Case Study Of City Research Articles

Enhancing Architectural Space through AI-Driven Ideation: A Case Study of Future Iranian-Traditional City

Artificial intelligence is an evolving technology that has drawn huge economic and social, educational benefits for the future. The revolutionary potential of this technology is boundless, as it can transform how humans live, work, learn, discover, and interact with each other. This research aims to investigate the theoretical foundations of environmental psychology, identify the characteristics of the appropriate architectural space, and then investigate the effect of using artificial intelligence in creating the appropriate architectural space from a formal aspect. The research method is descriptive-analytical and of a qualitative type, which was done by studying library sources and then logically analyzing the data. The research tool is artificial intelligence Midjourny and Dall.e3, which has been introduced for some time in creating artistic images from input words, and prompts. It has been the focus of various artists and architects. It can be said that with architectural conceptual plans, the future will become a reality differently, and they can positively influence the creativity of architects and create futures that most people will not think of today. The use of these images in creating accessible spaces is only one example of the use of artificial intelligence in the architectural design process and it can inspire architectural researchers in various fields.

Using roof garden to alleviate urbanization problems -- A case study of Chengdu city

The accelerated process of urbanization has led to significant urbanization issues such as noise pollution, air pollution, and the urban heat island effect. Roof gardens, as an effective way to create green spaces within cities, have considerable potential to address these urbanization problems. Taking Chengdu City as an example, this paper presents a simple design for a roof garden, focusing on the infrastructure, layout, plant species, and materials of the roof garden. The roof garden was improved after establishing its potential to mitigate urban noise by initial qualitative analysis. Firstly, specific vegetation selections are made which include deciduous trees such as Cephalotaxus Harrington and shrubs like Bauhinia blakeana. which can adapt to Chengdu's climate while effectively capturing dust particles. Secondly, a water storage layer was installed, and the angle of the pavilion roof was adjusted which allows the roof garden to regulate rainwater runoff effectively and minimize potential problems as possible. Although the current roof garden design is still in the theoretical stage and requires further field research and design, it holds tremendous potential in dealing with urban issues due to its unique advantages and policy support.

Cradle-to-gate life cycle assessment of interior floor material alternatives in Egypt

AbstractThe building materials sector has a major environmental impact and is one of the main causes of environmental degradation in the construction industry. An Environmental Impact Assessment of several interior floor material options, such as ceramic, marble, and parquet tiles, is conducted in this study using a Life Cycle Assessment (LCA) methodology. Due to the shortage of LCA applications in Egypt and the unavailability of the inventory database for the materials industries, the study uses a case study of a new city in Egypt to assess the environmental effects of various floor materials. The LCA approach uses SimaPro software V9.50 and its integrated Ecoinvent database V3.0 to define the objective and scope, conduct an inventory analysis, evaluate the impact, and interpret the results. According to the LCA findings, ceramic tiles, followed by marble tiles and parquet tiles, have the lowest negative environmental impacts. Regarding the singles score, results (midpoint method) have revealed that the marble tiles have hit the highest adverse environmental impacts by 2.24 pt, followed by parquet by 1.21 pt, and finally, the ceramic tiles by 0.594 pt. The global warming potential of ceramic tiles is 0.539 kg CO2, which is less than 74% of the global warming potential of marble tiles (2.01 kg CO2). The non-renewable energy impact of the marble tiles is (38.70 Mj primary), which is higher by 78% of the non-renewable energy of ceramic (8.46 Mj primary). Finally, the human health impact has recorded the highest numbers in the marble industry by 1.12E−5 DALY, which means the marble industry needs a high amount of energy. Ultimately, the findings of this study can potentially improve the sustainability and energy efficiency of construction projects by assisting decision-makers in selecting and applying environmentally friendly building materials.

Analyzing the Factors of Vacant Home Occurrence for Urban Sustainability: A Case Study of Medium-Sized Cities Focusing on Asan City, Chungcheongnam-do

This study aims to enhance urban sustainability by analyzing the spatial distribution and underlying causes of vacant homes in Asan, Chungcheongnam-do. Various statistical methods were employed to analyze date concerning the number of vacant stores, population changes, land use complexity, and the physical characteristics of land and buildings; these factors were found to influence the prevalence of vacant homes across Asan. Additionally, it was found that distinct factors differentially affect specific regions, such as old downtown areas versus rural villages. This indicates that reducing vacant homes in these areas requires distinct policies tailored to the unique circumstances of each region. For instance, in old town areas, small lot ratios and land use complexity are significant, while in rural villages, the average number of floors and land use complexity play a major role. This study highlights the diverse factors influencing the prevalence of vacant homes and suggests that to effectively address this issue, policies should be developed that are tailored to the unique characteristics of each area, categorized at both the city and local levels.

Efficiency in Urban Management and Smart City Concepts: A Russian Cities Case Study

Efficiency in Urban Management and Smart City Concepts: A Russian Cities Case Study

Relationships among six urban air pollutants and identification of pollution types – A case study of Chinese cities above prefecture level

Urban air pollution is caused by the interplay of urban development and natural conditions, but it's unclear which factor has a greater impact. Studying the link between air pollutants and identifying the types of pollution causes is crucial for understanding the pollution mechanisms and formulating efficient management measures. During the research period from 2015 to 2020, this study analyzed the relationships between six air pollutants, calculated the city capacity (CC) and natural condition (NC) scores of 285 cities with the help of the index system and entropy weight TOPSIS model, and employed Gaussian Mixture Model (GMM) and random forest regression model to identify air pollution types. The results show: During the study period, the concentrations of most pollutants decreased, with SO2 experiencing the largest decline, reflecting the significant effectiveness of China's recent atmospheric pollution control efforts. In terms of spatial distribution, the North China Plain (NCP) and the Loess Plateau (LP) remain focal areas of pollution. The correlation analysis results indicate a significant positive correlation among all air pollutants except O3, underscoring the necessity of coordinated pollution control efforts, but the complexity of O3 pollution should not be overlooked. High-value agglomerations of CC scores calculated by the model are predominantly located in the eastern coastal region, while NC scores exhibit two high-value agglomerations in the north and south. Utilizing the GMM, the 285 cities were classified into three categories, and the random forest regression model was employed to identify them as CC dominate, NC dominate, and intermediate types. This paper proposes a novel approach for categorizing city air pollution types, aiming to tailor prevention measures and foster sustainable urban development.

Machine learning for water demand forecasting: Case study in a Brazilian coastal city

ABSTRACT Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case.

Techno-economic optimization of a photovoltaic-wind energy-based hydrogen production system: A case study of different cities of Saudi Arabia

The study employed Hybrid Optimization of Multiple Electric Renewables software and two-hybrid energy systems configurations to ascertain the most effective approach for generating hydrogen and electrical power in the designated three cities of Dammam, Jeddah and Tabuk in Saudi Arabia. To reduce emissions of greenhouse gases, improve energy security and encourage sustainable development, this study was motivated by the need to address the urgent need to switch to renewable energy sources like solar and wind based on Saudi Arabian's Vision 2030 initiative. To achieve these objectives, this research investigates the techno-economic feasibility of producing hydrogen from renewable energy sources to promote energy independence and economic growth in the region of Saudi Arabia. The novelty of the study provides a regional view of the use of renewable energy sources for hydrogen production by concentrating on several Saudi Arabian cities. It is essential for successful energy planning and policy formation to have insights suited to unique regional settings as demonstrated in this study. According to the findings of the study, the electrical and hydrogen requirements of the selected cities in Saudi Arabia can be fulfilled using renewable energy sources, as sufficient wind speed and sunlight are available to produce renewable hydrogen. The outcomes of the study demonstrated that the electricity and hydrogen generated in the city of Dammam were found to be significantly higher than in the other two cities resulting in high hydrogen production at the city of Dammam compared to the cities of Tabuk and Jeddah. The hybrid energy system in Dammam was identified as the most practical option for fulfilling Saudi Arabia's electricity and hydrogen production requirements based on the analysis and optimization, with low net present cost, levelized cost and levelized cost of hydrogen values of $235235, 0.316 $/kWh and 4.16 $/kg, respectively. The outcome of the study suggests that incorporating a renewable energy system in Dammam may diminish the country's reliance on fossil fuels and decarbonize its transportation sector. The results of this study could be employed by members of the Saudi hydrogen industry to promote increased hydrogen production and make Saudi Arabia's energy infrastructure more resilient and sustainable.

Analyzing Green Growth Efficiency in China and Investigating the Spatial Effects of Fiscal Decentralization: Case Study of Prefecture-Level Cities

Analyzing Green Growth Efficiency in China and Investigating the Spatial Effects of Fiscal Decentralization: Case Study of Prefecture-Level Cities

Travel time reliability evaluation using fuzzy-possibility approach: a case study of an Indian city

ABSTRACT Due to growing traffic congestion, significant variations in travel time can be seen in Indian cities. This has led to inconsistency and uncertainty about reaching destinations on time. Therefore, average speeds cannot be the only factor in assessing traffic performance. Travel time reliability is a more crucial parameter to evaluate traffic performance considering the inconsistency and uncertainty involved in travel times. The probe vehicle technique is often employed to measure the travel time. However, this technique can yield only a small sample unless huge resources are deployed. Therefore, this paper attempts to develop a travel time reliability-consistency evaluation model (TTRC-EM) for limited and ambiguous information using a novel fuzzy-possibility approach. Two urban corridors, representing arterial and sub-arterial situations, are taken as a case study in Surat, India. The developed model can be applied to evaluate corridor traffic performance and assign a level of service under mixed traffic conditions.

The relationship between particulate matter and kidney biomarkers in pregnant women: A case study in Yazd city

The relationship between particulate matter and kidney biomarkers in pregnant women: A case study in Yazd city

Enhancing traffic control systems with live video analytics: Issues, challenges, opportunities, and recent problems

With the rapid urbanization and increasing number of vehicles on the roads, it has become imperative to develop innovative solutions that can monitor and manage traffic congestion automatically. Traffic congestion harms the economy, environment, and overall quality of life. To address these challenges, smart traffic management systems employ cutting-edge technologies such as live video analytics and sensor-based adaptive traffic control systems. These systems can predict traffic patterns, locate congestion hotspots, and uncover abnormalities contributing to road accidents in real time. However, adopting these technologies for traffic control systems raises important concerns such as robustness and sustainability across different traffic junctions, data integration from multiple sources, and computational feasibility for real time computation. Therefore, this paper aims to present an overview of the potential benefits and challenges in adapting the latest technologies including the Internet of things and machine learning for sustainable traffic management. Additionally, a case study of a smart city is presented to evaluate an adaptive traffic control system based on live camera feed analytics by leveraging computer vision techniques. The adaptive traffic control system is accurate in vehicle detection and counting. This system is very useful for smart cities where traffic signals need to be automated according to the density of vehicles.

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

To develop a water conscious strategy, it is critical to track and trace water from its source to the end users, understand water conservation behaviors, and identify the factors that influence water consumption. However, in developing nations, little research has been done to provide a quantitative picture of how water is consumed and transformed in urban households, as well as the water sensitive interventions needed to improve access to clean water. Hence, the main objective of the study was to determine the most significant residential water consumption variables and to predict residential water consumption in a way that can generate water consumption information for water sensitive intervention decision making using the case study of Adama city in Ethiopia. A combination of top down and bottom up data collection techniques were employed as the data collection instrument. Machine learning was integrated with spatial and socioeconomic analytic techniques to estimate daily household water consumption and identify the factors that significantly influence household water consumption. The results show that there is only “one source option” for the city’s clean water supply and that different water harvesting methods are not likely to be developed. The average daily water consumption per person is 69 liters which falls below the national standard of 80 liters allocated per person per day. The result reveals that the water distribution network covers only 45% of the city master plan. About 38% of the water demand is unmet and 30% of households only receive water once every three days or fewer. This shows that the city is experiencing physical and economic water scarcity. The results demonstrated that family size, housing quality, income, number of rooms, legal status of the parcel, supply reliability, climate, and topographical features are the most important factors in predicting residential water consumption. This study further demonstrates how well supervised machine learning models, such as the Random Forest Regression algorithm, can predict the household’s daily water consumption. The findings also showed that there is a need for significant improvements in water saving habits of the households. Another conclusion that can be drawn is that as long as the city’s business as usual water consumption practice doesn’t change, the water supply problem will worsen over time.

Use of hydraulic simulation as a tool in decision making in water supply network: a case study in the Brazilian city of São Gabriel, RS

Population care by water supply networks has evolved in the world but it is still not a reality for the entire population. In the existing systems, almost 40% of the volume of water available does not reach the user. An important variable that influences water losses in the system is the pressure. In this work, part of the water distribution system in the Brazilian city of So Gabriel, in the state of Rio Grande do Sul (RS), was evaluated through hydraulic simulation using [public domain, water distribution system modeling software package developed by the United States Environmental Protection Agencys (EPA) Water Supply and Water Resources Division] EPANET in three different scenarios: maximum, minimum and sustainable water consumption. In the simulations, the pressures and speeds in the distribution system were analyzed, which allowed the proposition of interventions such as: installation of accessories (pump and pressure reducing valves), reducing diameters of some pipe sections, alteration of the operating regime of already existing pumps, system sectorization and estimates of loss reduction resulting from these interventions.

The importance of social media and brand trust to promote tourism brands: case study in Ho Chi Minh City

The importance of social media and brand trust to promote tourism brands: case study in Ho Chi Minh City

Housing Vulnerability, Shared Housing, Informality, and Crowding: The Housing Strategies of China’s Recent College Graduates

ABSTRACT Mirroring developments elsewhere, for recent college graduates in China the transition to employment is increasingly protracted and, for some, precarious. Case studies of individual strategies and cities indicate a diversification of housing pathways, where economic precarity is rendering at least some vulnerable. Based on original survey data collected in 2019, I investigate the housing choices of Chinese college graduate job market entrants and ask who is vulnerable and whether adverse outcomes are mere market entry strategies or lasting. I find that recent graduates generally find housing in central locations, close to their work. However, to achieve favourable locational outcomes on low budgets, many resort to sharing. High-density room sharing of three or more people is illegal but prominent and dominates in first-tier cities. The findings highlight how urbanization interacts with housing vulnerability and contribute to theorizing on the housing crisis, the Global North-South binary, and the very nature of home sharing.

Smart city case study through urban regeneration living lab in the information technology era: Focusing on ‘Tech City’ in London, England

Smart city case study through urban regeneration living lab in the information technology era: Focusing on ‘Tech City’ in London, England

The Price of Losing Autonomy: Assessing the Economic Impact of County-to-District Mergers in China

Amalgamations in democratic settings often result in disadvantages for small and peripheral units, as their political power diminishes in the merged jurisdictions. This article extends the discussion to China by assessing the economic outcomes of county-level units after their conversion into districts directly controlled by city authorities. Leveraging a large county-year panel dataset and employing an advanced causal effect estimator, my analysis reveals a generally less optimistic outlook for these county-turned districts following the mergers. The negative impacts are initially evident in microeconomic indicators, especially in resident deposits, and later materialize in the long-term trajectories of macroeconomic indicators. Furthermore, heterogeneous analysis and a comparative case study in Hangzhou city link the problem to the loss of administrative autonomy. Notably, the negative impacts are less likely to manifest in county-turned districts that are under provincial protection and therefore maintain a certain degree of administrative autonomy after the mergers.

Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model.

Archaeological buildings and standards of their reuse suiting the civilized value of place and economic value of time: a case study of Old Ibb City

Archaeological buildings and standards of their reuse suiting the civilized value of place and economic value of time: a case study of Old Ibb City