Hasil untuk "Urban groups. The city. Urban sociology"

Álvaro Mazorra, Jordi Nofre, Manuel García Ruiz

This article examines how the processes of gentrification and touristification associated with the globalization of Madrid's economy have increased the existing socio-spatial inequalities in the city. Focusing on Lavapiés neighborhood, which is one of the most iconic historical neighborhoods of the Spanish capital, this article presents a mixed methods research based on both data extraction from official sources and conducting 22 semi-structured interviews. Findings show how the interplay between gentrification and touristification in Lavapiés has reinforced the process of expulsion of neighbors while local institutions promoted the neighborhood as the most cosmopolitan in Madrid. The final part of the article concludes that Madrid has moved in recent years towards a dual city model where the interplay between the gentrification and touristification of the historic center is the main driver of the suburbanization of poverty in contemporary Madrid.

Laura Ascione

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always remaining in the groove of rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is the expression of continuously updating emerging topics concerning relationships between urban planning, mobility, and environment, through a collection of short scientific papers written by young researchers. The Review Notes are made of five parts. Each section examines a specific aspect of the broader information storage within the main interests of TeMA Journal. In particular, the Urban planning literature review section presents recent books and journals on selected topics and issues within the global scientific panorama. For the first issue of TeMA Journal volume no. 18, this section provides a critical overview of recent reports and documents on climate change, published by different types of stakeholders. This review examines the landscape of climate change reporting through a comparative lens, focusing on key findings, strengths, weaknesses, and implications of selected publications. This contribution aims to examine reports produced by International Governmental Organizations (IGOs), analyzing their approach, findings, and potential limitations.

Wilfred Ochieng Omollo

Urbanisation across sub-Saharan Africa is transforming the spatial structure of secondary towns, often generating uneven and fragmented growth. A key manifestation of this process is intra-urban dualism, where well-planned, affluent neighbourhoods coexist with densely populated, poorly regulated settlements. This spatial divide undermines orderly growth, deepens inequality, and places pressure on urban infrastructure. In Kenya, intra-urban dualism is increasingly evident, yet limited research has explored how it influences land-use transformation and sustainable development. Addressing this research gap is essential to understand how spatial inequalities shape urban growth trajectories and to guide equitable planning interventions. This study examines intra-urban dualism and land-use transformation in Kisii town, western Kenya, focusing on the contrasting neighbourhoods of Milimani (a low-density planned area) and Jogoo (a high-density unregulated settlement). Land-use and land-cover changes from 2005 to 2024 were analysed and projected to 2044, using ArcGIS Pro and QGIS. Building density, plot size compliance, and coverage ratios were quantified and validated through a one-sample t-test. Results show that Milimani has largely retained its planned form, whereas Jogoo has undergone rapid, unregulated densification driven by weak development control and fragmented land ownership. The study recommends data-driven, geospatially informed development control supported by adaptive zoning, participatory monitoring, blockchain-based permitting, and resilience audits to promote sustainable, inclusive, and transparent urban growth.

Victoria Reis, Cheila Llorens, Pedro Soto et al.

This study uses the National Survey of Children’s Health to examine the nationwide prevalence and severity of US adolescent mental health issues in the 12–17 age group between the years 2022 and 2023 in relation to parental mental health and exposure to adverse childhood experiences (ACEs). We used the NSCH data collected for 12–17-year-old adolescents. Descriptive statistics were generated for the selected sample and binary logistics regressions were conducted to examine influential factors for the presence and severity of adolescent depression and anxiety for the selected year. Adolescents aged 12–17 who experienced neighborhood violence had higher odds of being diagnosed with anxiety (OR = 1.369, <i>p</i> = 0.009) and depression (OR = 1.508, <i>p</i> = 0.004). Those living with someone who was mentally ill, suicidal, or severely depressed showed increased odds of anxiety (OR = 1.642, <i>p</i> < 0.001) and depression (OR = 1.587, <i>p</i> < 0.001). Adolescents judged unfairly due to a health condition or disability had markedly higher odds of anxiety (OR = 3.056, <i>p</i> < 0.001) and depression (OR = 1.835, <i>p</i> < 0.001), including severe forms (severe anxiety OR = 2.569; severe depression OR = 2.238; both <i>p</i> < 0.001). Poorer parental emotional health was consistently associated with higher adolescent anxiety and depression, with “fair” parental emotional health showing the strongest association for depression (OR = 7.320, <i>p</i> < 0.001). These findings demonstrate the need for better tailored mental health efforts towards both adolescents and their caregivers highlighting the harm of long-term environmental and familial stressors, and the gaps in community approaches in this population.

Mithul Chander, Sai Pragnya Ranga, Prathamesh Mayekar

We introduce the {\em Atlas Urban Index} (AUI), a metric for measuring urban development computed using Sentinel-2 \citep{spoto2012sentinel2} satellite imagery. Existing approaches, such as the {\em Normalized Difference Built-up Index} (NDBI), often struggle to accurately capture urban development due to factors like atmospheric noise, seasonal variation, and cloud cover. These limitations hinder large-scale monitoring of human development and urbanization. To address these challenges, we propose an approach that leverages {\em Vision-Language Models }(VLMs) to provide a development score for regions. Specifically, we collect a time series of Sentinel-2 images for each region. Then, we further process the images within fixed time windows to get an image with minimal cloud cover, which serves as the representative image for that time window. To ensure consistent scoring, we adopt two strategies: (i) providing the VLM with a curated set of reference images representing different levels of urbanization, and (ii) supplying the most recent past image to both anchor temporal consistency and mitigate cloud-related noise in the current image. Together, these components enable AUI to overcome the challenges of traditional urbanization indices and produce more reliable and stable development scores. Our qualitative experiments on Bangalore suggest that AUI outperforms standard indices such as NDBI.

Miriam Louise Carnot, Jonas Kunze, Erik Fastermann et al.

To maintain an overview of urban conditions, city administrations manage databases of objects like traffic signs and trees, complete with their geocoordinates. Incidents such as graffiti or road damage are also relevant. As digitization increases, so does the need for more data and up-to-date databases, requiring significant manual effort. This paper introduces MapAnything, a module that automatically determines the geocoordinates of objects using individual images. Utilizing advanced Metric Depth Estimation models, MapAnything calculates geocoordinates based on the object's distance from the camera, geometric principles, and camera specifications. We detail and validate the module, providing recommendations for automating urban object and incident mapping. Our evaluation measures the accuracy of estimated distances against LiDAR point clouds in urban environments, analyzing performance across distance intervals and semantic areas like roads and vegetation. The module's effectiveness is demonstrated through practical use cases involving traffic signs and road damage.

Negin Nazarian, Andy J Pitman, Mathew J Lipson et al.

Cities are experiencing significant warming and more frequent climate extremes, raising risks for over 90% of Australians living in cities. Yet many of our tools for climate prediction and projection lack accurate representations of these environments. We also lack the observations and datasets needed to evaluate model performance. This paper identifies critical gaps in Australias current capability, showing how they undermine climate impact and risk assessments in cities and may lead to poorly designed adaptation and mitigation strategies. These gaps, and the recommendations to address them, were identified through consultation with experts across research institutes, universities, two ARC Centres of Excellence, federal and state governments, and private agencies. Our recommendations span four key areas: city descriptive datasets, integrated observations, fit for purpose models, and a coordinated community of research and practice. Urgent action is needed to tailor models to Australia's unique urban landscapes and climates. This requires comprehensive, nationally consistent, high resolution datasets that capture the form, fabric, and function of contemporary and future cities. It also requires filling systematic gaps in integrated networks of urban climate observations for evaluation and benchmarking. At the same time, scientific understanding of key urban processes that influence weather and climate must advance, alongside improvements in their representation in physical models. This can be achieved through a national community of research and practice that codesigns and oversees an implementation plan, integrated with infrastructure such as ACCESS NRI and AURIN. Building this capability will enable us to answer critical questions about the interaction between cities and climate, protecting Australias urban populations and ensuring a resilient future.

Feifei Tan, Chenyu Sun

Abstract Green innovation meets the simultaneous demands of green and innovation‐driven development models when it is deemed as a key to realizing a green economic transition. However, factor market distortion impedes China's green development through factor mobility and resource allocation. Under such circumstances, we detect whether and how factor market distortion affects green innovation from various perspectives in the case study of China's Sustainable Development Demonstration Belt. The findings demonstrate that the distortion has a pronounced inhibiting effect on the green innovation growth in the study area. For the eastern and more‐developed cities, factor market distortion considerably inhibits green innovation improvement, while the impact is less pronounced in the western (or central) and less developed cities. Furthermore, the factor market distortion negatively affects green innovation through some effective paths, like energy efficiency and environmental regulation. From a spatial perspective, the green innovation's spillover effect could be reduced by both the distortions of the labor market and capital market. Thus, this study would provide strong theoretical support for enhancing the factor market system and improving the multiregional green innovation power in China, as well as scientific suggestions on transitioning to China's sustainable development.

Ola M. Jarrar, Majd Al-Homoud

This paper investigates the potential for creating walkable communities in Riyadh using a qualitative research methodology. This approach encompassed: (1) systematic selection of international case studies renowned for their walkability strategies, (2) critical comparative analysis of these case studies, and (3) extraction of a Walkability model to derive strategic guidelines and recommendations tailored to Riyadh’s context. Through literature review, four international case studies emblematic of effective walkability practices were examined. Key themes and strategies that emerged from these analyses included integrated public transit, compact urban design, pedestrian infrastructure, community engagement, cultural considerations, and context-sensitive innovation. Based on these themes we threaded a comprehensive model for sustainable walkability in Riyadh. To assess such model, we extracted conceptual framework and suggested future hypotheses for future studies. By synthesising these findings, the research proposed a walkability model to advance Riyadh’s transformation into a walkable city.

Milad Malekzadeh, Elias Willberg, Jussi Torkko et al.

The visual appeal of urban environments significantly impacts residents' satisfaction with their living spaces and their overall mood, which in turn, affects their health and well-being. Given the resource-intensive nature of gathering evaluations on urban visual appeal through surveys or inquiries from residents, there is a constant quest for automated solutions to streamline this process and support spatial planning. In this study, we applied an off-the-shelf AI model to automate the analysis of urban visual appeal, using over 1,800 Google Street View images of Helsinki, Finland. By incorporating the GPT-4 model with specified criteria, we assessed these images. Simultaneously, 24 participants were asked to rate the images. Our results demonstrated a strong alignment between GPT-4 and participant ratings, although geographic disparities were noted. Specifically, GPT-4 showed a preference for suburban areas with significant greenery, contrasting with participants who found these areas less appealing. Conversely, in the city centre and densely populated urban regions of Helsinki, GPT-4 assigned lower visual appeal scores than participant ratings. While there was general agreement between AI and human assessments across various locations, GPT-4 struggled to incorporate contextual nuances into its ratings, unlike participants, who considered both context and features of the urban environment. The study suggests that leveraging AI models like GPT-4 allows spatial planners to gather insights into the visual appeal of different areas efficiently, aiding decisions that enhance residents' and travellers' satisfaction and mental health. Although AI models provide valuable insights, human perspectives are essential for a comprehensive understanding of urban visual appeal. This will ensure that planning and design decisions promote healthy living environments effectively.

Iñigo Delgado-Enales, Joshua Lizundia-Loiola, Patricia Molina-Costa et al.

The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. The ability to estimate temperatures with a high degree of accuracy allows for the identification of the highest priority areas in cities where urban improvements need to be made to reduce thermal discomfort. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models. Based on the obtained results, deep neural networks are confirmed to be faster and less computationally expensive alternative for ground-level air temperature compared to numerical models.

Myeong Lee, Julia H. P. Hsu

Between 1960 and 1980, urban renewal transformed many cities, creating vast handwritten records. These documents posed a significant challenge for researchers due to their volume and handwritten nature. The launch of GPT-4V in November 2023 offered a breakthrough, enabling large-scale, efficient transcription and analysis of these historical urban renewal documents.

Nicholas Kunz, H. Oliver Gao

Urban transit bus idling is a contributor to ecological stress, economic inefficiency, and medically hazardous health outcomes due to emissions. The global accumulation of this frequent pattern of undesirable driving behavior is enormous. In order to measure its scale, we propose GRD-TRT-BUF-4I (Ground Truth Buffer for Idling) an extensible, realtime detection system that records the geolocation and idling duration of urban transit bus fleets internationally. Using live vehicle locations from General Transit Feed Specification (GTFS) Realtime, the system detects approximately 200,000 idling events per day from over 50 cities across North America, Europe, Oceania, and Asia. This realtime data was created dynamically to serve operational decision-making and fleet management to reduce the frequency and duration of idling events as they occur, as well as to capture its accumulative effects. Civil and Transportation Engineers, Urban Planners, Epidemiologists, Policymakers, and other stakeholders might find this useful for emissions modeling, traffic management, route planning, and other urban sustainability efforts at a variety of geographic and temporal scales.

Bilicz Dávid

The aim of this paper is to measure whether the regions lagging behind in the European knowledge field could get closer to the core of the Framework Programmes’ (FP) R&D network.

Swati Mehta Dhawan, Julie Zollmann

Humanitarian actors touting financial inclusion posit that access to financial services builds refugees’ resilience and self-reliance. They claim that new digital financial tools create more efficient and dignified pathways for humanitarian assistance and enable refugees to better manage their savings and invest in livelihoods, especially during protracted displacement. Our in-depth, repeat interviews with refugees in Kenya and Jordan refute this narrative. Instead, self-reliance was hindered primarily by refugees’ lack of foundational rights to move and work. Financial services had limited ability to support livelihoods in the absence of those rights. The digital financial services offered to refugees under the banner of ‘financial inclusion’ were not mainstream services designed to empower and connect. Instead, they were segregated, second-class offerings meant to further isolate and limit refugee transactions in line with broader political desires to encamp and exclude them. The article raises questions about the circumstances in which humanitarian funding ought to fund financial service interventions and what those interventions are capable of achieving.

Sebastian Dembski

Book review

Kai Yin, Junwei Ma, Ali Mostafavi

Urban flood risk emerges from complex and nonlinear interactions among multiple features related to flood hazard, flood exposure, and social and physical vulnerabilities, along with the complex spatial flood dependence relationships. Existing approaches for characterizing urban flood risk, however, are primarily based on flood plain maps, focusing on a limited number of features, primarily hazard and exposure features, without consideration of feature interactions or the dependence relationships among spatial areas. To address this gap, this study presents an integrated urban flood-risk rating model based on a novel unsupervised graph deep learning model (called FloodRisk-Net). FloodRisk-Net is capable of capturing spatial dependence among areas and complex and nonlinear interactions among flood hazards and urban features for specifying emergent flood risk. Using data from multiple metropolitan statistical areas (MSAs) in the United States, the model characterizes their flood risk into six distinct city-specific levels. The model is interpretable and enables feature analysis of areas within each flood-risk level, allowing for the identification of the three archetypes shaping the highest flood risk within each MSA. Flood risk is found to be spatially distributed in a hierarchical structure within each MSA, where the core city disproportionately bears the highest flood risk. Multiple cities are found to have high overall flood-risk levels and low spatial inequality, indicating limited options for balancing urban development and flood-risk reduction. Relevant flood-risk reduction strategies are discussed considering ways that the highest flood risk and uneven spatial distribution of flood risk are formed.

Max Schrader, Navish Kumar, Nicolas Collignon et al.

Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating over half of urban van deliveries being replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. By modelling the relative delivery performance of different vehicle types across urban micro-regions, machine learning can help operators evaluate the business and environmental impact of adding cargo-bikes to their fleets. In this paper, we introduce two datasets, and present initial progress in modelling urban delivery service time (e.g. cruising for parking, unloading, walking). Using Uber's H3 index to divide the cities into hexagonal cells, and aggregating OpenStreetMap tags for each cell, we show that urban context is a critical predictor of delivery performance.

Chenyue Liu, Ali Mostafavi

This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence. Utilizing data from five Metropolitan Statistical Areas in the United States: Chicago, Dallas, Houston, Los Angeles, and New York, the study implemented an XGBoost machine learning model to predict the extent of cancer prevalence and evaluate the importance of different features. Our model demonstrates reliable performance, with results indicating that age, minority status, and population density are among the most influential factors in cancer prevalence. We further explore urban development and design strategies that could mitigate cancer prevalence, focusing on green space, developed areas, and total emissions. Through a series of experimental evaluations based on causal inference, the results show that increasing green space and reducing developed areas and total emissions could alleviate cancer prevalence. The study and findings contribute to a better understanding of the interplay among urban features and community health and also show the value of interpretable machine learning models for integrated urban design to promote public health. The findings also provide actionable insights for urban planning and design, emphasizing the need for a multifaceted approach to addressing urban health disparities through integrated urban design strategies.

Xin Li, Yanrong Li, Dongdong Lv et al.

Standard penetration test (SPT) is an important in situ measurement for field investigation of geotechnical and geological engineering. The drilling approaches for implementation of SPT can be classified into dry, wet, and water circulation drillings according to the amount of water used during drilling process. However, the influences of these drilling methods on the SPT results remain unclear, especially when being used in loess–paleosol sequence that is water sensitive. In this study, SPT tests were conducted in a typical loess stratum in the Loess Plateau of China. The difference of SPT N values under the above three drilling methods was compared together with the analysis of characteristics of samples from SPT sampler. The results showed that the N value exhibits positive correlation with dry density of the soil and negative correlation with moisture content. In shallow soil, the average N value under water circulation drilling was slightly higher than that of dry and wet drilling. This is because that the residual soil at the bottom of the drillhole caused by water circulation drilling provides additional penetration resistance. In deep soil, the difference of average N values among all three drilling methods was minimal although the structure of the samples from the SPT sampler differs from one another, indicating the determination of soil density on the SPT result. Empirical equations were proposed for the estimation of unconfined compressive strength of loess–paleosol sequence on the basis of SPT N values under the three drilling methods. Considering the efficiency of drilling and stability of SPT results, it is suggested that wet drilling is the most applicable method for implementation of SPT in the field investigation of loess–paleosol sequence.