We propose a statistical approach to determine the features of urban road networks affecting accessibility. Our approach is inspired by metabolic scaling theory (MST) in biology (West et al. 1997). We study the structure of road networks across 425 cities of different sizes in the USA. We show decentralization as an important difference between urban road networks and biological vascular networks. Per capita road capacity is independent of the spatial extent of cities. Driving distances do depend on the size of the city, although not as much as is predicted by a completely centralized model. This intermediate pattern between centralized and decentralized extremes may reflect a mixture of different travel behaviors. The approach presented here offers a novel macroscopic perspective on the differences between small and large cities and on how the road infrastructure and traffic might change as cities grow.

Understanding cities is central to addressing major global challenges from climate change to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only recently accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across US metropolitan statistical areas, and provide a framework for measuring the intrinsic diversity of economic activities that transcends scales of the classification scheme. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). We present a simple mathematical derivation of the universality, and provide a model, together with its economic implications of open-ended diversity created by urbanization, for understanding the observed empirical distribution. Given the universal distribution, scaling analyses for individual business categories enable us to determine their relative abundances as a function of city size. These results shed light on the processes of economic differentiation with scale, suggesting a general structure for the growth of national economies as integrated urban systems.

Attempts to understand the relationship between diversity, productivity and scale have remained limited due to the scheme-dependent nature of the taxonomies describing complex systems. We analyze the diversity of US metropolitan areas in terms of profession diversity and employment to show how this frequency distribution takes a universal scale-invariant form, common to all cities, in the limit of infinite resolution of occupational taxonomies. We show that this limit is obtained under general conditions that follow from the analysis of the variation of the occupational frequency across taxonomies at different resolutions in a way analogous to finite-size scaling in statistical physical systems. We propose a theoretical framework that derives the form and parameters of the limiting distribution of professions based on the appearance, in urban social networks, of new occupations as the result of specialization and coordination of labor. By deriving classification scheme-independent measures of functional diversity and modeling cities as social networks embedded in infrastructural space, these results show how standard economic arguments of division and coordination of labor can be articulated in detail in cities and provide a microscopic basis for explaining increasing returns to population scale observed at the level of entire metropolitan areas.

While segregation is usually evaluated at the residential level, the recent influx of large streams of data describing urbanites’ movement across the city allows to generate detailed descriptions of spatio-temporal segregation patterns across the activity space of individuals. For instance, segregation across the activity space is usually thought to be lower compared with residential segregation given the importance of social complementarity, among other factors, shaping the economies of cities. However, these new dynamic approaches to segregation convey important methodological challenges. This paper proposes a methodological framework to investigate segregation during working hours. Our approach combines three well-known mathematical tools: community detection algorithms, segregation metrics and random walk analysis. Using Santiago (Chile) as our model system, we build a detailed home–work commuting network from a large dataset of mobile phone pings and spatially partition the city into several communities. We then evaluate the probability that two persons at their work location will come from the same community. Finally, a randomization analysis of commuting distances and angles corroborates the strong segregation description for Santiago provided by the sociological literature. While our findings highlights the benefit of developing new approaches to understand dynamic processes in the urban environment, unveiling counterintuitive patterns such as segregation at our workplace also shows a specific example in which the exposure dimension of segregation is successfully studied using the growingly available streams of highly detailed anonymized mobile phone registries.

Reform to the Chilean educational system seeks to improve public education, adapting the school curriculum to new technologies and the information revolution. Our study focused on highlighting potential issues with environmental and technological education, offering opportunities to complement the current Chilean curriculum with an interdisciplinary approach. During a five-week period interspersed through the school semester, 115 underprivileged children in seventh grade from five public schools of southern Chile participated in workshops based on Arduino technology to address current environmental problems caused by global change and anthropization. Surveys were conducted to participating students and included both open and closed-ended questions, which measured attitudes and perceptions towards science and the use of technology in classrooms and daily lives. Surveys were analysed quantitatively with basic statistics and qualitatively by building semantic networks of the relationships between different concepts expressed by the students. Results did not show significant changes in attitudes towards science or technology before and after the workshops but students reported mixed feelings about science. Indifference and fear emerged, coupled with the need for more interactive experiences. Curiosity and enthusiasm also surfaced when students were using new technologies. Since the school curriculum is generally focused exclusively towards the completion of specific curricular aims, highly vulnerable schools in Chile have few opportunities to access interactive, hands-on activities. Low-cost, emergent technologies, such Arduinos, have a high potential for improving student's attitude towards science and technology and their use should be considered when tailoring school curriculum in Chile and elsewhere.

Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors’ location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators.