Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

Most cities are car-centric, allocating a privileged amount of urban space to cars at the expense of sustainable mobility like cycling. Simultaneously, privately owned vehicles are vastly underused, wasting valuable opportunities for accommodating more people in a livable urban environment by occupying spacious parking areas. Since a data-driven quantification and visualization of such urban mobility space inequality is lacking, here we explore how crowdsourced data can help to advance its understanding. In particular, we describe how the open-source online platform What the Street!? uses massive user-generated data from OpenStreetMap for the interactive exploration of city-wide mobility spaces. Using polygon packing and graph algorithms, the platform rearranges all parking and mobility spaces of cars, rails, and bicycles of a city to be directly comparable, making mobility space inequality accessible to a broad public. This crowdsourced method confirms a prevalent imbalance between modal share and space allocation in 23 cities worldwide, typically discriminating bicycles. Analyzing the guesses of the platform’s visitors about mobility space distributions, we find that this discrimination is consistently underestimated in the public opinion. Finally, we discuss a visualized scenario in which extensive parking areas are regained through fleets of shared, autonomous vehicles. We outline how such accessible visualization platforms can facilitate urban planners and policy makers to reclaim road and parking space for pushing forward sustainable transport solutions

Analyses of a Virtual World

We present an overview of a series of results obtained from the analysis of human behavior in a virtual environment. We focus on the massive multiplayer online game (MMOG) Pardus which has a worldwide participant base of more than 400,000 registered players. We provide evidence for striking statistical similarities between social structures and human-action dynamics in the real and virtual worlds. In this sense MMOGs provide an extraordinary way for accurate and falsifiable studies of social phenomena. We further discuss possibilities to apply methods and concepts developed in the course of these studies to analyse oral and written narratives.Comment: 16 pages, 7 figures. To appear in: "Maths Meets Myths: Complexity-science approaches to folktales, myths, sagas, and histories." Editors: R. Kenna, M. Mac Carron, P. Mac Carron. (Springer, 2016

How women organize social networks different from men

Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks. Here we study gender-specific differences of a multiplex network from a complete behavioral dataset of an online-game society of about 300,000 players. On the individual level females perform better economically and are less risk-taking than males. Males reciprocate friendship requests from females faster than vice versa and hesitate to reciprocate hostile actions of females. On the network level females have more communication partners, who are less connected than partners of males. We find a strong homophily effect for females and higher clustering coefficients of females in trade and attack networks. Cooperative links between males are under-represented, reflecting competition for resources among males. These results confirm quantitatively that females and males manage their social networks in substantially different ways.Comment: 8 pages, 3 figure

Multiplex Graph Association Rules for Link Prediction

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. In our experiments, we show that, exploiting graph association rules, we are able to achieve a prediction performance close to an ideal ensemble classifier. Further, we perform a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory.Comment: Accepted for publication in 15th International Conference on Web and Social Media (ICWSM) 202

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

Fast Multiplex Graph Association Rules for Link Prediction

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. We improve over previous work by creating a framework that is also efficient in terms of runtime, which enables an increase in prediction performance. This increase in efficiency allows us to improve a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory.Comment: arXiv admin note: substantial text overlap with arXiv:2008.0835

Contraction of online response to major events

Quantifying regularities in behavioral dynamics is of crucial interest for understanding collective social events such as panics or political revolutions. With the widespread use of digital communication media it has become possible to study massive data streams of user-created content in which individuals express their sentiments, often towards a specific topic. Here we investigate messages from various online media created in response to major, collectively followed events such as sport tournaments, presidential elections or a large snow storm. We relate content length and message rate, and find a systematic correlation during events which can be described by a power law relation - the higher the excitation the shorter the messages. We show that on the one hand this effect can be observed in the behavior of most regular users, and on the other hand is accentuated by the engagement of additional user demographics who only post during phases of high collective activity. Further, we identify the distributions of content lengths as lognormals in line with statistical linguistics, and suggest a phenomenological law for the systematic dependence of the message rate to the lognormal mean parameter. Our measurements have practical implications for the design of micro-blogging and messaging services. In the case of the existing service Twitter, we show that the imposed limit of 140 characters per message currently leads to a substantial fraction of possibly dissatisfying to compose tweets that need to be truncated by their users.Comment: project page: http://senseable.mit.edu/tweetbursts

Statistical physics approaches to large-scale socio-economic networks

Die statistische Physik erforschte im letzten Jahrzehnt eine Fülle von wissenschaftlichen Gebieten, was zu einem besseren quantitativen Verständnis von verschiedenen, aus vielen Elementen bestehenden Systemen, z.B. von sozialen Systemen, geführt hat. Eine empirische Quantifizierung von menschlichem Verhalten auf gesellschaftlichem Niveau hat sich allerdings bisher als sehr schwierig erwiesen, wegen Problemen bei der Gewinnung und Qualität von Daten. In dieser Doktorarbeit erstellen wir zum ersten mal einen umfangreichen über fünf Jahre gesammelten Datensatz, der praktisch alle Aktionen und Eigenschaften der 350.000 Teilnehmer einer gesamten menschlichen Gesellschaft aus einem selbstentwickelten Massive Multiplayer Online Game enthält. Wir beschreiben dieses aus stark wechselwirkenden Spielern bestehende soziale System in drei Ebenen. In einem ersten Schritt analysieren wir die Individuen und deren Verhalten im Verlauf der Zeit. Eine Skalen- und Fluktuationsanalyse von Aktions-Reaktions-Zeitreihen enthüllt Persistenz der möglichen Aktionen und qualitative Unterschiede zwischen "guten" und "schlechten" Spielern. Wir untersuchen danach den Diffusionsprozess der im Spieluniversum stattfindenden Bewegungen der Individuen. Wir finden Subdiffusivität und eine durch ein Potenzgesetz verteilte Präferenz zu kürzlich besuchten Orten zurückzukehren. Zweitens, auf der nächsthöheren Ebene, verwenden wir Netzwerktheorie um die topologische Struktur der Interaktionen zwischen Individuen zu quantifizieren. Wir konzentrieren uns auf sechs durch direkte Interaktionen definierte Netzwerke, drei davon positiv (Handel, Freundschaft, Kommunikation), drei negativ (Feindschaft, Attacke, Bestrafung). Diese Netzwerke weisen nichttriviale statistische Eigenschaften auf, z.B. skaleninvariante Topologie, und entwickeln sich in der Zeit, was uns erlaubt eine Reihe von Hypothesen über sozialdynamische Phänomene zu testen. Wir finden qualitative Unterschiede zwischen positiven und negativen Netzwerken in Evolution und Struktur. Schließlich untersuchen wir das Multiplex-Netzwerk der Spielergesellschaft, das sich aus den einzelnen Netzwerk-Schichten zusammensetzt. Wir quantifizieren Interaktionen zwischen verschiedenen Netzwerken und zeigen die nichttrivialen Organisationsprinzipien auf die auch in echten menschlichen Gesellschaften beobachtet wurden. Unsere Erkenntnisse liefern Belege für die Hypothese der strukturellen Balance, die eine Vermeidung von gewissen frustrierten Zuständen auf mikroskopischem Niveau postuliert. Mit diesem Aufbau demonstrieren wir die Möglichkeit der Gewinnung neuartiger wissenschaftlicher Erkenntnisse über die Natur von kollektivem menschlichen Verhalten in großangelegten sozialen Systemen.In the past decade a variety of fields has been explored by statistical physicists, leading to an increase of our quantitative understanding of various systems composed of many interacting elements, such as social systems. However, an empirical quantification of human behavior on a societal level has so far proved to be tremendously difficult due to problems in data availability, quality and ways of acquisition. In this doctoral thesis we compile for the first time a large-scale data set consisting of practically all actions and properties of 350,000 odd participants of an entire human society interacting in a self-developed Massive Multiplayer Online Game, over a period of five years. We describe this social system composed of strongly interacting players in the game in three consecutive levels. In a first step, we examine the individuals and their behavioral properties over time. A scaling and fluctuation analysis of action-reaction time-series reveals persistence of the possible actions and qualitative differences between "good" and "bad" players. We then study and model the diffusion process of human mobility occurring within the "game universe". We find subdiffusion and a power-law distributed preference to return to more recently visited locations. Second, on a higher level, we use network theory to quantify the topological structure of interactions between the individuals. We focus on six network types defined by direct interactions, three of them with a positive connotation (trade, friendship, communication), three with a negative one (enmity, attack, punishment). These networks exhibit non-trivial statistical properties, e.g. scale-free topology, and evolve over time, allowing to test a series of long-standing social-dynamics hypotheses. We find qualitative differences in evolution and topological structure between positive and negative tie networks. Finally, on a yet higher level, we consider the multiplex network of the player society, constituted by the coupling of the single network layers. We quantify interactions between different networks and detect the non-trivial organizational principles which lead to the observed structure of the system and which have been observed in real human societies as well. Our findings with the multiplex framework provide evidence for the half-century old hypothesis of structural balance, where certain frustrated states on a microscopic level tend to be avoided. Within this setup we demonstrate the feasibility for generating novel scientific insights on the nature of collective human behavior in large-scale social systems

Supersampling and network reconstruction of urban mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that aim to draw policies from the activities of humans in space. Despite recent availability of large scale data sets related to human mobility such as GPS traces, mobile phone data, etc., it is still true that such data sets represent a subsample of the population of interest, and then might give an incomplete picture of the entire population in question. Notwithstanding the abundant usage of such inherently limited data sets, the impact of sampling biases on mobility patterns is unclear -- we do not have methods available to reliably infer mobility information from a limited data set. Here, we investigate the effects of sampling using a data set of millions of taxi movements in New York City. On the one hand, we show that mobility patterns are highly stable once an appropriate simple rescaling is applied to the data, implying negligible loss of information due to subsampling over long time scales. On the other hand, contrasting an appropriate null model on the weighted network of vehicle flows reveals distinctive features which need to be accounted for. Accordingly, we formulate a "supersampling" methodology which allows us to reliably extrapolate mobility data from a reduced sample and propose a number of network-based metrics to reliably assess its quality (and that of other human mobility models). Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is needed.Comment: 14 pages, 4 figure