The Connected States of America: Quantifying Social Radii of Influence

Calabrese, Francesco; Dahlem, Dominik; Gerber, Alexandre; Paul, DeDe; Chen, Xiaoji; Rowland, James; Rath, Christopher; Ratti, Carlo

doi:10.1109/passat/socialcom.2011.247

Cited by 29 publications

(33 citation statements)

References 22 publications

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“…Thus the density of social ties formed between individuals grows as TðrÞ $ r ln r, a super-linear scaling consistent with the observations made by Calabrese et al 35 (also discussed in the content below). We argue that T(r) to a first approximation is the individual dyadic-level ingredient behind the empirically observed growth of city indicators.…”

Section: Resultssupporting

confidence: 88%

“…Based on this, we have described an empirically grounded generative model of social-tie density to account for the observed scaling behaviour of city indicators as a function of population density. Our model accurately explains how urban density drives the super-linear growth of social interaction density 35 , and eventually the super-linear growth of productivity as observed in many empirical data sets.…”

Section: Discussionsupporting

confidence: 57%

“…Recent work 35 shows a super-linear relationship between calling volume (time) and population across different counties in the United States. As Fig.…”

Section: Resultsmentioning

confidence: 99%

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Urban characteristics attributable to density-driven tie formation

et al. 2013

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Motivated by empirical evidence on the interplay between geography, population density and societal interaction, we propose a generative process for the evolution of social structure in cities. Our analytical and simulation results predict both super-linear scaling of social-tie density and information contagion as a function of the population. Here we demonstrate that our model provides a robust and accurate fit for the dependency of city characteristics with city-size, ranging from individual-level dyadic interactions (number of acquaintances, volume of communication) to population level variables (contagious disease rates, patenting activity, economic productivity and crime) without the need to appeal to heterogeneity, modularity, specialization or hierarchy.

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Section: Resultssupporting

confidence: 88%

Section: Discussionsupporting

confidence: 57%

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Urban characteristics attributable to density-driven tie formation

et al. 2013

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“…Hence, standard community detection methods (typically based on the NG null model) will discover communities of nodes that are spatially close, as opposed to communities that have particularly strong internal interactions [29,13,4,2,20]. To address this, Expert et al [21] proposed an alternative null model for P ij that takes into account the effect of space by favouring communities of nodes i and j that are more connected than expected, given the physical distance d ij between them:…”

Section: Community Structurementioning

confidence: 99%

Community Structure Detection in the Evolution of the United States Airport Network

Gegov

Postorino

Atherton

et al. 2013

Advs. Complex Syst.

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pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people travelling are both studied in detail, revealing high heterogeneity in space and time. A recently-developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, 2 Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration.

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“…A mobile phone, by its nature, contains interpersonal data, including phone call logs, text messages, and contacts. This is valuable information for understanding interpersonal behavior (Calabrese et al, 2011). Even more, a smartphone can provide surrounding information collected by mobile sensors; mobile sensing data include location or speed (Gonzalez, Hidalgo, & Barabasi, 2008;Song, Qu, Blumm, & Barabási, 2010).…”

Section: Background Smartphones As Data Collection Toolsmentioning

confidence: 99%

Localized Analysis Framework using Smartphones

Kim

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Mobile phones have become an intrinsic part of our daily lives. Any data collected from them is especially reflective of our day-to-day reality. By using smartphone apps, researchers can now observe behavioral patterns in mobile data, all without soliciting any responses or relying on self-reports. Such behavioral data includes the frequency and length of phone calls, the contents of text messages, and users' physical locations. However, this immediately brings up privacy issues. Many people do not want others to harvest their mobile phone's private data. To utilize mobile data without invading privacy, I propose a localized analysis framework using smartphones. In this approach, researchers do not directly collect raw data. Instead, software on each individual's smartphone accesses the raw data locally stored in the device, and only the processed results are sent to the researchers.Researchers then analyze the aggregated individual results and find meanings at the whole sample level.The current study aims to demonstrate utilities of the proposed localized analysis framework using smartphones with an emphasis on exploratory analysis using machine learning. While there are many possible methods to employ within this framework, in this study I adopt support vector machines (SVMs) for localized analysis at an individual level and visually inspect the aggregated patterns at the whole whole sample level. The simulation study results suggest that the proposed framework can perform as well as a conventional data collection and analysis paradigm. In this study, the aggregation of multiple individual models recovered the underlying true patterns as well as the single model built iii with the whole data. The localized analysis framework opens a new way of treating data to protect user privacy. iv

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The Connected States of America: Quantifying Social Radii of Influence

Cited by 29 publications

References 22 publications

Urban characteristics attributable to density-driven tie formation

Urban characteristics attributable to density-driven tie formation

Community Structure Detection in the Evolution of the United States Airport Network

Localized Analysis Framework using Smartphones

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