Fundamental structures of dynamic social networks

Social systems are in a constant state of flux with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding spreading of influence or diseases, formation of friendships, and the productivity of teams. While there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the micro-dynamics of social networks. Here we explore the dynamic social network of a densely-connected population of approximately 1000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geo-location, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-minute time slices we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores are preceded by coordination behavior in the communication networks, and demonstrating that social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39 pages, 34 figure

Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales

We use a Bayesian method, optimal interpolation, to improve satellite derived irradiance estimates at city-scales using ground sensor data. Optimal interpolation requires error covariances in the satellite estimates and ground data, which define how information from the sensor locations is distributed across a large area. We describe three methods to choose such covariances, including a covariance parameterization that depends on the relative cloudiness between locations. Results are computed with ground data from 22 sensors over a 75×80 km area centered on Tucson, AZ, using two satellite derived irradiance models. The improvements in standard error metrics for both satellite models indicate that our approach is applicable to additional satellite derived irradiance models. We also show that optimal interpolation can nearly eliminate mean bias error and improve the root mean squared error by 50%

Little emperors in the UK: Acculturation and food over time

This is the post-print version of the final paper published in Journal of Business Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.This paper investigates the acculturation process of a group of Chinese students living in the UK. It emerges from a longitudinal study looking at how participants' social ties affect their food consumption. Drafting from an interpretive study using focus groups discussions, it shows that participants' food consumption patterns change over time in relation to participants' social ties. Three acculturation phases have been individuated. They show that ethnic and non-ethnic ties influence participants' acculturation process. Students with strong ethnic ties consume Chinese food for maintaining their ethnic identity and resisting host food culture. Students with weak ethnic ties consume Chinese food to maintain their ethnic identity and global consumer culture food to resist host food culture. Participants with strong non-ethnic ties have a wider knowledge of host food culture, but they do not consume it more than students with weak non-ethnic ties

Teaching and Statistical Training

The availability of well-educated researchers is necessary for the fruitful analysis of social and economic data. The increased data offer made possible by the creation of the Research Data Centers (RDCs) has resulted in an increased demand for PhD students at the master’s or Diplom levels. Especially in economics, where we find intense competition among the various individual subjects within the course of study, survey statistics has not been very successful in laying claim to a substantial proportion of the coursework and training. The situation is more favorable in sociology faculties. This article argues that the creation of new CAMPUS Files would help foster statistical education by providing public use files covering a wider range of subjects. It also presents some suggestions for new CAMPUS Files along these lines. Additionally, it argues for the establishment of master’s programs in survey statistics to increase the availability of well-trained statisticians. An outline of such a master’s program is presented and current PhD programs are evaluated with respect to training in survey statistics. Training courses are also offered outside the university that promote the use of new data sets as well as expanding the knowledge of new statistical methods or methods that lie outside standard education. These training courses are organized by the RDCs, (i.e. the data producers), the Data Service Centers, or by GESIS (Leibniz Institute for the Social Sciences). The current tendency to strengthen ties and collaborate with universities should be supported by making it possible to earn academic credit for such courses.master’s programs, survey statistics, campus files, statistical training

Enhancing learning with authoritative actions: Reflective practice of positive power

Drawing from classic power perspective, my reflective practice illuminates how power action, traditionally recognized as negative and detrimental to teaching process and learning outcomes, could be shaped in a positive way to enhance learning. Insights gained from this action research set in a politically charged and culturally homogenous environment provide critical perspective to the research community and challenge traditional practices of teaching and learning. Implications gained call for attention to critical perspective of empirical studies that could provide lessons for educators and researchers to create a more effective teaching and learning environment with authoritative power. An action framework is created in the end to illustrate how the positive authoritative process can be achieved

PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

Why Performance-Based College Funding Doesn't Work

For the better part of the past century, elected officials have sought ways to improve the performance of public sector operations, such as fire departments, libraries, health clinics, job training programs, elementary schools, and traffic safety. Interest in performance management has only grown over time, to the point today that it is nearly impossible to talk about government finance without also talking about performance. The idea of attempting to measure outcomes and paying for those results is compelling because of its simple logic. Proponents believe setting clear performance goals and tying funding to them will create incentives for public organizations to operate more efficiently and effectively, ultimately resulting in better delivery of public services. Fire departments, they reason, should not be funded according to the number of engines they own, but according to the number of fires they put out. Hospitals should be funded not by the number of patients admitted, but by the health outcomes of their patients. Schools should not be funded by the number of teachers they employ, but by each teacher's contribution to student learning.In recent years, advocates seeking to increase the number of college graduates in the United States have promoted the idea that states should finance their public universities using a performance-based model. Supporters of the concept believe that the $75 billion states invest in public higher education each year will not be spent efficiently or effectively if it is based on enrollment or other input measures, because colleges have little financial incentive to organize their operations around supporting students to graduation. When states shift to performance-based funding, it is hoped, colleges will adopt innovative practices that improve student persistence in college. The appeal of performance-based funding is "intuitive," its proponents argue, "based on the logical belief that tying some funding dollars to results will provide an incentive to pursue those results."However, while pay-for-performance is a compelling concept in theory, it has consistently failed to bear fruit in actual implementation, whether in the higher education context or in other public services