Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility demand. Conventional global regression and time-series models cannot simultaneously capture these multi-scale dynamics across multiple travel modes. This study proposes a GeoAI Hybrid analytical framework that sequentially integrates Multiscale Geographically Weighted Regression (MGWR), Random Forest (RF), and Spatio-Temporal Graph Convolutional Networks (ST-GCN) to model the spatiotemporal heterogeneity of traffic flow patterns and their interaction with land use across three mobility modes: motor vehicle, public transit, and active transport. Applying the framework to an empirically calibrated dataset of 350 traffic analysis zones across six cities spanning two contrasting urban morphologies, four key findings emerge: (i) the GeoAI Hybrid achieves a root mean squared error (RMSE) of 0.119 and an R^2 of 0.891, outperforming all benchmarks by 23-62%; (ii) SHAP analysis identifies land use mix as the strongest predictor for motor vehicle flows and transit stop density as the strongest predictor for public transit; (iii) DBSCAN clustering identifies five functionally distinct urban traffic typologies with a silhouette score of 0.71, and GeoAI Hybrid residuals exhibit Moran's I=0.218 (p<0.001), a 72% reduction relative to OLS baselines; and (iv) cross-city transfer experiments reveal moderate within-cluster transferability (R^2>=0.78) and limited cross-cluster generalisability, underscoring the primacy of urban morphological context. The framework offers planners and transportation engineers an interpretable, scalable toolkit for evidence-based multimodal mobility management and land use policy design.
Vincent van Vliet, Menno van den Hout, Eduward Tangdiongga
et al.
Free-space optical communication links can enable high-capacity wireless connectivity in urban areas. We discuss the feasibility, challenges, and recent developments for high-capacity urban free-space optical links at kilometer scale.
Marta Bottero, Caterina Caprioli, Marcus Foth
et al.
Increasing concerns about sustainable development and climate change have pushed public and private actors and organisations to intensify their efforts to embed these issues in their plans, projects, programmes, and strategies. Within this context, the article examines sustainability as it relates to the Olympic Games, providing an overview of the measures taken by the International Olympic Committee (IOC) and the attention that these events' organisers have paid to integrating it over time. A comparative analysis of two cases – the 2006 Winter Olympic Games in Turin, Italy, and the 2032 Summer Olympic Games in Brisbane, Australia – has been developed to identify specificities and opportunities in staging such a global event, examining diverse and interconnected aspects of sustainability that could help evaluate it in future games. The article highlights that interpretations of the meaning of sustainability change over time and vary across stakeholders and that the long-term impact assessment of the legacy of such events requires further research.
While frameworks such as the WHO Age-Friendly Cities have advanced urban aging policy, rural contexts demand fundamentally different analytical approaches. The spatial dispersion, terrain variability, and agricultural labor dependencies that characterize rural aging experiences require moving beyond service-domain frameworks toward spatial stress assessment models. Current research on rural aging in China exhibits methodological gaps, systematically underrepresenting the spatial stressors that older adults face daily, including terrain barriers, infrastructure limitations, climate exposure, and agricultural labor burdens. Existing rural revitalization policies emphasize standardized interventions while inadequately addressing spatial heterogeneity and the spatially-differentiated needs of aging populations. This study developed a GIS-based spatial stress analysis framework that applies Lawton and Nahemow's competence-press model to quantify aging-related stressors and classify rural villages by intervention needs. Using data from 27 villages in Mamuchi Township, Shandong Province, we established four spatial stress indicators: slope gradient index (SGI), solar radiation exposure index (SREI), walkability index (WI), and agricultural intensity index (AII). Analysis of variance and hierarchical clustering revealed significant variation in spatial pressures across villages and identified distinct typologies that require targeted intervention strategies. The framework produces both quantitative stress measurements for individual villages and a classification system that groups villages with similar stress patterns, providing planners and policymakers with practical tools for designing spatially-targeted age-friendly interventions in rural China and similar contexts.
This paper investigates the relationship between smart city initiatives and evolving urbanization trends in the United States. The research addresses the critical issue of rapid urban growth in the U.S. and explores how innovations within the smart city paradigm influence urban development. Utilizing principles from Urban Complexity Theory, this study identifies four key variables relevant to smart cities and their impact on urbanization: smart city technology, government policy, environmental sustainability, and socioeconomic factors. A mixed-method approach, combining quantitative and qualitative methodologies, was employed. A web-based survey (n=50) utilizing a five-point Likert scale was conducted among residents of Manhattan, New York, and Capitol Hill, Seattle. Results indicate that the implementation of smart city technologies is significantly associated with shifts in population density, land use diversification, and enhanced infrastructure dynamics. Additionally, residents demonstrated preferences for smart cities based on efficient urban mobility, environmental sustainability, and personal socioeconomic improvements. The findings highlight essential considerations for urban planners, policymakers, and employers. This study concludes that incorporating the identified influential factors into strategic urban planning optimizes city development to better accommodate growing urban populations.
Ludmila Cardoso Fagundes Mendes, Roberta Vieira Gonçalves de Souza, Danielly Marcianny Silva Eulário
É fundamental criar ambientes hospitalares acessíveis, acolhedores e confortáveis. Infere-se que arquitetos, designers e engenheiros exercem um papel crucial durante o ciclo de vida da edificação hospitalar. No entanto, entende-se que a participação dos demais usuários do ambiente hospitalar é importante também na idealização de mudanças no espaço físico. Esta pesquisa teve como objetivo, examinar a perspectiva de profissionais influentes no planejamento de edifícios hospitalares quanto à introdução de estímulos no ambiente físico. Indicadores de bem-estar foram selecionados a partir das Teorias do Design de Suporte e do Design Baseado em Evidências, tendo sido aplicado um questionário versando sobre Senso de Controle, Apoio Social, Distrações Positivas e Iluminação Natural. Participaram 96 arquitetos, designers, engenheiros, gestores hospitalares, médicos, enfermeiros e outros profissionais da área. As respostas indicaram que, para a maioria dos respondentes, o ambiente físico interfere muito no bem-estar de todos os tipos de usuário de Estabelecimentos Assistenciais de Saúde (EAS) sendo a presença de iluminação natural o componente considerado mais relevante para seu bem-estar. Segundo os profissionais, os indicadores de bem-estar têm relevância superior para pacientes internados em enfermarias e para funcionários da assistência em regime de plantão igual ou superior a 12h, e menor relevância para pacientes não internados. Preservar a privacidade dos pacientes e disponibilizar iluminação natural em quartos e em enfermarias foram os indicadores de bem-estar mais priorizados pelos participantes da pesquisa. De modo geral, os projetistas tendem a priorizar mais os indicadores em novas edificações de EAS do que em reformas ou ampliações.
Architecture, Urban groups. The city. Urban sociology
Saiful Anwar, Ega Rusanti, Dewi Rahmawati Maulidiyah
This study aims to examine whether the adoption of the Global Reporting Initiative (GRI) Standards enhances carbon emission disclosure among banks in Indonesia. Furthermore, it provides empirical evidence that the presence of women on boards moderates the relationship between GRI adoption and carbon emission disclosure. The study was conducted on 40 conventional and Islamic banks listed on the Indonesia Stock Exchange (IDX) during the period 2015–2021. The analysis employs Ordinary Least Squares (OLS) regression, with robustness tests conducted using alternative measurement variables to ensure the consistency of the results. The findings consistently demonstrate that the adoption of GRI Standards positively influences carbon emission disclosure in Indonesian banks. The presence of women on boards promotes banks’ engagement in global climate change agendas, aligning with the implementation of Sustainable Development Goals (SDGs) 5, 8, and 13. This study reinforces stakeholder theory and Critical Mass Theory, indicating that a minimum threshold of female board members is necessary to influence strategic decisions, particularly in encouraging voluntary disclosures such as carbon emission reporting. Notably, the study also finds that carbon emission disclosure is valued by banking stakeholders in Indonesia. Therefore, policymakers are encouraged to establish regulations that mandate GRI adoption and ensure a minimum representation of women in strategic decision-making positions within the banking sector.
Economics as a science, Regional economics. Space in economics
Andrea Ballatore, Scott Rodgers, Liam McLoughlin
et al.
This paper investigates the geography of Facebook use at an urban-regional scale, focussing on place-named groups, meaning various interest groups with names relating to places such as towns, neighbourhoods, or points of interest. Conceptualising Facebook as a digital infrastructure – that is, the platform’s urban footprint, in the form of its place-named groups, rather than what individuals share and create using the service – we explore the location, theme, and scale of 3016 groups relating to places in Greater London. Firstly, we address the quantitative and qualitative methodological challenges that we faced to identify the groups and ground them geographically. Secondly, we analyse the scale of the toponyms in the group names, which are predominantly linked to London’s suburbs. Thirdly, we study the spatial distribution of groups, both overall and by specific types, in relation to the socio-demographic characteristics of residents at the borough level. Through correlation and robust regression analyses, the presence and activity of groups are linked to a relatively older, non-deprived, and non-immigrant population living in less dense areas, with high variability across different group types. These results portray place-named Facebook groups as communication infrastructure skewed towards more banal interactions and places in Greater London’s outlying boroughs. This research is among the first to explore and visualise the urban geographies of Facebook groups at a metropolitan scale, showing the extent, nature, and locational tendencies of large-scale social media use as increasingly ordinary aspects of how people come to know, experience, live, and work in cities.
Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352°K and MAE of 0.215°K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (AI) models have demonstrated their unique values in data and code generation. This survey paper aims to explore the innovative integration of generative AI techniques and urban digital twins to address challenges in the realm of smart cities in various urban sectors, such as transportation and mobility management, energy system operations, building and infrastructure management, and urban design. The survey starts with the introduction of popular generative AI models with their application areas, followed by a structured review of the existing urban science applications that leverage the autonomous capability of the generative AI techniques to facilitate (a) data augmentation for promoting urban monitoring and predictive analytics, (b) synthetic data and scenario generation, (c) automated 3D city modeling, and (d) generative urban design and optimization. Based on the review, this survey discusses potential opportunities and technical strategies that integrate generative AI models into the next-generation urban digital twins for more reliable, scalable, and automated management of smart cities.
The introduction of information and communication technology (ICT) has altered the way society operates things. ICT is used in various sectors, including agriculture. It can be used in the agricultural sector to distribute pricing and encourage agricultural commodity exports. The study aims to investigate the effect of ICT on cashew nut export in Benin using an autoregressive distributed lag (ARDL) approach. Data were collected over the period of 31 years (1990–2020) in Benin. The estimated results showed that mobile cellular telephone subscription is negatively and significantly correlated with cashew nut export in the short-run. However, in the long-run, it exhibits a positive and significant correlation. On the other hand, internet usage had no significant effect on cashew nut export in the short-run, but negatively influenced cashew nut export in the long-run. These results suggest that to increase its cashew nut export, the Beninese government should invest in technological infrastructure to improve internet access by reducing the cost of internet and increasing education that will allow farmers to better understand and use ICT.
Cities. Urban geography, Urbanization. City and country
Street networks are ubiquitous components of cities, guiding their development and enabling movement from place to place; street networks are also the critical components of many urban analytical methods. However, their graph representation is often designed primarily for transportation purposes. This representation is less suitable for other use cases where transportation networks need to be simplified as a mandatory pre-processing step, e.g., in the case of morphological analysis, visual navigation, or drone flight routing. While the urgent demand for automated pre-processing methods comes from various fields, it is still an unsolved challenge. In this article, we tackle this challenge by proposing a cheap computational heuristic for the identification of "face artifacts", i.e., geometries that are enclosed by transportation edges but do not represent urban blocks. The heuristic is based on combining the frequency distributions of shape compactness metrics and area measurements of street network face polygons. We test our method on 131 globally sampled large cities and show that it successfully identifies face artifacts in 89\% of analyzed cities. Our heuristic of detecting artifacts caused by data being collected for another purpose is the first step towards an automated street network simplification workflow. Moreover, the proposed face artifact index uncovers differences in structural rules guiding the development of cities in different world regions.
Data and models of non-motorized traffic on multiuse urban trails are needed to improve planning and management of urban transportation systems. Negative binomial regression models are appropriate and useful when dependent variables are non-negative integers with over-dispersion like traffic counts. This paper presents eight negative binomial models for estimating urban trail traffic using 1,898 daily mixed-mode traffic counts from active infrared monitors at six locations in Minneapolis, MN. Our models include up to 10 independent variables that represent socio-demographic, built environment, weather, and temporal characteristics. A general model can be used to estimate traffic at locations where traffic has not been monitored. A six-location model with dummy variables for each monitoring site rather than neighborhood specific variables can be used to estimate traffic at existing locations when counts from monitors are not available. Six trail-specific models are appropriate for estimating variation in traffic in response to variations in weather and day of week. Validation results indicate negative binomial models outperform models estimated by ordinary least squares regression. These new models estimate traffic within approximately 16.3% error, on average, which is reasonable for planning and management purposes.
Voluntary HIV counselling and testing (VHCT) is a successful intervention for fostering early HIV detection, which is essential for the management of the disease. This study sought to determine the prevalence and factors that influence the utilization of VHCT services among young people. In this study, young people in the Tema Metropolis were cross-sectionally surveyed. The simple random sampling method was used to select the participants. The majority (60%) of the participants were unaware of VHCT, and 83% did not know of any VHCT centre. The majority (72%) of the participants indicated that VHCT was important, and 81% were willing to test for HIV if VHCT services were available in schools. Young people who knew that parental consent was not a requirement during VHCT were about two times more likely to have been tested for HIV (COR = 1.96; 95% CI: 1.05–3.63) compared to their counterparts. Additionally, young people who were willing to test in youth-friendly clinics (AOR = 2.87; 95% CI: 1.09–7.51) had higher odds of testing for HIV compared to their counterparts. The utilisation of VHCT services among young people in Tema was found to be very low. Additionally, young people’s knowledge of VHCT services was below expectations.
Exploring the spatial variations and the impact of spatial and social factors on commuting behavior is vital to promote cities’ sustainable development and improve residents’ daily lives. Based on 2015 1% Population Sample Survey data in Shanghai, this study constructs an improved accessibility index to evaluate the jobs–housing spatial relationship and compares spatial variations and factors of commuting duration and commuting distance at the sub-district level by using spatial autocorrelation analysis, spatial lag model, and spatial error model. In terms of spatial variations, the median commuting distance and commuting duration are 6.32 km and 28.37 min, respectively. Both of them have significant spatial autocorrelation, and the latter is higher. The high–high agglomeration areas of commuting distance scatter between the outer ring road and the outer suburbs. The high–high agglomeration areas of commuting duration are mainly distributed between the middle and the outer ring roads. In terms of affecting factors, the impacts of social factors on the commuting level are more significant than spatial factors. Ignoring the former will overestimate the effects of the latter. Commuting distance is more significantly correlated with spatial factors, and job accessibility is the most critical factor, while commuting duration is more significantly associated with social factors, and education level is the essential factor. There is significant intra-urban heterogeneity and spatial autocorrelation of commuting distance and duration in the metropolis. Social factors are more influential than spatial factors on commuting behavior.
Engineering (General). Civil engineering (General), City planning
This paper examines two aspects of the digitalization of finance in India, which has been surprisingly robust and fast growing. Much of this success is due to the government's introduction of a biometric identity known as Aadhaar. Because financial transactions require definitive proof of identity of the transacting parties, Aadhaar provides this proof without any document via biometric verification, thus greatly facilitating electronic transactions. The paper also documents the impact of digital financialization by showing examples of entrepreneurship, both directly in the fintech space through the creation of new web-based services as well as the rise of new businesses that make use of the fintech infrastructure that has been created.
In recent times, with the main purpose of enhancing public health, proper management of household solid waste in low-income residential areas of urban centres has gained special attention among scholars in developing countries. From the sociological perspective, perception is one of the key determinants of the people’s behaviour in their society. Therefore this study examined the perception of people about solid waste management and identified the key factors influencing this perception. The study was carried out in the first quarters of 2021 and covered Ife Central Local Government Area of Osun State, South-West Nigeria and adopted cross-sectional research design. Purposive sampling method was used to select 5 electoral wards in the Local Government Area where heaps of dirt was more common and 30 households nearer to the heap in each of the wards respectively. The total sample size of 150 used in the study mainly composed the eldest and active female of each the selected household. Descriptive statistics was used to analyze the primary data gathered. This study found that the residents were aware of the health-related challenges resulted from improper management of solid waste and were interested in reducing household solid waste generated. However, methods, the cost, lack of cooperation and nonchalant attitude of people were among the major potential factors militating against achieving this.
How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of available information. Using state of the art machine learning techniques and thousands of geotagged satellite and perspective images from diverse urban cultures, this research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Indexes.
Sustainable roofs, such as those with greenery and photovoltaic panels, contribute to the roadmap for reducing the carbon footprint of cities. However, research on sustainable urban roofscapes is rather focused on their potential and it is hindered by the scarcity of data, limiting our understanding of their current content, spatial distribution, and temporal evolution. To tackle this issue, we introduce Roofpedia, a set of three contributions: (i) automatic mapping of relevant urban roof typology from satellite imagery; (ii) an open roof registry mapping the spatial distribution and area of solar and green roofs of more than one million buildings across 17 cities; and (iii) the Roofpedia Index, a derivative of the registry, to benchmark the cities by the extent of sustainable roofscape in term of solar and green roof penetration. This project, partly inspired by its street greenery counterpart `Treepedia', is made possible by a multi-step pipeline that combines deep learning and geospatial techniques, demonstrating the feasibility of an automated methodology that generalises successfully across cities with an accuracy of detecting sustainable roofs of up to 100% in some cities. We offer our results as an interactive map and open dataset so that our work could aid researchers, local governments, and the public to uncover the pattern of sustainable rooftops across cities, track and monitor the current use of rooftops, complement studies on their potential, evaluate the effectiveness of existing incentives, verify the use of subsidies and fulfilment of climate pledges, estimate carbon offset capacities of cities, and ultimately support better policies and strategies to increase the adoption of instruments contributing to the sustainable development of cities.