Entropy and Cities: A Bibliographic Analysis towards More Circular and Sustainable Urban Environments

Cities are critical to a sustainable future for our planet; still, the construction and operation of cities rely on intensive resource and energy use and transformation, leading to the generation of waste, effluents, and pollution, representing negative externalities outside and inside the city. Within every process, transformation implies the use of energy and the increase of entropy. In an urban system, the transformation of energy and materials will trigger the creation of entropic landscapes, mainly in the informal city and in unguarded natural landscapes, even hundreds of kilometers away, which generates substantial economic, social, and environmental impacts. In this sense, cities are significant contributors to the environmental crisis. Upstream, degradation of landscapes and ecosystems is frequent. Cities' externalities and exogenous consumptions are directly linked with entropy and entropic landscapes, which are recognized as pollution (in the air, water, and land) or waste and in the degradation of natural ecosystems and communities. Through a systematic review of existing literature, this paper first outlines briefly how entropy has been applied in different disciplines and then focuses on presenting recent developments of how entropy has been defined, used, and characterized in urban studies concerning sustainability in cities and architecture, and presents a definition of the concept in relation to urban systems and key aspects to consider.

Empirical evidence for a jamming transition in urban traffic

Understanding the mechanisms leading to the formation and the propagation of traffic jams in large cities is of crucial importance for urban planning and traffic management. Many studies have already considered the emergence of traffic jams from the point of view of phase transitions, but mostly in simple geometries such as highways for example or in the framework of percolation where an external parameter is driving the transition. More generally, empirical evidence and characterization for a congestion transition in complex road networks are scarce, and here, we use traffic measures for Paris (France) during the period 2014-2018 for testing the existence of a jamming transition at the urban level. In particular, we show that the correlation function of delays due to congestion is a power law (with exponent η ≈ 0.4) combined with an exponential cut-off ξ. This correlation length is shown to diverge during rush hours, pointing to a jamming transition in urban traffic. We also discuss the spatial structure of congestion and identify a core of congested links that participate in most traffic jams and whose structure is specific during rush hours. Finally, we show that the spatial structure of congestion is consistent with a reaction-diffusion picture proposed previously.

Multi-System Urban Waste-Energy Self-Circulation: Design of Urban Self-Circulation System Based on Emergy Analysis

The current worldwide state of energy scarcity and low waste utilization has led to a decrease in the supply of ecological services, something that seriously affects the development of cities. In this study, we propose an urban self-circulation design based on multiple systems within the traditional biogas, wetland, rainwater, solar power, and urban farm systems framework to achieve effective improvements in urban waste utilization and the optimization of the urban waste–energy flow cycle. Emergy conversion is used to evaluate system optimization, and the simulation results show that the novel proposed system can effectively improve urban waste utilization with an energy output rate of 3.18 × 10, an environmental load of 4.27 × 10⁻², and a sustainability index of 7.45 × 10² in the core system; additionally, it can improve resource utilization of small-scale cities with an energy output rate of 1.85 × 10⁰, an environmental load of 1.20 × 10⁰, and a sustainability index of 1.54 × 10⁰ in the total system. The inter-system energy flow model can significantly optimize urban energy systems based on ecological models with low-emergy resource input, including biogas systems and urban farm systems. This model can reduce the environmental load and effectively compensate for the reduced supply capacity of ecosystem services caused by urbanization, making it suitable for extension to other small-scale built environments that are relatively independent and rich in natural resources.

Diffusion segregation and the disproportionate incidence of COVID-19 in African American communities

One of the most concerning aspects of the ongoing COVID-19 pandemic is that it disproportionately affects people from some specific ethnic and socio-economic minorities. In particular, since from the beginning of the pandemic it has been clear that people from Black and African American backgrounds seem to be hit especially hard by the virus, creating a substantial infection gap. The observed abnormal impact on these ethnic groups could probably be due to the co-occurrence of other known risk factors, including co-morbidity, poverty, level of education, access to healthcare, residential segregation and response to cures, although those factors do not seem able to explain fully and in depth the excess incidence of infections and deaths among African Americans. Here, we introduce the concept of diffusion segregation, that is the extent to which a given group of people is internally clustered or exposed to other groups, as a result of mobility and commuting habits. By analysing census and mobility data on major US cities, we found that the weekly excess COVID-19 incidence and mortality in African American communities at the beginning of the COVID-19 pandemic is significantly associated with their level of diffusion segregation. The results confirm that knowing where people commute to, rather than where they live, is potentially much more important to contain and curb the spreading of infectious diseases.

Diffusion segregation and the disproportionate incidence of COVID-19 in African American communities

One of the most concerning aspects of the ongoing COVID-19 pandemic is that it disproportionately affects people from some specific ethnic and socio-economic minorities. In particular, since from the beginning of the pandemic it has been clear that people from Black and African American backgrounds seem to be hit especially hard by the virus, creating a substantial infection gap. The observed abnormal impact on these ethnic groups could probably be due to the co-occurrence of other known risk factors, including co-morbidity, poverty, level of education, access to healthcare, residential segregation and response to cures, although those factors do not seem able to explain fully and in depth the excess incidence of infections and deaths among African Americans. Here, we introduce the concept of diffusion segregation, that is the extent to which a given group of people is internally clustered or exposed to other groups, as a result of mobility and commuting habits. By analysing census and mobility data on major US cities, we found that the weekly excess COVID-19 incidence and mortality in African American communities at the beginning of the COVID-19 pandemic is significantly associated with their level of diffusion segregation. The results confirm that knowing where people commute to, rather than where they live, is potentially much more important to contain and curb the spreading of infectious diseases.

Uncovering the behaviour of road accidents in urban areas

Different patterns in the incidence of road accidents are revealed when considering areas with increased levels of urbanization. To understand these patterns, road accident data from England and Wales is explored. In particular, the data are used to (i) generate time series for comparison of the incidence of road accidents in urban as opposed to rural areas, (ii) analyse the relationship between the number of road accidents and the population size of a set of urban areas, and (iii) model the likelihood of suffering an accident in an urban area and its dependence with population size. It is observed that minor and serious accidents are more frequent in urban areas, whereas fatal accidents are more likely in rural areas. It is also shown that, generally, the number of accidents in an urban area depends on population size superlinearly, with this superlinear behaviour becoming stronger for lower degrees of severity. Finally, given an accident in an urban area, the probability that the accident is fatal or serious decreases with population size and the probability that it is minor, increases sublinearly. These findings promote the question as to why such behaviours exist, the answer to which will lead to more sustainable urban policies.

Segregation and polarization in urban areas

Social behaviours emerge from the exchange of information among individuals-constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.

An Urban Scaling Estimation Method in a Heterogeneity Variance Perspective

Urban scaling laws describe powerful universalities of the scaling relationships between urban attributes and the city size across different countries and times. There are still challenges in precise statistical estimation of the scaling exponent; the properties of variance require further study. In this paper, a statistical regression method based on the maximum likelihood estimation considering the lower bound constraints and the heterogeneous variance of error structure, termed as CHVR, is proposed for urban scaling estimation. In the CHVR method, the heterogeneous properties of variance are explored and modeled in the form of a power-of-the-mean variance model. The maximum likelihood fitting method is supplemented to satisfy the lower bound constraints in empirical data. The CHVR method has been applied to estimating the scaling exponents of six urban attributes covering three scaling regimes in China and compared with two traditional methods. Method evaluations based on three different criteria validate that compared with both classical methods, the CHVR method is more effective and robust. Moreover, a statistical test and long-term variations of the parameter in the variance function demonstrate that the proposed heterogeneous variance function can not only describe the heterogeneity in empirical data adequately but also provide more meaningful urban information. Therefore, the CHVR method shows great potential to provide a valuable tool for effective urban scaling studies across the world and be applied to scaling law estimation in other complex systems in the future.

An R Package for Open, Reproducible Analysis of Urban Water Systems, With Application to Chicago

Urban water systems consist of natural and engineered flows of water interacting in complex ways. System complexity can be understood via mass conservative models that account for the interrelationships among all major flows and storages. We have developed a generic urban water system model in the R package CityWaterBalance. CityWaterBalance provides a reproducible workflow for studying urban water systems by facilitating automated retrievals of open data and post-processing with open source R functions. It allows the user to 1) rapidly assemble a quantitative, comprehensive assessment of flows thorough an urban area, and 2) easily change the spatial and temporal boundaries of analysis. We use CityWaterBalance to evaluate the water system in the Chicago metropolitan area on a monthly basis for water years 2001-2010. Results are used to consider 1) impacts of management decisions aimed at reducing stormwater and combined sewer overflows and 2) the significance of future changes in precipitation.

Disaster preparedness in a complex urban system: the case of Kathmandu Valley, Nepal

The city is a growing centre of humanitarian concern. Yet, aid agencies, governments and donors are only beginning to comprehend the scale and, importantly, the complexity of the humanitarian challenge in urban areas. Using the case study of the Kathmandu Valley, Nepal, this paper examines the analytical utility of recent research on complex urban systems in strengthening scholarly understanding of urban disaster risk management, and outlines its operational relevance to disaster preparedness. Drawing on a literature review and 26 interviews with actors from across the Government of Nepal, the International Red Cross and Red Crescent Movement, non-governmental organisations, United Nations agencies, and at-risk communities, the study argues that complexity can be seen as a defining feature of urban systems and the risks that confront them. To manage risk in these systems effectively, preparedness efforts must be based on adaptive and agile approaches, incorporating the use of network analysis, partnerships, and new technologies.