Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.

Petabytes of data about human movements, transactions and communication patterns are being generated by everyday technologies such as mobile phones & credit cards. This unprecedented volume of information facilitates a novel set of research questions applicable to a wide range of development issues. In collaboration involving 237 mobile phone operators across 102 countries, Jana's mobile technology platform can instantly poll and compensate 3.48 billion active mobile subscriptions. This talk will discuss how insights gained from living in Kenya became the genesis of a technology company currently working with global clients in over 50 countries, including P&G, Google, Unilever, Danone, General Mills, Nestle, Johnson & Johnson, Microsoft, the World Bank, and the United Nations. After providing an overview of the mobile and social media landscapes in emerging markets, we discuss a system that implements polls & mobile subscription compensation. The presentation will conclude by emphasizing the value of consumer data in underserved and understudied regions of the world.

introduction Introduction to the special section on intelligent systems for socially aware computing Authors: Zhiwen Yu Northwestern Polytechnical University, China Northwestern Polytechnical University, ChinaView Profile , Daqing Zhang Institute Telecom and Management SudPans, France Institute Telecom and Management SudPans, FranceView Profile , Nathan Eagle Media Lab, MIT, USA Media Lab, MIT, USAView Profile , Diane Cook Washington State University, USA Washington State University, USAView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 4Issue 3Article No.: 45pp 1–3https://doi.org/10.1145/2483669.2483678Published:01 July 2013Publication History 25citation183DownloadsMetricsTotal Citations25Total Downloads183Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people's needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller's behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user's behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user's calling behavior and reciprocity. We show that the recent trend of the caller/user's calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user's calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.

With the increased availability of rich behavioral datasets, we present a novel application of tools to analyze this information. Using criminal offense records as an example, we employ cross-correlation measures, eigenvalue spectrum analysis, and results from random matrix theory to identify spatiotemporal patterns on multiple scales. With these techniques, we show that most significant correlation exists on the time scale of weeks and identify clusters of neighborhoods whose crime rates are affected simultaneously by external forces.

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.

Petabytes of data about human movements, transactions, and communication patterns are continuously being generated by everyday technologies such as mobile phones and credit cards. In collaboration with the mobile phone, internet, and credit card industries, Eagle and colleagues are aggregating and analyzing behavioral data from over 250 million people from North and South America, Europe, Asia and Africa. Eagle discusses projects arising from these collaborations that involve inferring behavioral dynamics on a broad spectrum of scales from risky behavior in a group of MIT freshman to population-level behavioral signatures, including cholera outbreaks in Rwanda and wealth in the UK. The research group is developing a range of large-scale network analysis and machine learning algorithms that will provide deeper insight into human behavior.

Mobility path information of cellphone users play a crucial role in a wide range of cellphone applications, including context-based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cellphones and the high level mobility path information required to support these cellphone applications. In this paper, we present formal definitions to capture the cellphone users' mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile user profiles starting from cell based location log data. We use real-world cellphone log data (of over 350 K hours of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cellphone users expose a significant long tail in a user's location-time distribution: A total of 15% of a user's time is spent on average in locations that each appear with less than 1% of time.