The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

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Understanding individual human mobility patterns

We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.

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Reality mining: sensing complex social systems

Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

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Activity recognition using cell phone accelerometers

Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

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A survey of mobile phone sensing

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

Addressing digital security and privacy issues can be particularly difficult for users who face challenging circumstances. We performed semi-structured interviews with residents and staff at 4 transitional homeless shelters in the U.S. San Francisco Bay Area (n=15 residents, 3 staff) to explore their digital security and privacy challenges. Based on these interviews, we outline four tough times themes --- challenges experienced by our financially insecure participants that impacted their digital security and privacy --- which included: (1) limited financial resources, (2) limited access to reliable devices and Internet, (3) untrusted relationships, and (4) ongoing stress. We provide examples of how each theme impacts digital security and privacy practices and needs. We then use these themes to provide a framework outlining opportunities for technology creators to better support users facing security and privacy challenges related to financial insecurity.

https://dl.acm.org/doi/pdf/10.1145/3290605.3300319

Tough Times at Transitional Homeless Shelters: Considering the Impact of Financial Insecurity on Digital Security and Privacy

Many have enthusiastically greeted the ability to search and view public records online as a great advance for transparency and accountability. Such ability, however, also creates value tensions with privacy and other important human values. In this paper, we report findings from a survey of 134 residents of the US Pacific Northwest on their awareness of and attitudes towards online access to political campaign records and real estate transaction histories, bringing to light some of the social implications of technological changes that increase ease of access to public records. We show that, while respondents often understood the reason behind making these records public, considerable concern about the current accessibility of these records exists, along with a precautionary indication that such open access may reduce public participation for some individuals.

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Attitudes toward online availability of US public records

This book will give you a practical overview of several methods and approaches for designing mobile technologies and conducting mobile user research, including how to understand behavior and evaluate how such technologies are being (or may be) used out in the world. Each chapter includes case studies from our own work and highlights advantages, limitations, and very practical steps that should be taken to increase the validity of the studies you conduct and the data you collect. This book is intended as a practical guide for conducting mobile research focused on the user and their experience. We hope that the depth and breadth of case studies presented, as well as specific best practices, will help you to design the best technologies possible and choose appropriate methods to gather ethical, reliable, and generalizable data to explore the use of mobile technologies out in the world.

Mobile User Research: A Practical Guide

There is a pressing need for an effective means of transforming health behaviors. Obesity, anxiety and other behavior-generated health conditions result in billions of dollars of healthcare costs and a great deal of human suffering. Technology innovations are rapidly presenting new delivery mechanisms that can be harnessed to influence health behaviors.The mobile platform is a uniquely persuasive delivery medium with ever increasing reach. Mobile phones are intimate, responsive, and serve as our constant companions and guides. The ability to reach people whenever and wherever they are provides a capability to persuade and influence behavioral decisions in real time that never existed before.Effective health interventions must be built on a foundation of persuasive design principles and behavior change theory. Important traditional factors for effective behavior change include setting clear goals, tracking progress, getting reminders and social support, among others.In this panel, we'll explore the use of mobile technology for reminders, repetition and reinforcement and how contextawareness contributes to effectiveness. We'll discuss goal-setting theory and how the mobile platform contributes to creating and engaging in intentions. We'll look at how location-awareness creates unique opportunities for social support of new behaviors, providing more subtle influences on frequent decisive moments. We'll describe mobile tools for tracking and logging behavior and how they provide awareness and insight. Lastly, we'll discuss the near future and our predictions for this incredible platform to create impact and make positive change in our collective health.

Designing for persuasion: mobile services for health behavior change

Content creators—social media personalities with large audiences on platforms like Instagram, TikTok, and YouTube—face a heightened risk of online hate and harassment. We surveyed 135 creators to understand their personal experiences with attacks (including toxic comments, impersonation, stalking, and more), the coping practices they employ, and gaps they experience with existing solutions (such as moderation or reporting). We find that while a majority of creators view audience interactions favorably, nearly every creator could recall at least one incident of hate and harassment, and attacks are a regular occurrence for one in three creators. As a result of hate and harassment, creators report self-censoring their content and leaving platforms. Through their personal stories, their attitudes towards platform-provided tools, and their strategies for coping with attacks and harms, we inform the broader design space for how to better protect people online from hate and harassment.

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Sunny Consolvo

Papers

Tough Times at Transitional Homeless Shelters: Considering the Impact of Financial Insecurity on Digital Security and Privacy

Attitudes toward online availability of US public records

Mobile User Research: A Practical Guide

Designing for persuasion: mobile services for health behavior change

“It’s common and a part of being a content creator”: Understanding How Creators Experience and Cope with Hate and Harassment Online