Research Projects

Dynamic Population Mapping Using Mobile Phone Data

We demonstrate how large-scale data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. The methods outlined may be applied to estimate human population densities in low-income countries where data on population distributions may be scarce, outdated, and unreliable, or to estimate temporal variations in population density. The work highlights how facilitating access to anonymized mobile phone data might enable fast and cheap production of population maps in emergency and data-scarce situations.

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Connecting Human Mobility And Social Interactions

Both our mobility and communication patterns obey spatial constraints: Most of the time, our trips or communications occur over a short distance, and occasionally, we take longer trips or call a friend who lives far away. These spatial dependencies play a consequential role in broad areas ranging from how an epidemic spreads to diffusion of ideas and information. By mining massive cell phone datasets from different countries, we established the first formal link, to our knowledge, between mobility and communication patterns. The uncovered scaling theory not only allows us to derive human movements from communication volumes, or vice versa, but it also documents a new degree of regularity that helps deepen our quantitative understanding of human behavior.

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Data for Development: the D4D Challenge on Mobile Phone Data

The Data for Development (D4D) challenge is a challenge that was organized with data and support from the telecom operator Orange and with the collaboration of partners at MIT, at the World Economic Forum, at Global Pulse (United Nations) and with GSMA. The challenge is an open data challenge, encouraging research teams around the world to use four datasets of 2.5 billion anonymous call patterns of Orange's Ivory Coast subsidiary, to help address society development questions in novel ways. 150 research groups have participated in the first challenge. The results of the challenge have been announced at a special session of the NetMob2013 conference at MIT in May 2013. Due to its success, another D4D Challenge has been organized in 2015 for Senegal.

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Voice on the Border: Do Cellphones Redraw the Maps ?

We describe a network constructed from anonymized mobile phone communications between 17 million mobile phone users in France over a period of 5 months. We use this network and a community detection method to automatically identify cohesive communities in France. The identified communities are contiguous and interestingly coincide with the administrative regions in France. We further analyze the stability of the communities by modifying the detection algorithm and the time frame used for the detection.

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Quantifying the Evolution of Individual Scientific Impact

For this project, we analyzed the publications of thousands of physicists, as well as data on thousands of scientists publishing in a variety of fields. When productivity is accounted for, the paper with the greatest impact occurs randomly in a scientist's career. However, the process of generating a high-impact paper is not an entirely random one. We developed a quantitative model of impact, based on an element of randomness, productivity, and a factor Q that is particular to each scientist and remains constant during the scientist's career.

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A Century of Physics: a Big Data Approach

An analysis of millions of documents from Web of Science spanning more than 100 years reveals the rapid growth and increasing multidisciplinarity of physics — as well its internal map of subdisciplines.

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  • Sinatra R, Deville P, Szell M, Wang D, Barabási AL. A century of physics. Nature Physics. 2015 Oct 1;11(10):791-6.

Career on the Move: Geography, Stratification, and Scientific Impact

By examining 400,000 scientific papers, we follow the affiliation information therein for individual scientists, allowing us to reconstruct their career trajectories over decades. We find that career movements are not only temporally and spatially localized, but also characterized by a high degree of stratification in institutional ranking. When cross-group movement occurs, we find that whereas going from elite to lower-rank institutes on average associates with modest decrease in scientific performance, transitioning into elite institutions does not imply any consequent gain in performance. These results present among the first few empirical evidence on institutional level career choices and movements and have potential implications for recruiting policy.

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