The anatomy of urban social networks and its implications in the searchability problem

The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure

Measuring the effect of node aggregation on community detection

Many times the nodes of a complex network, whether deliberately or not, are aggregated for technical, ethical, legal limitations or privacy reasons. A common example is the geographic position: one may uncover communities in a network of places, or of individuals identified with their typical geographical position, and then aggregate these places into larger entities, such as municipalities, thus obtaining another network. The communities found in the networks obtained at various levels of aggregation may exhibit various degrees of similarity, from full alignment to perfect independence. This is akin to the problem of ecological and atomic fallacies in statistics, or to the Modified Areal Unit Problem in geography. We identify the class of community detection algorithms most suitable to cope with node aggregation, and develop an index for aggregability, capturing to which extent the aggregation preserves the community structure. We illustrate its relevance on real-world examples (mobile phone and Twitter reply-to networks). Our main message is that any node-partitioning analysis performed on aggregated networks should be interpreted with caution, as the outcome may be strongly influenced by the level of the aggregation.Comment: 12 pages, 5 figure

Time evolution of the behaviour of Brazilian legislative Representatives using a complex network approach

The follow up of Representative behavior after elections is imperative for a democratic Representative system, at the very least to punish betrayal with no re-election. Our goal was to show how to follow Representatives' and how to show behavior in real situations and observe trends in political crises including the onset of game changing political instabilities. We used correlation and correlation distance matrices of Brazilian Representative votes during four presidential terms. Re-ordering these matrices with Minimal Spanning Trees displays the dynamical formation of clusters for the sixteen year period, which includes one Presidential impeachment. The reordered matrices, colored by correlation strength and by the parties clearly show the origin of observed clusters and their evolution over time. When large clusters provide government support cluster breaks, political instability arises, which could lead to an impeachment, a trend we observed three years before the Brazilian President was impeached. We believe this method could be applied to foresee other political storms.Comment: 11 pages, 4 Figure

Product Information Quality : A sustainability challenge in design and construction

The adverse consequences of building product performance pose sustainability problems for the built environment. Effective approaches to these problems require a clear understanding of building product information and its provision by manufacturers. This is an essential need for sustainable growth in industrialized construction, a system characterized by the expanded role of the manufacturing sector. Furthermore, a sustainable transition to digitalization in the construction industry needs digital interfaces capable of providing the information required for sustainable design and construction. The aim of this research is to contribute to an increased understanding of how building product information can support sustainability in the built environment. To this end, two fundamental aspects have been examined: the quality of information on the sustainability performance of building products and the usability of the digital interfaces providing such information. This research relies on critical realism and adopts a qualitative methodology to analyze and explain the mechanisms of creating and providing product information in four sequential case studies. Systems thinking and process tracing method have been applied to analyze the flow of product information in the construction industry, the operative processes that can support sustainability, and the stakeholders involved. In the first three case studies, the operative process is the diffusion of innovative ventilation products with superior indoor environmental performance. The first case study identifies the problems affecting this process. The second and third case studies, respectively, explore how product information and information exchange on building information modeling (BIM) library platforms can support the process. Influenced by the Grenfell Tower fire in London in 2017, the fourth case identifies the product information problems that can contribute to harmful facade fires threatening sustainability in the built environment. The study examines the capabilities for avoiding the identified problems and explores how an operative process of design, manufacturing, and construction of fire-safe facades can be supported. The findings reveal problems concerning the quality of information on the sustainability performance of products and the methods used by manufacturers for presenting such information. These problems have limited the availability and usability of the information in product databases and BIM object libraries. This defective flow of information affects the design process and can lead to unacceptable consequences such as facade fires. In addition, the inefficient methods of supplying product information have impeded the adoption of innovative products with improved sustainability performance. To address these issues, this research proposes the standardization of product information in collaboration with effective legislation and establishes a framework for evaluating the provision of information on the sustainability performance of building products. The theoretical contributions of this work include five tools: (1) a model for applied critical realism towards sustainability, (2) a matrix for the qualitative analysis of BIM object library platforms, (3) a matrix for evaluating the quality of information and digital interfaces, (4) a model of the functions of the standards on product information, and (5) a conceptual model of product information for sustainable design and construction

Applied Computational Modeling Approaches in Cigarette Smoking Epidemiology: Expanding Statistical Associations to Convey Theoretical Pathways

50 years since the landmark 1964 Surgeon General’s report on smoking and health, cigarette smoking remains the leading preventable cause of death and disability in the United States. The success of epidemiology and public health in the study of cigarette smoking, both as an exposure as well as a health outcome, has offered rich datasets and mechanistic discoveries that provide opportunities for the evolution of epidemiologic methods. Specifically, advancing computational science approaches allow for novel applications of methodologies, such as agent-based modeling or networks theory, in the epidemiological sciences to expand on existing knowledge. In this dissertation, we utilize approaches from epidemiology, statistics, computer science, and the philosophy of science to explore a range of hypothesized dynamics of smoking behavior that could contribute to changes in population-level smoking prevalence. We begin with a computational model that weighs the magnitude of the potential harms and benefits of electronic cigarette (e-cigarette or vaping) use from an adult smoking prevalence standpoint. We find that e-cigarettes can exert a much larger influence on smoking prevalence through routes of smoking cessation, as opposed to smoking initiation, if e-cigarette use remains primarily concentrated among current smokers. Conversely, e-cigarettes would need to behave as extremely effective gateways for smoking initiation, and never smokers would need to become e-cigarette users at substantially higher levels than currently observed, for these products to independently generate increases in population-level smoking prevalence. Next, we explore how contextual and individual network factors and demographic covariates change the effect of peer influence on smoking behavior in the National Longitudinal Study of Adolescent to Adult Health (Add Health). Using stratified mixed effects models, we find that the magnitude of friendship influence on smoking initiation differs by school social network density. We additionally find that the contextual factors, rather than peer influence, may be stronger predictors of smoking cessation. The effect estimates of these factors on smoking cessation of also differ by network density. Extending these results, we conclude with an abstract simulation of the hypothesized mechanisms that contribute to the outcomes of the stratified mixed effects model described previously. We find that network structure and peer influence are sufficient in combination to generate substantial differences in smoking prevalence by urbanicity, sex, and race, among US adolescents. These results provide evidence that support the potential for effect modification by network density on the hypothesized pathway between friendship influence and smoking behavior. While the field of tobacco control has been traditionally amenable to computational modeling approaches, few studies use computational modeling within an epidemiologic framework to provide support for hypothesized causal pathways that contribute to smoking behavior outcomes. Such perspectives are critical as the tobacco landscape continues to change with the introduction of new products, and as we gain a better understanding of the role that social networks play in the propagation of health behaviors. Through the integration of statistics, computational modeling, and epidemiologic methods, this dissertation seeks to provide insights into the potential causal pathways between various risk factors and smoking behavior outcomes. The results and discussions of this dissertation present potential avenues through which computational modeling can contribute added value to epidemiologic methods, in addition to our understanding of smoking behavior, beyond those of projection and evaluation.PHDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137162/1/scherng_1.pd

Inferring urban social networks from publicly available data

The emergence of social networks and the definition of suitable generative models for synthetic yet realistic social graphs are widely studied problems in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts -- including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. Given a territory of interest, and only based on widely available aggregated demographic and social-mixing data, we construct an age-stratified and geo-referenced synthetic population whose individuals are connected by "strong ties" of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size

A model of urban scaling laws based on distance dependent interactions

Socio-economic related properties of a city grow faster than a linear relationship with the population, in a log–log plot, the so-called superlinear scaling. Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socio-economic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socio-economic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences