Deva Menéndez García, Daniel Carmona Cardona , Isabella Tobón Franco
Este trabajo analiza el impacto del neoliberalismo en el diseño urbano y la sostenibilidad de Medellín, centrándose en la transformación de la ciudad bajo políticas neoliberales desde la década de 1980. A partir de la revisión de los principales instrumentos de planeación del Área Metropolitana de Medellín —como la Ordenanza Departamental n.º 34 de 1980, el Plan Integral de Desarrollo Metropolitano (PIDM) 2008-2020 y el Acuerdo Metropolitano 40 de 2007—, se evalúa la planeación del Valle de Aburrá como centro conurbado y la efectividad de sus políticas públicas ambientales. Asimismo, se examina la interacción entre los sectores público y privado en proyectos urbanos estratégicos, como Metroplús y el Parque Arví. Los hallazgos evidencian una fragmentación social, territorial y ambiental, así como una estética urbana orientada al turismo ecológico, cuyos efectos en la sostenibilidad y la equidad social resultan cuestionables. Se concluye que estos elementos han sido instrumentalizados como herramientas de neoliberalización urbana, lo que pone en entredicho su verdadera contribución a la justicia ambiental y social.
Aesthetics of cities. City planning and beautifying, Urban groups. The city. Urban sociology
Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across "activity spaces" where people work, socialize, and engage in leisure. Human mobility data offers new opportunities to analyze broader segregation patterns, encompassing both residential and activity spaces, but challenges existing methods in capturing the complexity and local nuances of urban segregation. This work introduces InclusiViz, a novel visual analytics system for multi-level analysis of urban segregation, facilitating the development of targeted, data-driven interventions. Specifically, we developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI to reveal how these features influence segregation. The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail and evaluate urban planning interventions with real-time feedback. We conducted a quantitative evaluation to validate the model's accuracy and efficiency. Two case studies and expert interviews with social scientists and urban analysts demonstrated the system's effectiveness, highlighting its potential to guide urban planning toward more inclusive cities.
Devashish Khulbe, Alexander Belyi, Stanislav Sobolevsky
Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods don't account for network-based effects. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city's socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models. This work provides urban researchers methods to incorporate network effects in urban modeling and informs stakeholders of wider network-based effects in urban policymaking and planning.
Accurate assessment of urban canopy coverage is crucial for informed urban planning, effective environmental monitoring, and mitigating the impacts of climate change. Traditional practices often face limitations due to inadequate technical requirements, difficulties in scaling and data processing, and the lack of specialized expertise. This study presents an efficient approach for estimating green canopy coverage using artificial intelligence, specifically computer vision techniques, applied to aerial imageries. Our proposed methodology utilizes object-based image analysis, based on deep learning algorithms to accurately identify and segment green canopies from high-resolution drone images. This approach allows the user for detailed analysis of urban vegetation, capturing variations in canopy density and understanding spatial distribution. To overcome the computational challenges associated with processing large datasets, it was implemented over a cloud platform utilizing high-performance processors. This infrastructure efficiently manages space complexity and ensures affordable latency, enabling the rapid analysis of vast amounts of drone imageries. Our results demonstrate the effectiveness of this approach in accurately estimating canopy coverage at the city scale, providing valuable insights for urban forestry management of an industrial township. The resultant data generated by this method can be used to optimize tree plantation and assess the carbon sequestration potential of urban forests. By integrating these insights into sustainable urban planning, we can foster more resilient urban environments, contributing to a greener and healthier future.
Abstract While heat mitigation is crucial to achieving sustainable urban development, an inadequate understanding of the evolution of the urban thermal environment (UTE) and its relationship with socio-ecological systems (SESs) constrains the development of effective mitigation strategies. In this study, we use satellite observations from 2000–2021 to explore the evolving impact of SES interactions on the UTE of 136 Chinese urban areas. The results reveal a nonlinear intensification of the UTE over the period and an indication that an increasing number of urban areas have successfully applied UTE mitigation measures. Spatio-temporal patterns in UTE are shown to be strongly influenced by social and ecological factors and their interactions, whereby the higher the SES status, the stronger the decreasing UTE trend. These findings highlight the need for, and advantages of, developing win-win solutions for urban society and ecology and have important implications in creating integrated strategies for heat mitigation in promoting urban sustainability.
Rafael Prieto-Curiel, Pavel Luengas-Sierra, Christian Borja-Vega
Many cities are expanding in areas with scarce rainfall and limited water retention capacity, and are also becoming elongated and sprawled, making it harder to deliver services. This study quantifies the impact of urban form on access to water. We craft comparable urban forms for over 100 cities in Asia, Africa, and Latin America. For each city, we analyse the distance to the centre, one of the most critical features of cities. We introduce two metrics: remoteness, which quantifies the distance of any location to the city centre, and sparseness, a population-weighted average of all locations. We find that less remote areas have higher average income, are closer to critical infrastructure and have higher access to sewage and piped water. Sparser cities have higher water tariffs, lower proximity to critical infrastructure, and lower access to sewage and piped water. Finally, we model urban expansion under three scenarios: compact, persistent, and horizontal growth. When cities expand through compact growth rather than horizontal expansion, 220 million more people could gain access to piped water, and 190 million more to sewage services.
Social segregation in cities, spanning racial, residential, and income dimensions, is becoming more diverse and severe. As urban spaces and social relations grow more complex, residents in metropolitan areas experience varying levels of social segregation. If left unaddressed, this could lead to increased crime rates, heightened social tensions, and other serious issues. Effectively quantifying and analyzing the structures within urban spaces and resident interactions is crucial for addressing segregation. Previous studies have mainly focused on surface-level indicators of urban segregation, lacking comprehensive analyses of urban structure and mobility. This limitation fails to capture the full complexity of segregation. To address this gap, we propose a framework named Motif-Enhanced Graph Prototype Learning (MotifGPL),which consists of three key modules: prototype-based graph structure extraction, motif distribution discovery, and urban graph structure reconstruction. Specifically, we use graph structure prototype learning to extract key prototypes from both the urban spatial graph and the origin-destination graph, incorporating key urban attributes such as points of interest, street view images, and flow indices. To enhance interpretability, the motif distribution discovery module matches each prototype with similar motifs, representing simpler graph structures reflecting local patterns. Finally, we use the motif distribution results to guide the reconstruction of the two graphs. This model enables a detailed exploration of urban spatial structures and resident mobility patterns, helping identify and analyze motif patterns that influence urban segregation, guiding the reconstruction of urban graph structures. Experimental results demonstrate that MotifGPL effectively reveals the key motifs affecting urban social segregation and offer robust guidance for mitigating this issue.
In this forum paper, I revisit the rich and coherent literature on inequality from the 1990s, immersed in radical urban studies and Marxist political economy, and apply it to recent transitions in city fabrics, that is the built environment and the social worlds around it. Some city fabrics reflect powerful interests, while others are more everyday and mundane. Recently, there has been the sense that powerful fabrics have increasingly encroached upon or erased everyday ones. I use urban vignettes to visualize the shift from the corrugated city, where there was a rough balance between powerful and everyday fabrics, and the lopsided city, where powerful fabrics seek to displace and dominate. This transition requires a more robustly class-driven analysis than what is currently used in urban studies, itself fragmented. In response, I articulate a focused yet balanced analysis of the lopsided city in conversation with certain key legacies of the 1990s literature on inequality: studying the extremes, building theory on empirical richness, paying attention to the city fabric, a concern for social justice, the importance of formal mechanisms in the city (e.g. the state and developers), and balancing fragmented and totalizing views of the city. However, certain aspects of the 1990s literature have aged less well, such as the obsession with the dystopic, the narrow focus on global cities of the Global North, and the ‘all-or-nothing’ (universalistic) notions that class should dominate urban analysis.
<p>Expanding as a result of information and technological progress, the processes of globalization are immersing the material world and the sphere of human relationships in the space of digital technologies. Leading experts note that the world community is on the verge of fundamental changes associated with technological breakthroughs in various fields of knowledge. The article discusses the regional features of digital transformation in the Kabardino-Balkarian Republic. If in developed countries and the most dynamically developing regions of Russia, the transition of society to a new era of digital technologies is a logical, natural, from the point of view of evolutionary development, phenomenon, then what is this process like in traditional societies? This question was central to the present work. The results of the study showed that people rightly point out both the positive and negative aspects of digital transformation. The problem of mass layoffs is relevant. People are afraid that robots and computer programs will force them out of the labor market. This problem is especially acute in such an economically depressed region as the Kabardino-Balkarian Republic. Also, people are concerned about the erosion of the cultural foundations of the titular peoples of the region, as a result of the processes of globalization, which are taking place with particular intensity in the digital world. At the same time, the respondents noted the presence of significant positive aspects of digital transformation. Welfare increases, the solution of many everyday problems is simplified, the quality of human life improves.</p>
Sociology (General), Urban groups. The city. Urban sociology
Experimental investigations using wind and water tunnels have long been a staple of fluid mechanics research for a large number of applications. These experiments often single out a specific physical process to be investigated, while studies involving multiscale and multi-physics processes are rare due to the difficulty and complexity in the experimental setup. In the era of climate change, there is an increasing interest in innovative experimental studies in which fluid (wind and water) tunnels are employed for modelling multiscale, multi-physics phenomena of the urban climate. High-quality fluid tunnel measurements of urban-physics related phenomena are also much needed to facilitate the development and validation of advanced multi-physics numerical models. As a repository of knowledge in modelling these urban processes, we cover fundamentals, recommendations and guidelines for experimental design, recent advances and outlook on eight selected research areas, including (i) thermal buoyancy effects of urban airflows, (ii) aerodynamic and thermal effects of vegetation, (iii) radiative and convective heat fluxes over urban materials, (iv) influence of thermal stratification on land-atmosphere interactions, (v) pollutant dispersion, (vi) indoor and outdoor natural ventilation, (vii) wind thermal comfort, and (viii) urban winds over complex urban sites. Further, three main challenges, i.e., modelling of multi-physics, modelling of anthropogenic processes, and combined use of fluid tunnels, scaled outdoor and field measurements for urban climate studies, are discussed.
The efficiency of urban logistics is vital for economic prosperity and quality of life in cities. However, rapid urbanization poses significant challenges, such as congestion, emissions, and strained infrastructure. This paper addresses these challenges by proposing an optimal urban logistic network that integrates urban waterways and last-mile delivery in Amsterdam. The study highlights the untapped potential of inland waterways in addressing logistical challenges in the city center. The problem is formulated as a two-echelon location routing problem with time windows, and a hybrid solution approach is developed to solve it effectively. The proposed algorithm consistently outperforms existing approaches, demonstrating its effectiveness in solving existing benchmarks and newly developed instances. Through a comprehensive case study, the advantages of implementing a waterway-based distribution chain are assessed, revealing substantial cost savings (approximately 28%) and reductions in vehicle weight (about 43%) and travel distances (roughly 80%) within the city center. The incorporation of electric vehicles further contributes to environmental sustainability. Sensitivity analysis underscores the importance of managing transshipment location establishment costs as a key strategy for cost efficiencies and reducing reliance on delivery vehicles and road traffic congestion. This study provides valuable insights and practical guidance for managers seeking to enhance operational efficiency, reduce costs, and promote sustainable transportation practices. Further analysis is warranted to fully evaluate the feasibility and potential benefits, considering infrastructural limitations and canal characteristics.
The a rticle presents a n a nalysis of religious identity depending on the t ype of locality, region, confessional affiliation, age of respondents. Global trends are taken into account, which affects identification processes, secularization urbanization, religious diversity in large cities, and leads to the formation of a special type of urban culture, characterized by the simultaneous decline and growth of religious consciousness, blurring the boundaries of the religious and non-religious way of life of citizens. Based on the data of a sociological survey conducted in 2021 by the Institute of Sociology of the National Academy of Sciences of Belarus, a comparative analysis of the confessional, religious structure of various population groups has been carried out, the level of trust in the main Christian churches has been revealed. The conclusion has been made about the specifics of the own way of development of the Belarusian society, which is characterized by wide opportunities for worldview and identification choice in large cities, where a certain part of the inhabitants consider themselves free from religious influences.
ABSTRACT This paper presents an empirical study on the social and educational gaps between Recife and São Paulo, cities characterized by fast urbanization and strongly unequal urban configurations. Based on the notion of social space and statistical data from the last Population Census (IBGE/ 2010), we present a study on the space of educational disparities in the two metropolises. The objective was to test the relevance of the notion of social space in the Brazilian context, identifying at once, the distribution of social groups and their educational investments. We argue that the notion of social space can integrate several other key concepts of Bourdieusian sociology. We mobilize a large set of variables captured simultaneously, bringing to light the differences within the two metropolises and between the cities. The study’s originality lies in starting from this wide range of objective indicators related to living conditions, associating it with the use of indicators likely to be perceived as “subjective.” As expected, the first axis strongly correlates with longevity, household income, and education level. The second axis is the result of the correlation between the possession of a high school diploma and the presence of greater public infrastructure. In Recife, this second axis concerns a few neighborhoods in the city. In São Paulo, however, the same correlation is observed with a much larger set of neighborhoods in intermediate social positions. It is also noticeable that Recife has a much larger number of regions in extreme poverty compared to São Paulo.
Marilia C.P. Borges, Sérgio B. Abreu, Carlos H.R. Lima
et al.
Basic sanitation services are essential for human development, promoting health and inhibiting the spread of waterborne diseases. The availability of information on water and sanitation services at the local level supports the formulation, implementation and improvement of public policies aimed at advancing the provision of basic sanitation services to the population. In Brazil, the National Water and Sanitation Data System (SNIS), administered by the Ministry of Regional Development (MDR), is the largest information system for water and sanitation services in the country. Here we present the significant aspects of SNIS and offer the most recent results of water and sanitation services in the country, which reveals that water supply is the sanitation service closest to achieve the universalization preconized by the United Nations with almost 93% of the population served. The situation of sanitary sewer services reveals that only 61.9% of the Brazilian population have sewer collection systems, while only 78.5% of the collected volume is actually treated. The remaining 22.5% of the raw sewer is directly disposed in the environment. With respect to the generated sewer, only 49.1% of the volume is treated. The solid waste data show that a large part of the urban population is served by home collection services. The major challenge of this component is to ensure that the final destination is environmentally appropriate, since there are still many dumps that receive waste from different municipalities. The urban drainage data show that most Brazilian municipalities still have deficiencies in the planning of drainage services.
Urbanization. City and country, Political institutions and public administration (General)
O artigo apresenta um recorte histórico do cenário de uso das tecnologias digitais na América do Sul, mais especificamente, a assimilação da fabricação digital. O objetivo é compreender o papel de certos eventos, atores e como algumas dinâmicas foram instauradas nas áreas de design e arquitetura na primeira década dos anos 2000. Desse modo, configura um panorama de formação do cenário local frente a uma dimensão global, que aconteceu com a aquisição e domínio gradual de software e maquinário para a criação de estratégias que versavam entre a computação e a materialização de protótipos e elementos construtivos.
Architecture, Urban groups. The city. Urban sociology
Traditional urban planning demands urban experts to spend considerable time and effort producing an optimal urban plan under many architectural constraints. The remarkable imaginative ability of deep generative learning provides hope for renovating urban planning. While automated urban planners have been examined, they are constrained because of the following: 1) neglecting human requirements in urban planning; 2) omitting spatial hierarchies in urban planning, and 3) lacking numerous urban plan data samples. To overcome these limitations, we propose a novel, deep, human-instructed urban planner. In the preliminary work, we formulate it into an encoder-decoder paradigm. The encoder is to learn the information distribution of surrounding contexts, human instructions, and land-use configuration. The decoder is to reconstruct the land-use configuration and the associated urban functional zones. The reconstruction procedure will capture the spatial hierarchies between functional zones and spatial grids. Meanwhile, we introduce a variational Gaussian mechanism to mitigate the data sparsity issue. Even though early work has led to good results, the performance of generation is still unstable because the way spatial hierarchies are captured may lead to unclear optimization directions. In this journal version, we propose a cascading deep generative framework based on generative adversarial networks (GANs) to solve this problem, inspired by the workflow of urban experts. In particular, the purpose of the first GAN is to build urban functional zones based on information from human instructions and surrounding contexts. The second GAN will produce the land-use configuration based on the functional zones that have been constructed. Additionally, we provide a conditioning augmentation module to augment data samples. Finally, we conduct extensive experiments to validate the efficacy of our work.
Alice Battiston, Ludovico Napoli, Paolo Bajardi
et al.
Cycling is an outdoor activity with massive health benefits, and an effective solution towards sustainable urban transport. Despite these benefits and the recent rising popularity of cycling, most countries still have a negligible uptake. This uptake is especially low for women: there is a largely unexplained, persistent gender gap in cycling. To understand the determinants of this gender gap in cycling at scale, here we use massive, automatically-collected data from the tracking application Strava on outdoor cycling for 61 cities across the United States, the United Kingdom, Italy and the Benelux area. Leveraging the associated gender and usage information, we first quantify the emerging gender gap in recreational cycling at city-level. A comparison of cycling rates of women across cities within similar geographical areas unveils a broad range of gender gaps. On a macroscopic level, we link this heterogeneity to a variety of urban indicators and provide evidence for traditional hypotheses on the determinants of the gender-cycling-gap. We find a positive association between female cycling rate and urban road safety. On a microscopic level, we identify female preferences for street-specific features in the city of New York. Enhancing the quality of the dedicated cycling infrastructure may be a way to make urban environments more accessible for women, thereby making urban transport more sustainable for everyone.
Most cities in the US and in the world were organized around car traffic. In particular, large structures such as urban freeways or ring roads were built for reducing car traffic congestion. With the evolution of public transportation, working conditions, the future of these structures and the organization of large urban areas is uncertain. Here, we analyze empirical data for US cities and show that they display two transitions at different thresholds. For the first threshold of order $T_c^{FW}\sim 10^4$ commuters, we observe the emergence of a urban freeway. The second threshold is larger and of the order $T_c^{RR}\sim 10^5$ commuters above which a ring road emerges. In order to understand these empirical results, we propose a simple model based on a cost-benefit analysis which relies on the balance between construction and maintenance costs of infrastructures and the trip duration decrease (including the effect of congestion). This model predicts indeed such transitions and allows us to compute explicitly the commuter's thresholds in terms of critical parameters such as the average value of time, average capacity of roads, typical construction cost, etc. Furthermore, this analysis allows us to discuss possible scenarios for the future evolution of these structures. In particular, we show that in many cases it is beneficial to remove urban freeways due to their large social cost (that includes pollution, health cost, etc). This type of information is particularly useful at a time when many cities must confront with the dilemma of renovating these aging structures or converting them into another use.
Taking a practice theoretical approach and building on the research conducted with a group of people who live their lives on the streets of two Polish cities, this paper provides an account of the homeless city dwellers’ mode of emplacement. It offers the terms licensed, invisible, motile, material, relational, affective, and ad hoc mooring to describe how homeless people establish a place of and for various activities that make up their everyday practice of inhabiting the city. While highlighting the accomplishments of homeless places, the paper also underscores their tentativeness and instability. It situates the homeless mode of emplacement within a wider landscape of normative urban geography, against which the ways homeless people establish themselves in place are often judged out-of-place. It attends to the role that this transgressive potential plays in limiting homeless dwellers’ capabilities for mooring and considers how they might be enhanced.
Urban evolution processes occur at different scales, with intricate interactions between levels and relatively distinct type of processes. To what extent actual urban dynamics include an actual strong coupling between scales, in the sense of both top-down and bottom-up feedbacks, remains an open issue with important practical implications for the sustainable management of territories. We introduce in this paper a multi-scalar simulation model of urban growth, coupling a system of cities interaction model at the macroscopic scale with morphogenesis models for the evolution of urban form at the scale of metropolitan areas. Strong coupling between scales is achieved through an update of model parameters at each scale depending on trajectories at the other scale. The model is applied and explored on synthetic systems of cities. Simulation results show a non-trivial effect of the strong coupling. As a consequence, an optimal action on policy parameters such as containing urban sprawl is shifted. We also run a multi-objective optimization algorithm on the model, showing showing that compromise between scales are captured. Our approach opens new research directions towards more operational urban dynamics models including a strong feedback between scales.