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Featured researches published by Jameson L. Toole.


knowledge discovery and data mining | 2012

Inferring land use from mobile phone activity

Jameson L. Toole; Michael Ulm; Marta C. González; Dietmar Bauer

Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.


PLOS ONE | 2012

Modeling the adoption of innovations in the presence of geographic and media influences.

Jameson L. Toole; Meeyoung Cha; Marta C. González

While there is a large body of work examining the effects of social network structure on innovation adoption, models to date have lacked considerations of real geography or mass media. In this article, we show these features are crucial to making more accurate predictions of a social contagion and technology adoption at a city-to-city scale. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homophily both among individuals with similar propensities to adopt a technology and geographic location is critical to reproducing features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitters user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves.


Journal of the Royal Society Interface | 2015

Tracking employment shocks using mobile phone data

Jameson L. Toole; Yu-Ru Lin; Erich Muehlegger; Daniel Shoag; Marta C. González; David Lazer

Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behaviour following the plants closure. For these affected individuals, we observe significant declines in social behaviour and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviours, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure microeconomic behaviour and improve estimates of critical economic indicators.


ACM Transactions on Intelligent Systems and Technology | 2011

Spatiotemporal correlations in criminal offense records

Jameson L. Toole; Nathan Eagle; Joshua B. Plotkin

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.


Archive | 2015

Modeling and Understanding Intrinsic Characteristics of Human Mobility

Jameson L. Toole; Yves-Alexandre de Montjoye; Marta C. González; Alex Pentland

Humans are intrinsically social creatures and our mobility is central to understanding how our societies grow and function. Movement allows us to congregate with our peers, access things we need, and exchange information. Human mobility has huge impacts on topics like urban and transportation planning, social and biologic spreading, and economic outcomes. Modeling these processes has however been hindered so far by a lack of data. This is radically changing with the rise of ubiquitous devices. In this chapter, we discuss recent progress deriving insights from the massive, high resolution data sets collected from mobile phone and other devices. We begin with individual mobility, where empirical evidence and statistical models have shown important intrinsic and universal characteristics about our movement: we as human are fundamentally slow to explore new places, relatively predictable, and mostly unique. We then explore methods of modeling aggregate movement of people from place to place and discuss how these estimates can be used to understand and optimize transportation infrastructure. Finally, we highlight applications of these findings to the dynamics of disease spread, social networks, and economic outcomes.


Transportation Research Part C-emerging Technologies | 2015

The path most traveled: Travel demand estimation using big data resources

Jameson L. Toole; Serdar Çolak; Bradley Sturt; Lauren P. Alexander; Alexandre Evsukoff; Marta C. González


Journal of the Royal Society Interface | 2015

Coupling human mobility and social ties

Jameson L. Toole; Carlos Herrera-Yaqüe; Christian Schneider; Marta C. González


Political Analysis | 2012

Revised-Path Dependence

Jenna Bednar; Scott E. Page; Jameson L. Toole


arXiv: Physics and Society | 2014

The path most travelled: Mining road usage patterns from massive call data.

Jameson L. Toole; Serdar Çolak; Fahad Alhasoun; Alexandre Evsukoff; Marta C. González


Journal of the Royal Society Interface | 2016

Data analytics for simplifying thermal efficiency planning in cities

Mohammad Javad Abdolhosseini Qomi; Arash Noshadravan; Jake M. Sobstyl; Jameson L. Toole; Joseph Ferreira; Roland J.-M. Pellenq; Franz-Josef Ulm; Marta C. González

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Marta C. González

Massachusetts Institute of Technology

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Joshua B. Plotkin

University of Pennsylvania

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Serdar Çolak

Massachusetts Institute of Technology

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Alexandre Evsukoff

Federal University of Rio de Janeiro

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Alex Pentland

Massachusetts Institute of Technology

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Bradley Sturt

Massachusetts Institute of Technology

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