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Understanding metropolitan patterns of daily encounters - Supporting Information Appendix
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Familiarity is an elusive concept, capturing what we know intimately and what we only recognize from having seen before. This article aims to disambiguate these interpretations by proposing a sociological conceptualization of familiarity as a dynamic relationship to the world that develops over time and through experience and that allows one to progressively disattend from what appears as “usual.” Focusing on how urban environments and their human entities become familiar and stop being familiar, I propose that familiarity be thought of as an ongoing relational and interactional achievement, allowing us to focus on our daily activities while relying on a practical knowledge of our surroundings. The conceptualization process unfolds via five questions: What is familiarity? Where does it come from? What threatens it? What does it produce? How can it be operationalized and studied empirically?
Where do neighborhoods come from and why do certain resources and effects--such as social capital and collective efficacy--bundle together in some neighborhoods and not in others? From the Ground Up argues that neighborhood communities emerge from neighbor networks, and shows that these social relations are unique because of particular geographic qualities. Highlighting the linked importance of geography and children to the emergence of neighborhood communities, Rick Grannis models how neighboring progresses through four stages: when geography allows individuals to be conveniently available to one another; when they have passive contacts or unintentional encounters; when they actually initiate contact; and when they engage in activities indicating trust or shared norms and values. Seamlessly integrating discussions of geography, household characteristics, and lifestyle, Grannis demonstrates that neighborhood communities exhibit dynamic processes throughout the different stages. He examines the households that relocate in order to choose their neighbors, the choices of interactions that develop, and the exchange of beliefs and influence that impact neighborhood communities over time. Grannis also introduces and explores two geographic concepts--t-communities and street islands--to capture the subtle features constraining residents' perceptions of their environment and community. Basing findings on thousands of interviews conducted through door-to-door canvassing in the Los Angeles area as well as other neighborhood communities, From the Ground Up reveals the different ways neighborhoods function and why these differences matter.
Complex networks are used to depict topological features of complex systems. The structure of a network characterizes the interactions among elements of the system, and facilitates the study of many dynamical processes taking place on it. In previous investigations, the topological infrastructure underlying dynamical systems is simplified as a static and invariable skeleton. However, this assumption cannot cover the temporal features of many time-evolution networks, whose components are evolving and mutating. In this letter, utilizing the log data of WiFi users in a Chinese university campus, we infuse the temporal dimension into the construction of dynamical human contact network. By quantitative comparison with the traditional aggregation approach, we find that the temporal contact network differs in many features, e.g., the reachability, the path length distribution. We conclude that the correlation between temporal path length and duration is not only determined by their definitions, but also influenced by the micro-dynamical features of human activities under certain social circumstance as well. The time order of individuals' interaction events plays a critical role in understanding many dynamical processes via human close proximity interactions studied in this letter. Besides, our study also provides a promising measure to identify the potential superspreaders by distinguishing the nodes functioning as the relay hub.
This chapter is proposing a consistent set of definitions of movement and activity against the back- ground of the data needs of transport modelling. The proposed consistent classification scheme for the movement of goods and persons is based on the concept of the stage, the activity and the reference lo- cation or base. Movement is considered as separate from activities at destinations in contrast for ex- ample to time-budget studies, where it is just one among many types of activity. The operational defi- nition of the activity is left to the survey designer to suit the needs of the particular study. The chapter discusses the difficulties resulting from the definition, in particular when aggregating information and the typical items recorded for stages and activities. In the second part, the chapter discusses how to translate these definitions into surveys in two steps. The first step is to define the scope of the survey in detail, in particular the movement and activities to be reported, so that the universe of the survey is clear. The second step is the choice of the survey ap- proach, the way in which the respondent is guided in recording/recalling movements and activities. The different possible forms and their limitations are discussed.
This analysis of newly available longitudinal data on individual trip making and activity behaviour debates the most suitable methodological tools to represent the structures of long-term travel behaviour. Also discussed is what such data reveals about travellers' motives, and how planning should translate the findings into forecasting tools and transport strategies. The inquiry reveals the multifaceted character of daily life travel, and the variability that individuals show in activity behaviour. © Stefan Schönfelder and Kay W. Axhausen 2010. All rights reserved.
Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions. These data can be very useful to transit planners, from the day-to-day operation of the transit system to the strategic long-term planning of the network. This review covers several aspects of smart card data use in the public transit context. First, the technologies are presented: the hardware and information systems required to operate these tools; and privacy concerns and legal issues related to the dissemination of smart card data, data storage, and encryption are addressed. Then, the various uses of the data at three levels of management are described: strategic (long-term planning), tactical (service adjustments and network development), and operational (ridership statistics and performance indicators). Also reported are smart card commercialization experiments conducted all over the world. Finally, the most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.
The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible–infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks.
Many systems take the form of networks, including the Internet, distribution and transport networks, neural networks, food webs, and social networks. The characterization and modeling of these systems has proved amenable to treatment using techniques drawn from statistical and computational physics, and has as a result attracted considerable attention in the physics literature in recent years. In this paper the author reviews some of the interesting issues in this area and recounts some recent work on these issues by himself and by others.
Linked Research
Article
January 2013