Nathan Eagle

Inferring friendship network structure by using mobile phone data

Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.

Network Diversity and Economic Development

Network for Recovery A long-standing theory suggests that social diversity leads to economic development. By combining the United Kingdoms telephone communication records (both landline and mobile) with information on regional economic conditions, Eagle et al. (p. 1029) demonstrate that network diversity alone accounts for over three-quarters of the variance of a regions economic status. Although the data cannot be used to show causality, the association suggests that economic development and recovery may depend not solely on monetary stimulus but also on the development of a nations social infrastructure. Social diversity is associated with economic development. Social networks form the backbone of social and economic life. Until recently, however, data have not been available to study the social impact of a national network structure. To that end, we combined the most complete record of a national communication network with national census data on the socioeconomic well-being of communities. These data make possible a population-level investigation of the relation between the structure of social networks and access to socioeconomic opportunity. We find that the diversity of individuals’ relationships is strongly correlated with the economic development of communities.

Quantifying the impact of human mobility on malaria

Mobile Phone “Hot Spots” An obstacle to developing effective national malaria control programs is a lack of understanding of human movements, which are an important component of disease transmission. As mobile phones have become increasingly ubiquitous, it is now possible to collect individual-level, longitudinal data on human movements on a massive scale. Wesolowski et al. (p. 267) analyzed mobile phone call data records representing the travel patterns of 15 million mobile phone owners in Kenya over the course of a year. This was combined with a detailed malaria risk map, to estimate malaria parasite movements across the country that could be caused by human movement. This information enabled detailed analysis of parasite sources and sinks between hundreds of local settlements. Estimates were compared with hospital data from Nairobi to show that local pockets of transmission likely occur around the periphery of Nairobi, accounting for locally acquired cases, contrary to the accepted idea that there is no transmission in the capital. Geographical information in mobile phone records for 15 million Kenyans is linked to malaria prevalence estimates. Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.

Smartphones An Emerging Tool for Social Scientists

Recent developments in mobile technologies have produced a new kind of device: a programmable mobile phone, the smartphone. In this article, the authors argue that the technological and social characteristics of this device make it a useful tool in social sciences, particularly sociology, social psychology, urban studies, technology assessment, and media studies. The device is willingly carried by a large fraction of people in developed countries, integrates a number of technologies for automatic observation, can be programmed to interact with the user, and can communicate with remote researchers. This allows unobtrusive and cost-effective access to previously inaccessible sources of data on everyday social behavior, such as physical proximity of people, phone calls, and patterns of movement. The authors describe three studies in human behavior that have augmented existing methods with the smartphone, two of which the authors conducted themselves. Based on their experience, the authors critically evaluate the improvements and threats to validity and reliability of smartphone-augmented methods. These approaches are rapidly becoming feasible for the social scientist, since research software for smartphones have been published in open source, which lowers the technical and economic investment needed for their utilization in research.Recent developments in mobile technologies have produced a new kind of device: a programmable mobile phone, the smartphone. In this article, the authors argue that the technological and social char...

txteagle: Mobile Crowdsourcing

We present txteagle, a system that enables people to earn small amounts of money by completing simple tasks on their mobile phone for corporations who pay them in either airtime or MPESA (mobile money). The system is currently being launched in Kenya and Rwanda in collaboration with the mobile phone service providers Safaricom and MTN Rwanda. Tasks include translation, transcription, and surveys. User studies in Nairobi involving high school students, taxi drivers, and local security guards have been completed and the service has recently launched in Kenya nationwide.

Heterogeneous Mobile Phone Ownership and Usage Patterns in Kenya

The rapid adoption of mobile phone technologies in Africa is offering exciting opportunities for engaging with high-risk populations through mHealth programs, and the vast volumes of behavioral data being generated as people use their phones provide valuable data about human behavioral dynamics in these regions. Taking advantage of these opportunities requires an understanding of the penetration of mobile phones and phone usage patterns across the continent, but very little is known about the social and geographical heterogeneities in mobile phone ownership among African populations. Here, we analyze a survey of mobile phone ownership and usage across Kenya in 2009 and show that distinct regional, gender-related, and socioeconomic variations exist, with particularly low ownership among rural communities and poor people. We also examine patterns of phone sharing and highlight the contrasting relationships between ownership and sharing in different parts of the country. This heterogeneous penetration of mobile phones has important implications for the use of mobile technologies as a source of population data and as a public health tool in sub-Saharan Africa.

The impact of biases in mobile phone ownership on estimates of human mobility

Mobile phone data are increasingly being used to quantify the movements of human populations for a wide range of social, scientific and public health research. However, making population-level inferences using these data is complicated by differential ownership of phones among different demographic groups that may exhibit variable mobility. Here, we quantify the effects of ownership bias on mobility estimates by coupling two data sources from the same country during the same time frame. We analyse mobility patterns from one of the largest mobile phone datasets studied, representing the daily movements of nearly 15 million individuals in Kenya over the course of a year. We couple this analysis with the results from a survey of socioeconomic status, mobile phone ownership and usage patterns across the country, providing regional estimates of population distributions of income, reported airtime expenditure and actual airtime expenditure across the country. We match the two data sources and show that mobility estimates are surprisingly robust to the substantial biases in phone ownership across different geographical and socioeconomic groups.

Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda

We combine data from a field survey with transaction log data from a mobile phone operator to provide new insight into daily patterns of mobile phone use in Rwanda. The analysis is divided into three parts. First, we present a statistical comparison of the general Rwandan population to the population of mobile phone owners in Rwanda. We find that phone owners are considerably wealthier, better educated, and more predominantly male than the general population. Second, we analyze patterns of phone use and access, based on self-reported survey data. We note statistically significant differences by gender; for instance, women are more likely to use shared phones than men. Third, we perform a quantitative analysis of calling patterns and social network structure using mobile operator billing logs. By these measures, the differences between men and women are more modest, but we observe vast differences in utilization between the relatively rich and the relatively poor. Taken together, the evidence in this paper suggests that phones are disproportionately owned and used by the privileged strata of Rwandan society.

Human dynamics: computation for organizations

The human dynamics group at the MIT Media Laboratory proposes that active pattern analysis of face-to-face interactions within the workplace can radically improve the functioning of the organization. There are several different types of information inherent in such conversations: interaction features, participants, context, and content. By aggregating this information, high-potential collaborations and expertise within the organization can be identified, and information efficiently distributed. Examples of using wearable machine perception to characterize face-to-face interactions and using the results to initiate productive connections are described, and privacy concerns are addressed.

Community Computing: Comparisons between Rural and Urban Societies Using Mobile Phone Data

We present a comparative analysis of the behavioral dynamics of rural and urban societies using four years of mobile phone data from all 1.4M subscribers within a small country. We use information from communication logs and top up denominations to characterize attributes such as socioeconomic status and region. We show that rural and urban communities differ dramatically not only in terms of personal network topologies, but also in terms of inferred behavioral characteristics such as travel. We confirm the hypothesis for behavioral adaptation, demonstrating that individuals change their patterns of communication to increase the similarity with their new social environment. To our knowledge, this is the first comprehensive comparison between regional groups of this size.