432 research outputs found
COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour
Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restrictiveness of cities' mobility quotas (the share of residents allowed out daily according to policy advice) does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with more formalized and complex industrial structure experienced greater reductions in mobility. Within cities, wealthier residents are more likely to reduce mobility, and commuters are especially more likely to stay at home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities' employment characteristics and work-from-home capabilities are the primary determinants of mobility reduction. This finding underscores the need for mitigations aimed at lower income/informal workers, and sheds light on critical dependencies between socio-economic classes in Latin American cities
High resolution dynamical mapping of social interactions with active RFID
In this paper we present an experimental framework to gather data on
face-to-face social interactions between individuals, with a high spatial and
temporal resolution. We use active Radio Frequency Identification (RFID)
devices that assess contacts with one another by exchanging low-power radio
packets. When individuals wear the beacons as a badge, a persistent radio
contact between the RFID devices can be used as a proxy for a social
interaction between individuals. We present the results of a pilot study
recently performed during a conference, and a subsequent preliminary data
analysis, that provides an assessment of our method and highlights its
versatility and applicability in many areas concerned with human dynamics
Relaxed 2-D Principal Component Analysis by Norm for Face Recognition
A relaxed two dimensional principal component analysis (R2DPCA) approach is
proposed for face recognition. Different to the 2DPCA, 2DPCA- and G2DPCA,
the R2DPCA utilizes the label information (if known) of training samples to
calculate a relaxation vector and presents a weight to each subset of training
data. A new relaxed scatter matrix is defined and the computed projection axes
are able to increase the accuracy of face recognition. The optimal -norms
are selected in a reasonable range. Numerical experiments on practical face
databased indicate that the R2DPCA has high generalization ability and can
achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure
Modelling Realistic User Behaviour in Information Systems Simulations as Fuzzing Aspects
In this paper we contend that the engineering of information systems is hampered by a paucity of tools to tractably model, simulate and predict the impact of realistic user behaviours on the emergent properties of the wider socio-technical system, evidenced by the plethora of case studies of system failure in the literature. We address this gap by presenting a novel approach that models ideal user behaviour as workflows, and introduces irregularities in that behaviour as aspects which fuzz the model. We demonstrate the success of this approach through a case study of software development workflows, showing that the introduction of realistic user behaviour to idealised workflows better simulates outcomes reported in the empirical software engineering literature
Participation and satisfaction after spinal cord injury: results of a vocational and leisure outcome study
Study design: Survey. Objectives: Insight in (1) the changes in participation in vocational and leisure activities and (2) satisfaction with the current participation level of people with spinal cord injuries (SCIs) after reintegration in society. Design: Descriptive analysis of data from a questionnaire. Setting: Rehabilitation centre with special department for patients with SCIs, Groningen, The Netherlands. Subjects: A total of 57 patients with traumatic SCI living in the community, who were admitted to the rehabilitation centre two to 12 years before the current assessment. Main outcome measures: Changes in participation in activities; current life satisfaction; support and unmet needs. Results: Participation expressed in terms of hours spent on vocational and leisure activities changed to a great extent after the SCI. This was mainly determined by a large reduction of hours spent on paid work. While 60% of the respondents successfully reintegrated in work, many changes took place in the type and extent of the job. Loss of work was partially compensated with domestic and leisure activities. Sports activities were reduced substantially. The change in participation level and compensation for the lost working hours was not significantly associated with the level of SCI-specific health problems and disabilities. As was found in other studies, most respondents were satisfied with their lives. Determinants of a negative life satisfaction several years following SCI were not easily indicated. Reduced quality of life was particularly related to an unsatisfactory work and leisure situation. Conclusions: Most people with SCI in this study group were able to resume work and were satisfied with their work and leisure situation
Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas
Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic’s first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies. Copyright © 2022 the Author(s
Change in BMI Accurately Predicted by Social Exposure to Acquaintances
Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data.
We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R[superscript 2].
This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI.
This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.MIT Masdar ProgramMIT Media Lab Consortiu
Recognition of Crowd Behavior from Mobile Sensors with Pattern Analysis and Graph Clustering Methods
Mobile on-body sensing has distinct advantages for the analysis and
understanding of crowd dynamics: sensing is not geographically restricted to a
specific instrumented area, mobile phones offer on-body sensing and they are
already deployed on a large scale, and the rich sets of sensors they contain
allows one to characterize the behavior of users through pattern recognition
techniques.
In this paper we present a methodological framework for the machine
recognition of crowd behavior from on-body sensors, such as those in mobile
phones. The recognition of crowd behaviors opens the way to the acquisition of
large-scale datasets for the analysis and understanding of crowd dynamics. It
has also practical safety applications by providing improved crowd situational
awareness in cases of emergency.
The framework comprises: behavioral recognition with the user's mobile
device, pairwise analyses of the activity relatedness of two users, and graph
clustering in order to uncover globally, which users participate in a given
crowd behavior. We illustrate this framework for the identification of groups
of persons walking, using empirically collected data.
We discuss the challenges and research avenues for theoretical and applied
mathematics arising from the mobile sensing of crowd behaviors
- …