 Introduction and Motivation Do today’s communication technologies hold potential According to the United Nations Development Pro- to alleviate poverty? The mobile phone’s accessibility gram’s 2014 Human Development Index (HDI), Senegal and use allows us with an unprecedented volume of data is ranked 163 out of 187 countries with an HDI index of on social interactions, mobility and more. Can this data 0.485. HDI measures achievement in three basic dimen- help us better understand, characterize and alleviate sions of human development: health, knowledge, and poverty in one of the poorest nations in the world. Our standard of living. Senegal has a population of 14.1 study is an attempt in this direction. We discuss two million, with 43.1% urban population, and the median concepts, which are both interconnected and immensely age of 18.2 years. It is one of the poorest country in useful for securing the important link between mobile the world, with over 9.2 million people living in multi- accessibility and poverty. dimensional poverty. Wealth distribution in Senegal is First, we use the cellular-communications data to very unequal. construct virtual connectivity maps for Senegal, which Poverty incidence remains high, affecting about are then correlated with the poverty indicators to learn a 47% of the population. There are wide disparities model. Our model predicts poverty index at any spatial between poverty in rural areas (at 57%)s, and urban resolution. Thus, we generate Poverty Maps for Senegal areas, where the poverty rate is 33%. More than 42% at an unprecedented finer resolution. Such maps are of the population lives in rural areas, with a population essential for understanding what characterizes poverty density that varies from 77 people per square kilometer in a certain , and how it differentiates from other to 2 people per square kilometer in the dry of regions, for targeted responses for the demographic of the country. the population that is most needy. An interesting fact, On the other hand, the growth in mobile-cellular that is empirically proved by our methodology, is that technology has been very impressive in recent decades. a large portion of all communication, and economic It is estimated that there are 95 mobile-cellular tele- arXiv:1506.03401v1 [cs.CY] 10 Jun 2015 activity in Senegal is concentrated in , leaving phone subscriptions per 100 inhabitants worldwide [1]. many other regions marginalized. In Senegal, there are 93 mobile phone subscriptions Second, we study how user behavioral statistics, per 100 people, according to the latest world-bank re- gathered from cellular-communications, correlate with port [2]. the poverty indicators. Can this relationship be learnt The power of growth of mobile technology poses as a model to generate poverty maps at a finer resolu- a question: Can their accessibility be used to identify, tion? Surprisingly, this relationship can give us an al- characterize, and, in turn, alleviate poverty? Ours is ternate poverty map, that is solely based on the user a case study towards answering this question. An ex- behavior. Since poverty is a complex phenomenon, pected outcome is a high resolution poverty map of poverty maps showcasing multiple perspectives, such as Senegal, and its poverty analysis, with some recommen- ours, provide policymakers with better insights for ef- dations for effective policies for an inclusive growth. We fective responses for poverty eradication. believe that such poverty a