Urban characteristics attributable to density-driven tie formation

Pan, Wei; Ghoshal, Gourab; Krumme, Coco; Cebrián, Manuel; Pentland, Alex

doi:10.1038/ncomms2961

Cited by 189 publications

(185 citation statements)

References 36 publications

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“…These include many creative productivities and infrastructure volumes [14][15][16][17] such as the gross domestic product (GDP), the number of patents, human online activities [18], prosocial behaviors [19], quantities related to highway systems [20], the number of crimes [21], the number of supply stations [22], and emissions of CO 2 [23] and NO 2 [24]. There exist several models explaining such allometric scaling behavior [25][26][27][28] and fluctuations around average power-law relations [29][30][31]. These findings allow us to write an urban indicator Y as a function of the population size N as…”

Section: Introductionmentioning

confidence: 99%

Superlinear and sublinear urban scaling in geographical networks modeling cities

Yakubo

Saijo²,

Korošak

2014

Phys. Rev. E

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Using a geographical scale-free network to describe relations between people in a city, we explain both superlinear and sublinear allometric scaling of urban indicators that quantify activities or performances of the city. The urban indicator Y (N ) of a city with the population size N is analytically calculated by summing up all individual activities produced by person-to-person relationships. Our results show that the urban indicator scales superlinearly with the population, namely, Y (N ) ∝ N β with β > 1, if Y (N ) represents a creative productivity and the indicator scales sublinearly (β < 1) if Y (N ) is related to the degree of infrastructure development. These results coincide with allometric scaling observed in real-world urban indicators. We also show how the scaling exponent β depends on the strength of the geographical constraint in the network formation.

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Section: Introductionmentioning

confidence: 99%

Superlinear and sublinear urban scaling in geographical networks modeling cities

Yakubo

Saijo²,

Korošak

2014

Phys. Rev. E

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“…The aggregated networks each have a density of 1, that is, the networks are completely connected with every region having some level of communication with every other region. This high level of connectivity makes these kind of networks unlike the majority of networks studied in relation to information diffusion [8], [42], [49]- [52].…”

Section: Network Structurementioning

confidence: 99%

“…Relatedly, Pan et al [8] hypothesise that the superlinear scaling of urban characteristics such as wealth and rate of innovation with population size can be attributed to social tie density, which in turn enables increased flow of information through the population. To test this, they simulated information flow using the SI model in synthetic city-wide social networks and, in support of their hypothesis, found that diffusion rates also scale super-linearly with population size.…”

Section: B Information Diffusionmentioning

confidence: 99%

“…Many characteristics of urban areas have been found to scale super linearly with population size, including crime levels, the spread of infectious diseases, and also economic development as measured by Gross Domestic Product [4]- [6]. This relationship has been attributed to denser social network formation since social networks tend to densify as the number of individuals increases, which in turn increases flow of information [7], [8]. It has further been shown that having a diverse set of contacts is strongly related to living in a less deprived neighbourhood [9].…”

Section: Introductionmentioning

confidence: 99%

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Information Diffusion and Economic Development

Smith-Clarke

Street

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Abstract-In many developing countries there remains a limited view on the socioeconomic status of the population, owing to the high cost associated with detailed and comprehensive surveying. This situation has encouraged a number of researchers to attempt to exploit alternative sources of data in order to derive estimates, including mobile phone data, which offers a rich depiction of the social dynamics of a population. Meanwhile, from the level of the individual to the city, access to information has been posited as an important factor in determining prosperity and economic development. In this paper we explore this relationship by simulating the flow of information through a mobile phone call graph in two sub-Saharan countries. We find a strong relationship between a location's average wealth and its access to information as determined by the simulations in one country, and a weaker correlation in the second country. This finding adds to recent evidence that mining patterns from mobile phone data represents a viable means to estimate poverty in places where traditionally derived estimates are lacking. We further investigate the impact of various factors on the empirical results in order to explain the variation between the two countries.

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“…Pentland and a team of colleagues investigated this phenomenon with a computer model that simulated social ties in virtual cities of from 10 thousand to 10 million residents (6). They found that, as the population density grew, the number of interactions each individual could have increased by an exponential factor.…”

Section: Society In High Resolutionmentioning

confidence: 99%

Computational social science

Mann¹

2016

Proc. Natl. Acad. Sci. U.S.A.

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Cell phone tower data predicts which parts of London can expect a spike in crime (1). Google searches for polling place information on the day of an election reveal the consequences of different voter registration laws (2). Mathematical models explain how interactions among financial investors produce better yields, and even how they generate economic bubbles (3).These are just a few examples of how a suite of technologies is helping bring sociology, political science, and economics into the digital age. Such social science fields have historically relied on interviews and survey data, as well as censuses and other government databases, to answer important questions about human behavior. These tools often produce results based on individuals-showing, for example, that a wealthy, well-educated, white person is statistically more likely to vote (4)-but struggle to deal with complex situations involving the interactions of many different people.A growing field called "computational social science" is now using digital tools to analyze the rich and interactive lives we lead. The discipline uses powerful computer simulations of networks, data collected from cell phones and online social networks, and online experiments involving hundreds of thousands of individuals to answer questions that were previously impossible to investigate. Humans are fundamentally social creatures and these new tools and huge datasets are giving social scientists insights into exactly how connections among people create societal trends or heretofore undetected patterns, related to everything from crime to economic fortunes to political persuasions. Although the field provides powerful ways to study the world, it's an ongoing challenge to ensure Using cell-phone and taxi GPS data, researchers classified people in San Francisco into "tribal networks," clustering them according to their behavioral patterns. Student's, tourists, and businesspeople all travel through the city in various ways, congregating and socializing in different neighborhoods. Image courtesy of Alex Pentland (Massachusetts Institute of Technology, Cambridge, MA).

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Urban characteristics attributable to density-driven tie formation

Cited by 189 publications

References 36 publications

Superlinear and sublinear urban scaling in geographical networks modeling cities

Superlinear and sublinear urban scaling in geographical networks modeling cities

Information Diffusion and Economic Development

Computational social science

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