Search Results (445)

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article

In earthquake-prone areas such as Tokyo, accurate estimation of bearing stratum depth is crucial for foundation design, liquefaction assessment, and urban disaster mitigation. However, traditional methods such as the standard penetration test (SPT), while reliable, are labor-intensive and have limited spatial distribution. In this study, 942 geological survey records from the Tokyo metropolitan area were used to evaluate the performance of three machine learning algorithms, random forest (RF), artificial neural network (ANN), and support vector machine (SVM), in predicting bearing stratum depth. The main input variables included geographic coordinates, elevation, and stratigraphic category. The results showed that the RF model performed well in terms of multiple evaluation indicators and had significantly better prediction accuracy than ANN and SVM. In addition, data density analysis showed that the prediction error was significantly reduced in high-density areas. The results demonstrate the robustness and adaptability of the RF method in foundation soil layer identification, emphasizing the importance of comprehensive input variables and spatial coverage. The proposed method can be used for large-scale, data-driven bearing stratum prediction and has the potential to be integrated into geological risk management systems and smart city platforms. Full article

This work presents a parallel agent-based framework designed to analyze the dynamics of vegetable trade within a metropolitan area. The system integrates agent-based and discrete event techniques to capture the complex interactions among farmers, vendors, and consumers in urban agricultural supply chains. Decision-making processes are modeled in detail: farmers select crops based on market trends and environmental risks, while vendors and consumers adapt their purchasing behavior according to seasonality, prices, and availability. To efficiently handle the computational demands of large-scale scenarios, we adopt an optimistic approximate parallel execution strategy. Furthermore, we introduce a credit-based load balancing mechanism that mitigates the effects of heterogeneous communication patterns and improves scalability. This framework enables detailed analysis of food distribution systems in urban contexts, offering insights relevant to smart cities and digital agriculture initiatives. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article

Air quality (AQ) modeling is at the forefront of estimating pollution levels in areas where the spatial representativity is low. Large metropolitan areas in Asia such as Beijing face significant pollution issues due to rapid industrialization and urbanization. AQ nowcasting, especially in dense urban centers like Beijing, is crucial for public health and safety. One of the most popular and accurate modeling methodologies relies on black-box models that fail to explain the phenomena in an interpretable way. This study investigates the performance and interpretability of Explainable AI (XAI) applied with the eXtreme Gradient Boosting (XGBoost) algorithm employing the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-Agnostic Explanations (LIME) for PM_2.5 nowcasting. Using a SHAP-based technique for dimensionality reduction, we identified the features responsible for 95% of the target variance, allowing us to perform an effective feature selection with minimal impact on accuracy. In addition, the findings show that SHAP and LIME supported orthogonal insights: SHAP provided a view of the model performance at a high level, identifying interaction effects that are often overlooked using gain-based metrics such as feature importance; while LIME presented an enhanced overlook by justifying its local explanation, providing low-bias estimates of the environmental data values that affect predictions. Our evaluation set included 12 monitoring stations using temporal split methods with or without lagged-feature engineering approaches. Moreover, the evaluation showed that models retained a substantial degree of predictive power (R² > 0.93) even in a reduced complexity size. The findings provide evidence for deploying interpretable and performant AQ modeling tools where policy interventions cannot solely depend on predictive analytics tools. Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes. Full article

Environmental consciousness has become increasingly important in the professional sports industry as it often hosts large-scale events that have significant environmental impacts. While the economic benefits of locating stadiums in city centers have been discussed, especially in terms of neighborhood revitalization, there has been limited empirical research on whether stadium relocation affects the transportation choices of spectators and reduces carbon dioxide emissions. Through a case study of a Japanese professional football club that relocated its home stadium from the suburb to the city center, this study quantitatively elucidated the change in spectators’ transportation choices and resulting reductions in carbon emissions achieved by the stadium relocation. Analysis indicated variations in behavioral changes among groups based on their loyalty levels to the club. It also highlighted the varying influence of the different residential areas within the metropolitan area on the modal choice. This study demonstrates the potential contribution of stadium relocation to sustainable urban planning by providing empirical evidence of these behavioral changes and policy implications for restructuring the urban public transportation network. Full article

Human T-lymphotropic virus 1 (HTLV-1) infection has been associated with inflammatory, autoimmune, and lymphoproliferative diseases with a wide spectrum of clinical manifestations. Among patients with inflammatory rheumatological disease manifestations, cases of rheumatoid arthritis, Sjögren’s syndrome, polymyositis, and fibromyalgia, among others, have been reported. Another common feature of rheumatological diseases is the presence of joint manifestations, such as arthralgia and arthritis. In the present study, we sought to determine the laboratory profile and clinical rheumatological manifestations of people living with HTLV-1/2 residing in a metropolitan area in the Brazilian Amazon. A total of 957 individuals were screened for HTLV-1/2 infection by enzyme-linked immunosorbent assay (ELISA), and samples from seropositive individuals were subjected to infection confirmation by Western blotting or quantitative polymerase chain reaction (qPCR). Individuals with confirmed HTLV-1 and HTLV-2 infection were clinically evaluated for signs and symptoms of rheumatological diseases. Of the 957 individuals tested, 69 were positive for HTLV-1/2 infection, with 56 confirmed cases of HTLV-1 infection (5.9%), 12 of HTLV-2 infection (1.2%), and 1 classified as undetermined (0.1%). After clinical screening, 15 infected individuals with complaints suggestive of rheumatological disease were selected for evaluation by a rheumatologist (11 with HTLV-1 infection (1.1%) and 4 with HTLV-2 infection (0.4%)). The predominant pain pattern was symmetrical polyarthralgia, with large joints predominantly being affected. The diseases diagnosed were psoriatic arthritis, osteoarthritis, fibromyalgia, and regional pain syndromes. Antinuclear antibody (ANA) positivity was observed in two patients. Our findings confirm that HTLV-1 infection is associated with rheumatological disease manifestations and highlight the novel finding of cases of HTLV-2 infection in patients with rheumatoid arthritis symptoms. Full article

International industrial transfer has driven rapid construction land expansion in emerging metropolitan areas, posing challenges for sustainable land management. However, existing research has largely overlooked the spatiotemporal patterns and driving mechanisms of this expansion, particularly in Southeast Asian metropolitan regions. To address this gap, we focused on the Ho Chi Minh City metropolitan area, utilizing construction land data from GLC_FCS30D to analyze the dynamics of construction land expansion during this period. Findings indicated that: (1) Continuous expansion of construction land, with the expansion rate during 2010–2020 being five times that of 2000–2010; (2) The spatial pattern evolved from initial infilling development in urban cores to subsequent leapfrogging and edge expansion toward peripheral counties and transportation corridors; (3) The expansion of construction land occurred alongside substantial losses of wetland and cultivated land. Between 2000 and 2020, the conversion of cultivated land to construction land increased significantly, particularly during 2010–2020 when cultivated land conversion accounted for 93.76% of newly developed construction land. Wetland conversion also showed notable growth during this period, comprising 3.86% of total newly added construction land; (4) Foreign direct investment (FDI) served as the primary catalyst, while industrial park development and transport infrastructure projects functioned as secondary accelerants. This study constructed a framework to systematically analyze the global and local driving mechanisms of metropolitan land expansion. The findings deepen the understanding of land-use transitions in emerging countries and provide both theoretical support and policy references for sustainable land management. Full article

Notwithstanding the Korean Urban Regeneration Act 2013’s support for sustainable neighborhood regeneration programs, the number and scale of such projects relative to large-scale urban redevelopment remain limited. To address this imbalance, this research advances existing form-based approaches through a multi-scalar morphological analysis encouraging harmonized urban transformation and sustainable urban regeneration. The analysis encompasses the macroscale (metropolitan area development), mesoscale (urban characterization of the central urban area), and microscale (aging urban fabric detailed analysis). The case study focuses on Daegu, a major Korean city experiencing population decline. Mappings and quantitative and qualitative analysis used Geographic Information System QGIS, as well as the Python suite Momepy. The study revealed that large-scale urban redevelopments are driving urban densification and demographic shifts. While older low-rise structures occupy most of the urban landscape in the central city area, piecemeal high-rise redevelopment is increasingly fragmenting it. The overly fine urban grain resists regeneration, limiting car access, building scales, and urban density. The research findings help identify the urban areas that are most appropriate for urban regeneration and redevelopment projects and streamline and coordinate planning efforts and the adjusting of regulations. The method developed is transferable to other Korean and international cities, fostering sustainable urban regeneration. Full article

This study investigates the impact of a thermal power plant site on air quality in Bucharest, Romania. It emphasizes the importance of accurate air pollutant inmission measurements in urban areas by utilizing mobile measurements of low-cost sensors, Copernicus’ Copernicus Atmosphere Monitoring Service (CAMS) and Copernicus Land Monitoring Service (CLMS), and satellite retrieval to better understand climate change drivers and their potential impact on near- surface concentrations and column densities of ${\mathrm{NO}}_2$, CO, and PM (particulate matter). It focuses the attention on the need of considering the placement of power plants in relation to metropolitan areas while making this assessment. The research highlights the limits of typical mesoscale air quality models in effectively capturing pollution dispersion and distribution using LUR (Land Use Regressions) retrievals. The authors investigate a variety of ways to better understand air pollution in metropolitan areas, including satellite observations, mobile measurements, and land use regression models. The study focuses largely on Bucharest, the capital of Romania, which has air pollution issues caused by vehicle traffic, industrial activity, heating systems, and power plants. The results indicate how the placement of a power plant may affects air quality in the nearby residential areas. Full article

Understanding the drivers of regional economic growth requires examining the mesoeconomic conditions that influence the ability of small firms to scale. This study investigates how the local composition of firms—by size and sector—along with socio-economic and geographic characteristics, affects the prevalence of Scaled Firms across U.S. labor market areas. Using cross-sectional data from 2022, the analysis applies a log-linear regression model to examine the relationship between the density of micro, midsize, and large firms and the share of Scaled Firms (defined as employing 5–99 workers) within industry sectors. Covariates include household wealth, educational attainment, unemployment, population diversity, and metropolitan classification. The results show that the presence of midsize and large firms, along with regional human capital and economic context, is significantly associated with higher levels of small firm scaling. These findings suggest that the mesoeconomic context plays an important role in shaping regional economic growth outcomes and that the composition of local firm ecosystems may influence a region’s capacity for resilience and inclusive development. Full article

Urban densification in Istanbul is progressively limiting access to green spaces, with significant implications for public health and environmental awareness. This study investigates how urban green space use relates to psychological well-being and environmental values by surveying 400 visitors to Belgrad Forest. Exploratory factor analysis revealed five key dimensions of user perception and behavior: (1) personal benefit and well-being, (2) energy and concentration, (3) urban green space experience, (4) use and activities, and (5) environmental concern and value. A strong positive relationship was observed between well-being and energy-related factors, while environmental concern emerged as a distinct construct with limited overlap with recreational behavior. Demographic variables such as age, income, and education level significantly shaped green space perceptions. These findings suggest that while urban green areas support mental and physical health, their role in enhancing environmental awareness follows a separate pathway. The study underscores the importance of incorporating large-scale green infrastructure into urban health and sustainability strategies, particularly in rapidly growing metropolitan regions. Full article

Background: Oral diseases represent a constant burden for health care and socio-economic systems as they are correlated to other non-communicable diseases. The aim of the proposed intervention is to test the effect of daily tooth brushing and oral health education on the oral health status of kindergarten children. Methods: The protocol will be conducted based on a previous epidemiological survey and conducted over 24 months; it has been developed on different levels. Dental hygienists will receive specific training to deliver oral health promotion to children and nursery educators. Training will focus on tailoring key messages to the specific age at visit; this will be outlined in the care pathway and offer practical preparation for delivering interventions and a toothpaste/toothbrush scheme. It will also, involving involve offering free daily tooth brushing to every 4–6-year-old child attending nursery. Data will be collected in four kindergartens in the capital or metropolitan areas, two kindergartens each in two large cities, and one kindergarten each in four villages from different geographic areas. Procedures used to assess the outcomes of each activity will be tailored to specific outcomes. Daily tooth-brushing activities will be monitored using qualitative research. A cost analysis including the distribution of necessary materials and correct delivery of products that shows price trends and percentage differences over the time span as well as consumer price index evaluation for the given time span will be conducted. Clinical outcomes will be evaluated using the caries incidence rate; this will be calculated for each tooth as the unit of analysis and evaluated using a multi-step approach. Discussion: Downstream oral health prevention interventions, like clinical prevention and oral health promotion, aim to enhance children’s quality of life. The program’s goal is to progress towards upstream interventions for a more significant impact. Full article

This study examines access to rental housing in the metropolitan area of Las Palmas de Gran Canaria, linking it to socio-economic inequalities and the increasing precarization. In recent years, housing affordability has worsened due to rising rents, stagnant wages, and speculative dynamics—particularly those linked to tourism and platform-based economies. Drawing on official data from the State Reference System for Rental Housing Prices (SERPAVI) and income statistics at the census tract level, this research quantifies housing affordability and spatial disparities through indicators such as economic effort rates. The analysis identifies patterns of exclusion and urban fragmentation, showing that large sectors of the population—especially those earning the minimum age—face severe barriers to accessing adequate housing. The findings highlight the insufficiency of current public policies and propose the expansion of social rental housing and stricter rental market regulation as necessary steps to ensure fairer urban conditions. Full article

Rural homestays have emerged as pivotal drivers of rural socioeconomic revitalization, particularly in metropolitan peripheries characterized by intensified urban–rural dynamics. However, their spatiotemporal distribution patterns and underlying mechanisms remain underexplored. This study employs Geodetector and related analytical methods to examine rural homestays in Beijing, aiming to decipher spatial heterogeneity and driving factors. The results reveal a distinct “large-scale dispersion with small-scale clustering” pattern marked by pronounced agglomeration in northern mountainous areas and sparse distributions in southern suburban regions. Temporally, the sector currently exhibits a notable expansion–contraction phase influenced by external factors, alongside spatial centroid migration toward resource-rich zones. Geodetector quantification identifies the proximity to transportation nodes and vegetation coverage as primary spatial determinants, while socioeconomic factors demonstrate comparatively limited influence—contrasting sharply with urban contexts. Rural homestay concentration zones are classified into high-, medium-, and low-intensity categories based on the homestay density, with high-intensity zones leveraging apex advantages of scenic resources, cultural heritage, and infrastructure. These findings underscore the interplay of natural environmental factors, tourism resources, transportation accessibility, and socioeconomic conditions in shaping agglomeration dynamics, providing actionable insights for optimizing spatial planning and promoting sustainable development in rural regions adjacent to megacities. Full article