Soong Moon Kang

Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows

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.

A long-time limit for world subway networks

We study the temporal evolution of the structure of the worlds 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 = 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 r2 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.

Field experiments of success-breeds-success dynamics

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. Seemingly similar individuals often experience drastically different success trajectories, with some repeatedly failing and others consistently succeeding. One explanation is preexisting variability along unobserved fitness dimensions that is revealed gradually through differential achievement. Alternatively, positive feedback operating on arbitrary initial advantages may increasingly set apart winners from losers, producing runaway inequality. To identify social feedback in human reward systems, we conducted randomized experiments by intervening in live social environments across the domains of funding, status, endorsement, and reputation. In each system we consistently found that early success bestowed upon arbitrarily selected recipients produced significant improvements in subsequent rates of success compared with the control group of nonrecipients. However, success exhibited decreasing marginal returns, with larger initial advantages failing to produce much further differentiation. These findings suggest a lesser degree of vulnerability of reward systems to incidental or fabricated advantages and a more modest role for cumulative advantage in the explanation of social inequality than previously thought.

The Organizational Selection of Status Characteristics: Status Evaluations in an Open Source Community

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.

Equicentrality and network centralization: A micro-macro linkage

Abstract This paper investigates a linkage between micro- and macrostructures as an intrinsic property of social networks. In particular, it examines the linkage between equicentrality [Kang, S.M., 2007. A note on measures of similarity based on centrality. Social Networks 29, 137–142] as a conceptualization of a microstructural process (i.e., the likelihood of social actors to be connected with similarly central others) and network centralization as a macrostructural construct, and shows that they have a negative linear association. In other words, when actors are connected with similarly central alters (i.e., high equicentrality), the overall network centralization is low. Conversely, when highly central actors are connected with low-centrality actors (i.e., low equicentrality), the overall network centralization is high. The relationship between degree equicentrality and degree centralization is more significant in observed networks, especially those evolving over time, as compared to random networks. An application of this property is given by venture capital co-investment networks.

Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

Significance We propose a new approach to analyzing massive transportation systems that leverages traffic information about individual travelers. The goals of the analysis are to quantify the effects of shocks in the system, such as line and station closures, and to predict traffic volumes. We conduct an in-depth statistical analysis of the Transport for London railway traffic system. The proposed methodology is unique in the way that past disruptions are used to predict unseen scenarios, by relying on simple physical assumptions of passenger flow and a system-wide model for origin–destination movement. The method is scalable, more accurate than blackbox approaches, and generalizable to other complex transportation systems. It therefore offers important insights to inform policies on urban transportation. 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.

A note on measures of similarity based on centrality

This note presents a measure of similarity between connected nodes in terms of centrality based on Euclidean distances, and compares it to ‘assortative mixing’ [Newman, M.E.J., 2002. Assortative mixing in networks. Physical Review Letters 89, 208701], which is based on Pearson correlation coefficient. This study suggests that the measure based on Euclidean distances may be more appropriate for relatively smaller (N < 500) and denser networks.

The kindness of strangers? An investigation into the interaction of funder motivations in online crowdfunding campaigns

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.

Subway station real-time indoor positioning system for cell phones

As wireless local area network, WLAN, access point (AP) are becoming very common wireless communication infrastructures in indoor environments, Wi-Fi signal based Indoor Positioning Systems (IPS) have been widely developed in recent years and one of the most popular technologies is the received signal strength (RSS) fingerprinting technology. However, due to large amount of time-consuming work required for offline calibration in large indoor environments, researchers have investigated generating the calibration database while walking about instead of carrying out measurements over a time period at fixed reference points [1]. This paper combines both Wi-Fi fingerprinting and Pedestrian Dead-reckoning (PDR) technologies to introduce a real-time indoor navigation system for large complex three-dimensional indoor environments including a novel calibration method with associated novel matching algorithms. Detailed experiments were conducted in two subway stations with complicated structure under normal operating conditions in which trains regularly arrived and departed and groups of people walked to and from the trains. The results for real cell phone tracking on phones carried by passengers, give a satisfactory error of 2.9 metres during peak congestion times and 1.7 metres when few people were in the station.

Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects

We develop a model that connects the ideas of topic modeling and time series via the construction of topic-sentiment random variables. By doing so, the proposed model provides an easy-to-understand topic-sentiment relationship while also improving the accuracy of regression models on quantitative variables associated with texts. We perform empirical studies on crowdfunding, which has gained mainstream attention due to its enormous penetration in modern society via a variety of online crowdfunding platforms. We study Kickstarter, one of the major players in this market and propose a model and an inference procedure for the amount of money donated to projects and their likelihood of success by capturing and quantifying the importance (sentiment) that possible donors give to the subjects (topics) of the projects. Experiments on a set of 45 K projects show that the addition of the temporal elements adds valuable information to the regression model and allows for a better explanation of the overall temporal behavior of the whole market in Kickstarter.