The spatial arrangement of urban hubs and centers and how individuals interact with these centers is a crucial problem with many applications ranging from urban planning to epidemiology. We utilize here in an unprecedented manner the large scale, real-time ‘Oyster’ card database of individual person movements in the London subway to reveal the structure and organization of the city. We show that patterns of intraurban movement are strongly heterogeneous in terms of volume, but not in terms of distance travelled, and that there is a polycentric structure composed of large flows organized around a limited number of activity centers. For smaller flows, the pattern of connections becomes richer and more complex and is not strictly hierarchical since it mixes different levels consisting of different orders of magnitude. This new understanding can shed light on the impact of new urban projects on the evolution of the polycentric configuration of a city and the dense structure of its centers and it provides an initial approach to modeling flows in an urban system.

Significance Social scientists have long debated why similar individuals often experience drastically different degrees of success. Some scholars have suggested such inequality merely reflects hard-to-observe personal differences in ability. Others have proposed that one fortunate success may trigger another, thus producing arbitrary differentiation. We conducted randomized experiments through intervention in live social systems to test for success-breeds-success dynamics. Results show that different kinds of success (money, quality ratings, awards, and endorsements) when bestowed upon arbitrarily selected recipients all produced significant improvements in subsequent rates of success as compared with the control group of nonrecipients. However, greater amounts of initial success failed to produce much greater subsequent success, suggesting limits to the distortionary effects of social feedback.

We study the temporal evolution of the structure of the world's largest subway networks in an exploratory manner. We show that, remarkably, all these networks converge to a shape that shares similar generic features despite their geographical and economic differences. This limiting shape is made of a core with branches radiating from it. For most of these networks, the average degree of a node (station) within the core has a value of order 2.5 and the proportion of k ¼ 2 nodes in the core is larger than 60 per cent. The number of branches scales roughly as the square root of the number of stations, the current proportion of branches represents about half of the total number of stations, and the average diameter of branches is about twice the average radial extension of the core. Spatial measures such as the number of stations at a given distance to the barycentre display a first regime which grows as r 2 followed by another regime with different exponents, and eventually saturates. These results-difficult to interpret in the framework of fractal geometry-confirm and yield a natural explanation in the geometric picture of this core and their branches: the first regime corresponds to a uniform core, while the second regime is controlled by the interstation spacing on branches. The apparent convergence towards a unique network shape in the temporal limit suggests the existence of dominant, universal mechanisms governing the evolution of these structures.

Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system. smart cities | transportation | regime change | complex systems W ell-designed transportation systems are a key element in the economic welfare of major cities. Design and planning of these systems requires a quantitative understanding of traffic patterns and relies on the ability to predict the effects of disruptions to such patterns, both planned and unplanned (1).There is a long history of analytic and modeling approaches to the study of traffic patterns (2), for example using simulated scenarios in simple transportation systems (3), and analysis of real traffic data in complex systems, either focusing on a small samples (4) or using more aggregate data (5, 6). Here we take this approach to the next level by making use of smart-card data and incident logs to (i) predict traffic patterns and (ii) estimate the effect of unplanned disruptions on these patterns. We analyzed 70 d of smart-card transactions from the London transportation network, composed of ∼10 million unique IDs and 6 million transactions per day on average, resulting in one of the largest statistical analyses of transportation systems to date.A related literature deals with various aspects of dynamics in complex networks and complex systems in general (7-9), using a variety of data sources, from emails (10) to the circulation of bank notes (11) to online experiments on Amazon Turk (12). More recently, a number of analyses have leveraged mobile phone data as proxies for mobility (4, 13-15).However, smart-card technology allows us to obtain large samples of passenger location and movements without requiring noisy and potentially unreliable proxies such as mobile Global Positioning System traces ...

Organizations mediate societal cultural belief systems and group-level encounters by filtering, and sometimes transforming, social information regarding which status characteristics are salient during group encounters embedded within organizations. This study uses status characteristics theory to add to our understanding of social status within organizations by explaining why organizations matter in determining which status characteristics will be activated within task groups. By analyzing status rankings within an organization of open source software programmers, we find that the organization develops its own unique shared belief system, which inculcates actors with beliefs about status characteristics that are potentially unique within the boundaries of the organization. Specifically, in this study we find that through a process of status generalization, organizational members create new status markers (location) that are potentially only meaningful for the given social situation, and they selectively nullify others (education and age). To the best of our knowledge, the current study is the first work in the expectation states tradition to demonstrate an outcome for an organization-level selection process for status characteristics. This paper adds to status characteristics theory by empirically analyzing how organizational contexts create boundaries around groups in which new and extant status characteristics are activated and in which predefined characteristics inherited from more global, society-level contexts are deactivated.

Summary This study investigates the interaction of motivations among contributors to online crowdfunding campaigns. Based on evidence from the literature on philanthropic behaviour, we argue that funder behaviour is likely to be driven by a combination of intrinsic, extrinsic and image enhancement motivations. We undertake an empirical investigation into the relationships between these factors by analysing data from an online rewards‐based crowdfunding platform. These data not only reveal the monetary values of individual contributions to fundraising campaigns but also indicate particular combinations of motivations based on the material reward selected (if any) and the decision as to whether or not to contribute anonymously. We find that extrinsically motivated funders generally make larger contributions than intrinsically motivated funders, which does not suggest the presence of a ‘crowding‐out’ effect given the presence of material incentives. We further show that named funders with intrinsic motivations contribute more than anonymous funders with intrinsic motivations, whereas the same pattern of behaviour is not observed among extrinsically motivated funders. The evidence from our study therefore suggests that image concerns interact with intrinsic and extrinsic motivations in different ways.

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