Case Study Of Shanghai Research Articles

Understanding the relationship between small urban parks and mental health: A case study in Shanghai, China

The literature to date has mainly explored the impact on public mental health of green spaces around residential areas, large urban parks, family gardens, blue spaces, national parks, etc., while few studies have examined the association of Small Urban Green Spaces (SUGS) with public mental health. The role, function, and service radius of SUGS in urban people’s lives are quite different from those of general green spaces, and the relationship and mechanism linking general green spaces to mental health cannot be fully applied to small parks. This work was conducted to: 1) investigate the relationship between SUGS and mental health; 2) understand what factors affect mental health; 3) determine what kind of SUGS is more conducive to improving mental health. SUGS’ designs and physical characteristics were evaluated with the Natural Environment Scoring Tool (NEST) and user information for 10 SUGS (1–5 ha) in Shanghai in 2021 was collected via a questionnaire. We developed a multilevel model for exploring the factors affecting mental health from two aspects: the individual level and the park level. This study found that SUGS and mental health were positively associated. In addition to individual–level variables like income, marital status, social cohesion, and use frequency, park–level variables such as usability, aesthetics–natural features, and civilized environment are also conducive to improving mental health, while entertainment facilities in SUGS have a negative association with mental health. There was no evidence that visit frequency, stay time, social cohesion, or physical exercise act as a mediator between park features and mental health. This study provides empirical evidence for the positive correlation between SUGS and mental health and presents a means of promoting public mental health with efficient SUGS planning and management.

Assessing the impacts of urban morphology factors on the energy performance for building stocks based on a novel automatic generation framework

The building sector accounts for an increasing amount of energy and emissions with continued urbanization, and improving the energy performance of building stocks at the early planning stage is cost-efficient and easy to operate. This study presents an automatic generation framework to quantify the impacts of urban morphology factors on the energy performance of neighborhood-scale building stocks. The typical office building shape is firstly determined by a field survey and clustering analysis. Then the building modular cells-based method is introduced to establish a comprehensive energy performance database for building stocks. The impacts of urban morphology factors on energy performance are further prioritized via global sensitivity analysis. A case study in Shanghai, China demonstrated the proposed automatic generation framework which can act as a generalized paradigm to model building stocks and be widely applicable to other climate zones. The urban morphology factors representing buildings’ geometric characteristics tend to have greater impacts on energy performance, among which the top influencing factors are relative compactness, building coverage ratio, and building height distribution. The proposed framework and the findings are expected to generate valuable insights into energy-efficient urban design.

Residents’ behavioral intention to participate in neighborhood micro-renewal based on an extended theory of planned behavior: A case study in Shanghai, China

Neighborhood micro-renewal (NMIR) plays a critical role in revitalizing old neighborhoods and urban sustainability. Public participation has attracted widespread concern to satisfy the multiple demands for social governance in China. Prior studies have underlined the promotion of communal participation, whereas collective characteristics and individual intentions of public behavior have been largely neglected. This study investigates determinants of residents' participation intention and behavior in a framework that incorporates the theory of planned behavior (TPB) and the expectation-confirmation model (ECM). Based on structural equation modeling (SEM), the empirical analysis was conducted with questionnaires completed by 443 residents who dwell in a neighborhood undergoing micro-renewal. The results indicated that residents' participatory intention and behavior were predicted by subjective norms, behavioral attitudes, and perceived behavioral control. In addition, perceived usefulness positively affected behavioral intention, whereas the impact of satisfaction was not significant. Furthermore, community attachment could positively moderate the effect of intention on behavior. Accordingly, several targeted managerial implications regarding public participation in NMIR are proposed. This study extends the TPB into the practice of public involvement on a neighborhood scale and provides insightful ideas to promote residents’ participation awareness.

Analysis of accessibility to hierarchical healthcare facilities in urban area: A case study in Shanghai

AbstractThe accessibility to healthcare facilities is closely linked to people's health and quality of life, which is especially evident in densely populated metropolises, such as Shanghai, China. Access to healthcare facilities is determined by various spatial and nonspatial factors, such as population, transportation modes, travel time, and the reputation of hospitals. In this research, with the consideration of all these factors mentioned above, a new approach based on the two‐step floating catchment area method and multi‐source datasets was proposed to measure the accessibility to these hierarchical healthcare facilities in Shanghai, China, including secondary hospitals and tertiary hospitals. In addition, a web mapping Application Programming Interface (API) was employed in this research to retrieve the traffic cost for different transportation modes and the analytic hierarchy process approach was also employed to help calculate the comprehensive accessibility in Shanghai considering both different types of hospitals and different modes of transportation. The results show that depending on modes of transportation, significant differences can be seen in accessibilities to different types of healthcare facilities. The calculated comprehensive accessibility reflects that the central regions in the research area have higher accessibility compared to the surrounding areas in Shanghai in general and more detailed spatial patterns could also been seen from the accessibility map. The research findings can also be utilized to support the better allocation of healthcare facilities at various levels.

Rural residential energy-saving in China: Role of village morphology and villagers’ daily activities

The impact of village morphology on habitats' daily spatiotemporal behavior and building energy consumption is still vague. This research aims to propose a method to reveal how and to what extent village morphology and residents' demographic structure influence farmhouses' occupancy rate and energy consumption. For this purpose, a combined time geography and building energy consumption method was proposed to disclose the relationship among residents' family roles, village morphology and household energy consumption. To explore the occupancy rate of farm-houses with different family members in different village morphology, resident types based on field research (retrospective interviews and aggregation behavior observation) were formulated. Residents with similar time-space behavior seemed as an “agent” and then a specified number of agents are combined into different families for ideal experiments. By placing different households in different villages for multi-factor cross-experiment, the occupancy rates of different households are obtained based on individual time-space behavior patterns, and then the energy consumption of each household under different scenarios is obtained by combining building energy consumption simulation. In addition, the relationship between resident's disposable personal time, local public space’ quality, and the house occupancy rate was analyzed. Combined with a case study in Shanghai with 417 valid questionnaires, it can be seen that the method proposed in this paper is effective and villages with high quality public space have the potential to reduce household energy consumption from 2.17% to 38.82% for diverse families. The quantitative result indicated that the occupancy rate is mainly influenced by residents having more disposable personal time, and such space-time behavior is sensitive to village morphology. This study proposed a combined method and case evidence to systematically analyze the complex relationship between resident behavior, the village morphology, and household energy consumption, thus contributing to the sustainable plan and design of the community spatial system.

Evaluation of spatial performance and supply-demand ratios of urban underground space based on POI data: A case study of Shanghai

Master planning of urban underground space (UUS) is essential to guide orderly utilization of UUS. POI (point of interest) data from online maps provide a new data source for UUS research. This study proposed a simple framework to evaluate UUS development levels based on POI data. Evaluation methods of UUS spatial performance and development demand were established separately and then applied to the calculation of UUS supply-demand ratios of 217 sub-districts in Shanghai. The results showed that Huaihaizhonglu Sub-district achieved both the highest spatial performance and development demand values. The majority of the sub-districts with high or medium high performance values were situated in the central urban area. By comparison, the sub-districts with extremely low spatial performance clustered in the southern suburb and on Chongming Island. By means of Natural Breaks (Jenks) method, the sub-districts of oversupplied and undersupplied UUS development were figured out and then classified into five types to propose corresponding development strategies for future UUS utilization. The evaluation framework and results can give insights for more scientific and rational compilation of UUS master planning, especially for the metropolitan cities with a huge amount of developed UUS.

Economic analysis of gas heat pump in different building applications:A case study in Shanghai

The application of gas engine driven heat pump (GHP) system can improve the peak valley difference of power and gas system and promote the low-carbon utilization of energy system. The energy consumption of GHP system in different types of buildings and especially the coefficient of performance (COP) under partial load rate needs more analysis. In this paper, according to the local meteorological parameters in Shanghai, the COP operation curve of GHP and electric driven heat pump (EHP) units under partial load rate was analyzed. Combined with the load calculation, the annual energy consumption of GHP and EHP units in different types of buildings, and the influence of energy price on economy of GHP system were analyzed. For office buildings and catering buildings, the energy consumption and operating costs of GHP system were slightly lower than those of EHP system, while for hotel buildings, the annual operating costs of GHP were lower than those of EHP only when the winter gas price was 2.39 yuan/m 3 and the summer gas price was 2.19 yuan/m 3 . Therefore, in order to promote the market competitiveness of gas air conditioning, it is necessary to further refine the gas price or charge standard.

Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China

In this paper, the factors influencing the passenger flow of rail transit stations in Shanghai of China are studied by using the entropy weight-grey correlation model. The model assumptions and the corresponding variables are proposed, including traffic accessibility, built environment, regional characteristics of the district to which the rail transit station belongs, conditions of the station and spatial location, which affect the passenger flow of rail transit stations. Based on the assumptions and the variables, the entropy weight-grey correlation model for analyzing the passenger flow of urban rail transit stations is presented. By collecting the data of passenger flow of rail transit stations and corresponding influencing factors in Shanghai, the results of the entropy weight-grey correlation model are obtained. It is shown that the influencing factors, such as the distances from the rail transit station to the adjacent third-class hospital and the adjacent large commercial plazas, district committees, parking areas and the transaction price of important plots, and the gross output value of the tertiary industry, have significant impacts on the passenger flow of a subway station. Finally, some suggestions are proposed for the local governments to formulate improved policies for rail transit development. The conclusions can provide a reference for the development of rail transit in other large cities and countries.

Investigation of sniffer technique on remote measurement of ship emissions: A case study in Shanghai, China

Shipping emissions have aroused wide concern in the world. In order to promote the implementation of emission regulations, this study develop a ship based sniffing technique to perform remote measurement of the SO2, NOx and CO2 from ships entering and leaving Shanghai port at the open sea. The ship emission prediction model, Smoke diffusion model and source identification model were developed to automatically analyze the emission data and screen the object ship source based on Automatic Identification System (AIS) system. The fueling documents of the detected ship were obtained from maritime sector and the results precision of the sniffer technique was evaluated by comparing the measured Fuel sulfur content (FSC) with actual value deduced from fueling documents. The influences of wind speed and direction, object ship parameters and monitoring distance on the identification of object ship and accuracy of the calculated FSC were thoroughly investigated and the corresponding correction factors under different conditions were deduced. The modified emission factor ratio of CO2 to NOx were proposed in order to improve the accuracy. It is demonstrated that with wind speed higher than 2 m/s and test distance shorter than 400m, the sniffer technique exhibit high efficiency and accuracy for the remote emissions measurement of ship upwind with detection rate higher than 90% and test error of FSC below 15%. To reduce the influence of the wind direction, at least two sniffer systems were required to guarantee that at least one station is in the downwind of the ship lane. Based on the results and discussion, a novel sniffer monitoring system with two buoy based sniffing stations placed close to each side of the ship lane far off shore was proposed to realize the remote monitoring of ship emissions.

How generativity affects organic dining intention: Case study of Shanghai.

With people’s concerns about the environment growing, the demand for organic food has increased. However, few studies have focused on organic dining intention. Therefore, this study examined generativity, which is defined as the direction and care for the growth of future generations through self-expanding forms, and its influence on attitude toward organic food, environment concern and dining intention. The moderating effect of age was also examined. A total of 418 responses were collected through a face-to-face survey from Shanghai respondents. PLS-SEM was utilized to verify the model and test the relationships among the constructs. The results show that generativity directly influences environmental concerns, attitudes toward organic food and dining intentions. Furthermore, environmental concerns and attitudes toward organic food are mediating variables for generativity to affect dining intentions. The older the age group, the more likely it is for generativity to have a positive influence on attitude toward organic food and environmental concerns. Theoretical and practical implications are also discussed in this study.

A Method for Assessing Flood Vulnerability Based on Vulnerability Curves and Online Data of Residential Buildings—A Case Study of Shanghai

Flood vulnerability is the key to understanding and assessing flood risk. However, analyzing flood vulnerability requires sophisticated data, which is usually not available in reality. With the widespread use of big data in cities today, it is possible to quickly obtain building parameters in cities on a large scale, thus offering the possibility to study the risk flooding poses to urban buildings. To fill this research gap, taking Shanghai as an example, this study developed a new research framework to assess urban vulnerability based on vulnerability curves and online data of residential buildings. First, detailed information about residential buildings was prepared via web crawlers. Second, the cleaned residential building information fed a support vector machine (SVM) algorithm to classify the buildings into four flood vulnerability levels that represented the vulnerability curves of the four building types. Third, the buildings of different levels were given vulnerability scores by accumulating the depth–damage ratios across the possible range of flood depth. Further, combined with the unit price of houses, flood risk was assessed for residential buildings. The results showed that the F1-score for the classification of buildings was about 80%. The flood vulnerability scores were higher in both the urban center and the surrounding areas and lower between them. Since 1990, the majority of residential buildings in Shanghai have switched from masonry–concrete structures to steel–concrete structures, greatly reducing the vulnerability to floods. The risk assessment showed decreasing risk trend from the center outward, with the highest risk at the junction of the Huangpu, Jing’an and Xuhui districts. Therefore, this framework can not only identify the flood vulnerability patterns but also provide a clue for revealing the flood risk of residential buildings. With real estate data becoming increasingly accessible, this method can be widely applied to other cities to facilitate flood vulnerability and risk assessment.

Air quality indicators and AQI prediction coupling long-short term memory (LSTM) and sparrow search algorithm (SSA): A case study of Shanghai

Air quality indicators and air quality index (AQI) prediction are effective approaches for urban decision-makers, planners, managers and even city residents to arrange their risk abatement measures in advance. In this study, five models, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Multi-layer perceptron (MLP), Long-short term memory (LSTM) and Long-short term memory coupling with sparrow search algorithm (LSTM-SSA), were used to predict hourly, daily and weekly concentrations of six air quality indicators (PM2.5, PM10, SO2, NO2, CO, O3) and AQI. The case study was based on the hourly observed data of Shanghai from February 1, 2021 to January 31, 2022 and prediction accuracy for different prediction models, indicators and periods was compared. Results revealed that: (1) The prediction accuracy of three neural network models (MLP, LSTM, LSTM-SSA) was superior to two tree-based models (RF, XGBoost) in terms of MAPE, RMSE, MAE and R2. (2) Comparing three neural network models, LSTM-SSA was more accurate than MLP and LSTM in terms of MAPE, RMSE, MAE and R2. (3) For LSTM-SSA, the MAPE of AQI was minimal (1.53%), followed by PM2.5 (2.72%), O3 (3.93%), PM10 (4.87%), NO2 (5.74%), CO (7.54%) and SO2 (8.82%). (4) For LSTM-SSA, the value of MAPE increased from 3.88%, 5.28%–5.91% when the prediction period increased from an hour, a day to a week.

Typology, Preservation, and Regeneration of the Post-1949 Industrial Heritage in China: A Case Study of Shanghai

Industrial heritage is one of the most neglected types of cultural heritage and urban landscape, often being vulnerable to rather than blessed by urban (re)development. China is confronting an unprecedentedly intensive challenge of preserving industrial heritage, as the country has rapidly shifted towards post-industrialization only several years after being recognized as the “world’s factory” in the 21st century. However, none of the existing literature has systematically investigated the typology and preservation of China’s post-1949 industrial heritage. This research selects Shanghai—the largest metropolis and a prime industrial hub in China— for the case study, and examines 83 accredited modern industrial heritage sites in the city through typological analysis, descriptive statistical analysis, and GIS spatial analysis. Two principal findings are identified. First, there is a diverse range of the post-1949 industrial heritage in China, by industries, time, and spatial forms. Particularly the industrial block—where industrial development is intermingled with the surrounding urban fabric—is the dominant spatial type. Second, the preservation and regeneration of China’s post-1949 industrial heritage in the suburbs are substantially more complex and more threatened than those in the inner city. This study concludes by providing implications for enhanced management and investigation of China’s post-1949 industrial heritage preservation and regeneration.

Field Survey on Local Thermal Comfort of Students at a University Campus: A Case Study in Shanghai

University campuses have various functional outdoor spaces characterized by diversified spatial morphology. This study focuses on the local thermal environment of a university campus by conducting fixed weather station monitoring and a mobile survey on a typical summer day. Questionnaire results of college students accompanied by the surrounding climatic conditions reveal obvious linear relationships between thermal sensation voting (TSV) and thermal index physiological equivalent temperature (PET). The range from 29.16 °C to 32.04 °C of the PET is discussed as evaluating the thermal neutral sensation. The PET variations at nine test sites are different due to their different surrounding environments. Mobile survey results across the whole university campus emphasize that the PET varied with time and space in local zones. Spatial differences in the thermal environment are small at 9:00 and larger at 14:00. A correlation analysis of the local Ta and relative humidity (RH) reveals the different effects of spatial morphology characteristic parameters. After calculating the averaged PET values of local zones, problem zones with a higher PET exceeding the thermal neutral limit are recognized. Appropriate optimization on the geometry layouts and land cover patterns is proposed, which would help guide environmentally comfortable university campus design.

Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China

Housing price is one of the most concerning issues to the public worldwide. Studying the spatial characteristics of Shanghai’s housing prices and their explanatory factors is of great practical significance, for Shanghai is the largest city in China and serves as the national economic center and a global financial hub. By crawling the point of interest (POI) data from the Lianjia Real Estate and Gaode Map in the past decade and applying the multiscale geographically-weighted regression (MGWR) model, this study deeply explores the spatial characteristics of housing prices and their main influencing variables in Shanghai before and after the COVID-19 pandemic. Results show that housing prices in Shanghai kept rising even under the shock of the pandemic, especially in high-priced housing. After the pandemic, Shanghai’s housing price market polarization intensified. In addition, housing prices are very sensitive to location and have strong spatial heterogeneity. The influencing effects of different explanatory factors vary perceptibly in spatial heterogeneity as well as pre- and post- COVID-19.

The projection of electric vehicle population growth considering scrappage and technology competition: A case study in Shanghai

Electric vehicles (EV) are expected to see a rapid growth in next decades as a substantial measure to achieve global climate goal. This course will be an unprecedented event in the industry history, and it will inevitably influence and change the power grid and the transportation network. The projection of EV population is the basis to coordinate the development of EV and urban systems. This paper proposes a modified Bass diffusion model to fulfill this task. Firstly, advantages of Bass diffusion model are analyzed, while car scrappage and technology competition with internal combust engine vehicle (ICEV) are involved to improve the model. Secondly, performance of the proposed model is evaluated with actual data during 2006–2020, and the EV stock up to 2050 is projected with the assessment of uncertainties. Thirdly, the policy on license plate quota and the ban on ICEV sales are discussed through experiment simulation. In a standard growing mode, EV is expected to dominant the new sales market by 2030 in Shanghai, and to completely replace ICEV before 2050. This research can provide a reference for urban power-transportation integrated system planning, renewable energy consumption, and carbon reduction path design. • The Bass diffusion model is modified with car scrappage and technology competition. • Effects of the license plate quota and the fuel vehicle sales ban are discussed. • Data of a pilot city for electric vehicle applications are collected for modeling. • The EV population growth up to 2050 is described with discussion of uncertainties.

Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

Proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.

Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.

Impacts of Transportation Networks on the Landscape Patterns—A Case Study of Shanghai

During 1995–2010, a comparison of the comprehensive effects of four different types of urban roads on landscape diversity in Shanghai was performed. Remote sensing images from 1995, 2000, 2005, and 2010 and the transfer matrix mathematical model were used to conduct the research, which focused on studying the rule of land use change in the study area over the past 15 years. Seven different landscape indices (SHDI, SHEI, ED, NP, TE, AWMSI, and MSI) were also selected to analyze the landscape pattern of Shanghai in three different periods and to explore the regularity of its dynamic changes and influential factors. Buffer analysis and statistical analyses were applied to represent the relationship between the distance from different roads quantitatively (e.g., railway, highway, national highway, and provincial highway) and a series of landscape pattern metrics (e.g., SHDI, PD, ED, and IJI) thus exploring the influence of transportation routes on the surrounding ecological environment. The results showed that (1) each type of landscape in the study area changed considerably during the research periods, a great amount of cultivated land was transformed into construction land, and the water area largely decreased. (2) The transportation routes exerted different impacts on the road landscape during different periods, and the effects were more obvious at the early stage of road construction. (3) The national way and the highway influenced the patch density (PD) index and edge density (ED) index more clearly, while the national way and the railway had greater effects on the landscape diversity indices. This study presents an approach that can be applied to quantitatively describe the impacts of transportation routes on landscape patterns and has the potential to facilitate route network planning.

Application of Project Management in Epidemic Prevention and Control: A Case Study of Shanghai

COVID-19 is one of the hot topics in current research. Most researchers have summarized the basic methods and overall processes to deal with COVID-19. However, there are optimal solutions for the most effective COVID-19 response processes. Therefore, this paper summarizes and analyzes the optimal solutions for COVID-19. The research topic of this paper is the prevention and treatment of COVID-19. The research methods of this paper are as follows: first, we will understand the detection and screening methods of COVID-19; second, we will analyze the main methods to stop the spread of the epidemic; finally, we will use the formula to evaluate the risk and analyze the most efficient treatment process. The analysis and improvement methods in this paper can help government departments and relevant organizations better control the epidemic, accurately implement containment, and play an important role in the deployment of medical resources and the identification and screening of COVID-19 patients.