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

Sean Elliott, Sohini Roy

Public transit is a critical component of urban mobility and equity, yet mobility and air-quality linkages are rarely operationalized in reproducible smart-city analytics workflows. This study develops a transparent, multi-source monitoring dataset that integrates agency-reported transit ridership with ambient fine particulate matter PM2.5 from the U.S. EPA Air Quality System (AQS) for four U.S. metropolitan areas - New York City, Chicago, Las Vegas, and Phoenix, using two seasonal snapshots (March and October 2024). We harmonize heterogeneous ridership feeds (daily and stop-level) to monthly system totals and pair them with monthly mean PM2.5 , reporting both absolute and per-capita metrics to enable cross-city comparability. Results show pronounced structural differences in transit scale and intensity, with consistent seasonal shifts in both ridership and PM2.5 that vary by urban context. A set of lightweight regression specifications is used as a descriptive sensitivity analysis, indicating that apparent mobility-PM2.5 relationships are not uniform across cities or seasons and are strongly shaped by baseline city effects. Overall, the paper positions integrated mobility and environment monitoring as a practical smart-city capability, offering a scalable framework for tracking infrastructure utilization alongside exposure-relevant air-quality indicators to support sustainable communities and public-health-aware urban resilience.

Rui Liu, Steven Jige Quan, Zhong-Ren Peng et al.

As cities evolve over time, challenges such as traffic congestion and functional imbalance increasingly necessitate urban renewal through efficient modification of existing plans, rather than complete re-planning. In practice, even minor urban changes require substantial manual effort to redraw geospatial layouts, slowing the iterative planning and decision-making procedure. Motivated by recent advances in agentic systems and multimodal reasoning, we formulate urban renewal as a machine-executable task that iteratively modifies existing urban plans represented in structured geospatial formats. More specifically, we represent urban layouts using GeoJSON and decompose natural-language editing instructions into hierarchical geometric intents spanning polygon-, line-, and point-level operations. To coordinate interdependent edits across spatial elements and abstraction levels, we propose a hierarchical agentic framework that jointly performs multi-level planning and execution with explicit propagation of intermediate spatial constraints. We further introduce an iterative execution-validation mechanism that mitigates error accumulation and enforces global spatial consistency during multi-step editing. Extensive experiments across diverse urban editing scenarios demonstrate significant improvements in efficiency, robustness, correctness, and spatial validity over existing baselines.

Sümeyye Kahraman, Burak Korkmazyurek, Erkan Polat

Lokacije avtobusnih postaj so ključne za učinkovito storitev potniškega prometa in trajnostno mobilnost v mestih. Določajo možnost dostopa obiskovalcev do mesta ter varnost, dostopnost in ekonomičnost javnega prevoza. Poleg tega vplivajo na dostop prebivalcev do delovnih mest, šol, zdravstvenih in drugih osnovnih storitev ter posledično na splošno družbenogospodarsko blaginjo mesta. V članku je določena optimalna lokacija nove avtobusne postaje v turškem mestu Uşak, za kar so avtorji uporabili hibridni model večkriterijskega odločanja, ki vključuje presojo dvajsetih kriterijev in celovito analizo dostopnosti lokacij na podlagi mnenj strokovnjakov ter upošteva raznovrstne potrebe prebivalcev in lokalnih oblasti. S postopkom analitične hierarhije so merilom pripisali uteži, na podlagi česar so nato z metodami MOOSRA, ARAS in VIKOR sedem možnih lokacij razvrstili po primernosti. Izsledki so pokazali, katera lokacija bi bila najprimernejša, hkrati pa so potrdili robustnost uporabljenega modela in njegovo uporabnost v urbanističnem načrtovanju

Felicia H. M. Liu, Karen P. Y. Lai, Bertrand Seah et al.

Abstract This paper critically assesses the complex interplay between urban transitions of digitisation and sustainability. Building on a mixed-method research design, we unpack the challenges of decarbonising digital infrastructure while attending to urban sustainability goals in a land- and water-scarce country facing significant physical climate risks. We identify transferrable lessons on the economic, technological, and environmental synergies and trade-offs behind data centre development and argue that stewarding the global data centre sector towards sustainability requires an ecosystem-wide approach. We identify implementation gaps across five key dimensions: technological innovation, policy and regulation, finance, infrastructure, and people. We find that the progress and uptake of sustainability initiatives are often impeded by risk-averse DC operators, who are most concerned with real and perceived risks of downtime. We conclude with recommendations for data centre stakeholders to align the low-carbon transition of the data centre sector with broader objectives of climate resilience, smart city development, and sustainable finance.

Joshua Dimasaka, Christian Geiß, Emily So

To understand our global progress for sustainable development and disaster risk reduction in many developing economies, two recent major initiatives - the Uniform African Exposure Dataset of the Global Earthquake Model (GEM) Foundation and the Modelling Exposure through Earth Observation Routines (METEOR) Project - implemented classical spatial disaggregation techniques to generate large-scale mapping of urban morphology using the information from various satellite imagery and its derivatives, geospatial datasets of the built environment, and subnational census statistics. However, the local discrepancy with well-validated census statistics and the propagated model uncertainties remain a challenge in such coarse-to-fine-grained mapping problems, specifically constrained by weak and conditional label supervision. Therefore, we present Deep Conditional Census-Constrained Clustering (DeepC4), a novel deep learning-based spatial disaggregation approach that incorporates local census statistics as cluster-level constraints while considering multiple conditional label relationships in a joint multitask learning of the patterns of satellite imagery. To demonstrate, compared to GEM and METEOR, we enhanced the quality of Rwandan maps of urban morphology, specifically building exposure and physical vulnerability, at the third-level administrative unit from the 2022 census. As the world approaches the conclusion of many global frameworks in 2030, our work offers a new deep learning-based mapping technique that explicitly encodes well-validated census and experts' belief systems to achieve an explainable and interpretable auditing of existing coarse-grained derived information at large scales.

Yatao Zhang, Ye Hong, Song Gao et al.

Understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities. However, their bidirectional causal relationships remain underexplored due to challenges of simultaneously inferring spatial heterogeneity, temporal variation, and feedback mechanisms. To address this gap, we propose a novel spatio-temporal causality framework that bridges correlation and causation by integrating spatio-temporal weighted regression with a newly developed spatio-temporal convergent cross-mapping approach. Characterizing cities through urban structure, form, and function, the framework uncovers bidirectional causal patterns between urban systems and traffic dynamics across 30 cities on six continents. Our findings reveal asymmetric bidirectional causality, with urban systems exerting stronger influences on traffic dynamics than the reverse in most cities. Urban form and function shape mobility more profoundly than structure, even though structure often exhibits higher correlations, as observed in cities such as Singapore, New Delhi, London, Chicago, and Moscow. This does not preclude the reversed causal direction, whereby long-established mobility patterns can also reshape the built environment over time. Finally, we identify three distinct causal archetypes: tightly coupled, pattern-heterogeneous, and workday-attenuated, which map pathways from causal diagnosis to intervention. This typology supports city-to-city learning and lays a foundation for context-sensitive strategies in sustainable urban and transport planning.

Andrea Musso, Diego Rybski, Dirk Helbing et al.

The share of the world population living in cities with more than one million people rose from 11% in 1975 to 24% in 2025 (our estimates). Will this trend towards greater concentration in large cities continue or level off? We introduce two new city population datasets that use consistent city definitions across countries and over time. The first covers the world between 1975 and 2025, using satellite imagery. The second covers the U.S. between 1850 and 2020, using census microdata. We find that urban growth follows a characteristic life cycle. In the early stages of a country's urbanization process, large cities grow faster than smaller ones. At later stages, growth rates equalize across sizes. We use this life cycle to project future population concentration in large cities. Our projections suggest that 38% of the world population will be living in cities with more than one million people by 2100. This estimate is higher than the 33% implied by the well-known theory of proportional growth, but lower than the 42% obtained by extrapolating current trends.

Mingbao Chen, Zhibin Xu

Cities are the frontiers of the Sustainable Development Goals (SDGs) adopted in the United Nations 2030 Agenda. Although quantitative methods have been applied to assess cities’ sustainability progress, knowledge gaps exist in the differences between inland and coastal cities’ performance and their internal variations against common standards. Using the Voronoi-based kaleidoscope diagram embedded in two circular plots, the article visualises the overall sustainability progress of China’s inland and coastal cities in economy, society, biosphere and partnership. By measuring overall progress with circular length and individual scores with kaleidoscope area size, triple inland-coastal gaps and trifold intracoastal inequalities were highlighted, as well as city types characterised by economy-society balance and land–sea relation. References for implementing sustainable development transformations for coastal cities were derived, along with the circular-kaleidoscope diagram’s potential for checking the pulse of cities’ performances in further uses and finishing the circle.

Seng W. Loke

Caring for the elderly, aging-in-place, and enabling the elderly to maintain a good life continue to be topics of increasing importance, especially in countries with a higher percentage of older people, as people live longer, and care-giving costs rise. This position paper proposes the concept of urban agetech, where agetech services beyond the home can be an integral part of a modern ageing-friendly city, and where support for the elderly, where needed, in the form of automated systems (e.g., robots and automated vehicles) would be a normal city function/service, akin to the rather commonplace public transport services today.

Keith Burghardt, Johannes H. Uhl, Kristina Lerman et al.

Despite the rapid growth of cities in the past century, our quantitative, in-depth understanding of how cities grow remains limited due to a consistent lack of historical data. Thus, the scaling laws between a city's features and its population as they evolve over time, known as temporal city scaling, is under-explored, especially for time periods spanning multiple decades. In this paper, we leverage novel data sources such as the Historical Settlement Data Compilation for the U.S. (HISDAC-US), and analyze the temporal scaling laws of developed area, building indoor area, building footprint area, and road length and other road network statistics for nearly all metropolitan areas in the U.S. from 1900 to 2015. We find that scaling exponents vary dramatically between cities as a function of their size and location. Three notable patterns emerge. First, scaling law exponents imply many, but not all, metropolitan areas are becoming less dense and indoor area per capita increases as cities grow, in contrast to expectations. Second, larger cities tend to have a smaller scaling exponent than smaller cities. Third, scaling exponents (and growth patterns) are similar between nearby cities. These results show a long-term trend that could harm urban sustainability as previously dense populations are rapidly spreading out into undeveloped land. Moreover, the regional similarity of long-term urban growth patterns implies that city evolution and sustainability patterns are more interconnected than prior research has suggested. These results help urban planners and scientists understand universal, long-term patterns of city growth across the US.

Zhenhui Li, Hongwei Zhang, Kan Wu

Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data. One such challenge is the need to uncover universal urban patterns, such as the urban scaling law, across thousands of cities worldwide. In this study, we propose a novel large-scale geospatial data processing system that enables city analysis on an unprecedented scale. We demonstrate the system's capabilities by revisiting the urban scaling law across 21,280 cities globally, using a range of open-source datasets including road networks, nighttime light intensity, built-up areas, and population statistics. Analyzing the characteristics of 21,280 cities involves querying over half a billion geospatial data points, a task that traditional Geographic Information Systems (GIS) would take several days to process. In contrast, our cloud-based system accelerates the analysis, reducing processing time to just minutes while significantly lowering resource consumption. Our findings reveal that the urban scaling law varies across cities in under-developed, developing, and developed regions, extending the insights gained from previous studies focused on hundreds of cities. This underscores the critical importance of cloud-based big data processing for efficient, large-scale geospatial analysis. As the availability of satellite imagery and other global datasets continues to grow, the potential for scientific discovery expands exponentially. Our approach not only demonstrates how such large-scale tasks can be executed efficiently but also offers a powerful solution for data scientists and researchers working in the fields of city and geospatial science.

Kennedy Machira, Wisdom Richard Mgomezulu, Mark Malata

Poverty alleviation remains one of the ancient goals of Malawi as the country has since 1994 adopted a poverty alleviation strategy throughout its developmental programs. Through the support of the World Bank, a poverty monitoring system was put in place whose data are collected through the Living Standards Measurement Surveys (LSMS). However, since the establishment of the LSMS, findings of different assessments and eras have revealed instabilities in the country’s poverty levels overtime. What remains unclear is whether households have been able to move out of poverty or not. The current study employed a two wave LSMS panel of 2016 and 2019 and assessed poverty dimensions including poverty incidence, depth and severity. The study further assessed the determinants of poverty transitions in order to understand movements in and out of poverty. Household size, gender of household head, education level of the household head, agricultural land holding sizes, access to credit, residence (urban or rural) and expected shocks significantly influenced the poverty dimensions and poverty transition. It is hence imperative that proper strategies that embrace robust and sustainable credit systems, improvement in literacy levels of the Malawian population, and further improving agricultural land productivity can help reduce poverty and further move households out of poverty. Such initiatives should take into consideration the gender divide and the rapid population growth faced by the country.

Thomas Stringer, Monserrat Ramírez-Melgarejo

The COVID-19 pandemic prompted global supply chain upheavals, triggering shortages and delays. Governments and companies sought resilient strategies for future crises. A US response was "nearshoring," shifting manufacturing from China to Mexico. Analyzing trade data from 2019 to 2023, this study examines if this shift occurred and its sectoral impact. Both countries initially rebounded post-Q1 2020 disruptions. However, China's exports waned, while Mexico's surged, surpassing China by March 2023. Sectors like machinery and electrical components showed similar trends. Mexico excelled in US supply, while China's dominance eroded, affirming the nearshoring hypothesis. Proximity significantly bolstered long-term supply chain resilience.

Xinke Huang, Yuqin Jiang, Ali Mostafavi

Understanding the relationship between spatial structures of cities and environmental hazard exposures (such as urban heat) is essential for urban health and sustainability planning. However, a critical knowledge gap exists in terms of the extent to which socio-spatial networks shaped by human mobility exacerbate or alleviate urban heat exposures of populations in cities. In this study, we utilize location-based data to construct human mobility networks in twenty metropolitan areas in the U.S. The human mobility networks are analyzed in conjunction with the urban heat characteristics of spatial areas. We identify areas with high and low urban heat exposure and evaluate visitation patterns of populations residing in high and low urban heat areas to other spatial areas with similar and dissimilar urban heat exposure. The results reveal the presence of urban heat traps in the majority of the studied metropolitan areas in which populations residing in high heat exposure areas primarily visit areas with high heat exposure. The results also show a small percentage of human mobility to produce urban heat escalate (visitations from low heat areas to high heat areas) and heat escapes (movements from high heat areas to low heat areas). The findings from this study provide a better understanding of urban heat exposure in cities based on patterns of human mobility. These finding contribute to a broader understanding of the intersection of human network dynamics and environmental hazard exposures in cities to inform more integrated urban design and planning to promote health and sustainability.

Fan Zhang, Arianna Salazar Miranda, Fábio Duarte et al.

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Harsh G. Kamath, Manmeet Singh, Neetiraj Malviya et al.

We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six US cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km^2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset's effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85 C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Timothy O. Adekunle

This research discusses thermal indices and outdoor comfort before and during the Coronavirus Disease of 2019 (COVID-19) pandemic in three counties in Connecticut (41.6032°N, 73.0877°W), United States. The counties are Fairfield, Hartford, and New Haven. Existing research noted that people residing in highly populated urban and low-income areas are disproportionately affected by the pandemic and subject to health, heat, and cold stress-related problems. As a result, the study is motivated to examine outdoor comfort and thermal indices in the counties that account for over 75% of the population in the state. The specific aim of the study is to examine outdoor comfort and thermal indices a year before and during the pandemic to determine if the pandemic significantly affects outdoor occupants and their overall well-being. Due to lesser activities observed during the pandemic than before the pandemic, the research questions include 1) Does the pandemic year provide a more comfortable thermal environment for outdoor occupants than the period before the pandemic? 2) Does the period provide a cleaner environment with no thermal or cold stress to occupants than before the pandemic? The research approaches include the field data recorded in 2019 and 2020. The research also utilized observations and mathematical models. The findings revealed that the mean monthly temperatures varied from −3.2°C to 25.2°C and relative humidity ranged from and 62.6–70.7%. The study revealed cold stress in wintertime, especially in Fairfield. Heat stress is also noted in summertime across the counties. New Haven is more prone to heat stress than other counties because of some factors (such as climate change, lesser land area, higher incidence from solar radiation, etc.). Higher thermal indices are reported in 2020 (during the pandemic) than the indices computed for 2019 (pre-pandemic) which could influence thermal comfort, health, and well-being of people. The indices are strongly influenced by outdoor temperatures and dew-point. A combination of some environmental variables such as temperature and wind speed also have significant effects on the indices. The study recommends that the use of clean energy for running infrastructure systems would help in mitigating the impact of climate change in various locations. The investigation suggests that a thorough evaluation of environmental conditions and interventions should be explored for developing resilience to emergencies in cities and urban areas. The research outcomes provide useful information for designers, planners, stakeholders, policymakers, etc., to develop pathways for achieving resilient zero-carbon cities in various places.

Ivanović-Vojvodić Jelena, Vojvodić Goran

Krystian Banet, Rafal Kucharski, Vitalii Naumov

We demonstrate how digital traces of city-bike trips may become useful to identify urban space attractiveness. We exploit their unique feature - stopovers: short, non traffic-related stops made by cyclists during their trips. As we demonstrate on the case-study of Krakow (Poland), when applied to a big dataset, meaningful patterns appear, with hotspots (places with long and frequent stopovers) identified at both the top tourist and leisure attractions as well as emerging new places. We propose a generic method, applicable to any spatiotemporal city-bike traces, providing results meaningful to understand both the general urban space attractiveness and its dynamics. With the proposed filtering (to mitigate a selection bias) and empirical cross-validation (to rule-out false-positive classifications) results effectively reveal spatial patterns of urban attractiveness. Valuable for decision-makers and analysts to enhance understanding of urban space consumption patterns by tourists and residents.

Jungheum Park, Hyunji Chung

In smart cities built on information and communication technology, citizens and various IT systems interoperate in harmony. Cloud computing and Internet-of-Things technologies that have been developed for a long time are making modern cities smarter. Smart cities can have a positive impact on citizens, but they can also make cities dangerous. Today, with the emerging reality of smart cities, this paper looks at both the bright and dark sides and provides a foundation for supporting work-related tasks of IT professionals as well as non-IT experts involved in urban design and development.