Case Study Of Shanghai Research Articles

A deep learning model incorporating frequency domain information for ultra multi-step air pollutant forecasting: A case study of Shanghai

Multi-step forecasting of air pollutants extends the horizon for individuals and authorities to take informed actions for mitigating potential risks. Due to the instability of air pollutants, current research primarily focuses on relatively short-term forecasting, with achieving ultra multi-step forecasting presenting a significant challenge. In response to this issue, this study proposes a novel model: Frequency Enhanced Decomposed Temporal Convolution Networks (Fed-TCN) to achieve ultra multi-step forecasting. This study applies time-frequency transformation to explore the frequency characteristics of air pollutants and extract long-term patterns. These patterns are then fed into TCN to enhance the accuracy of ultra multi-step forecasting. Extensive experiments were conducted on eight air pollutants at four monitoring stations in Shanghai. The results indicate variations in forecastable ranges for different pollutants. NO and NOx can be forecasted up to one week, while NO2, CO, SO2, and O3 require forecasting within 1–3 days (approximately 24–72 steps ahead). Furthermore, PM2.5 and PM10 can only be forecasted for short-term periods, not exceeding 12 h. When compared to baseline models, Fed-TCN achieves a 4.3%–11% lower Mean Absolute Error. Moreover, Fed-TCN provides insights into the contribution of pollutant composition patterns to forecasting accuracy. In general, daily patterns, semi-commuting patterns, and residuals contribute 68.8%–81.7% to ultra multi-step forecasting. The proposed method is applicable for ultra multi-step forecasts of other regions and different types of air pollutants.

Development of an expanded local climate zone scheme to accommodate diversified urban morphological evolution: A case study of Shanghai, China

The local climate zone (LCZ) scheme efficiently associates urban forms with thermal environments. However, confronted with diversified urban fabrics, the initial LCZ scheme has encountered challenges of its restricted classes and intraclass temperature differences. This study aimed to develop an expanded LCZ scheme to accommodate the diversified urban morphological evolution. Thirty-two block prototypes were developed according to the building footprint ratios (BFRs), mean building heights (BHs), and mean sky view factors (SVFs) of the blocks in Shanghai, China. Then, the block prototypes were classified based on their morphological and temperature indices by k-means clustering, an unsupervised clustering method that maximizes the distance between the clusters and minimizes the distance within the cluster. Results indicated that the block prototypes were clustered into 14 classes, the high-rise LCZs were subdivided, and blocks within six classes were not covered by the initial LCZ scheme. Accordingly, an expanded LCZ scheme was developed by introducing four novel LCZ types and adjusting and subdividing the initial LCZ types, which covered 35% more blocks than the initial LCZ scheme. The mean SVF demonstrated the most significant correlation with the temperature of LCZs, whereas the effects of BFR and BH on the temperature depended on LCZ types.

Analysing the Spatio-Temporal Variations of Urban Street Summer Solar Radiation through Historical Street View Images: A Case Study of Shanghai, China

Analysing the Spatio-Temporal Variations of Urban Street Summer Solar Radiation through Historical Street View Images: A Case Study of Shanghai, China

The development strategies of amateur table tennis matches in China based on the SWOT-AHP model: a case study in Shanghai.

Given the significance of amateur sports matches in health promotion and city culture construction. It is essential to systematically analyze the organizational mode of city amateur matches and propose development strategies. This study aimed to investigate the sustainable development strategies for city amateur matches in China. This study adopted a hybrid model of combined SWOT and the AHP analysis, using the Shanghai City Amateur Table Tennis Matches (ATTM) as a case study. Results showed that 20 factors of the SWOT analysis were included, and the ranking of weights of the SWOT group are Strengths, Opportunities, Weaknesses and Threats, respectively, and the strategic vector (θ, ρ) are (74.21°, 0.5861). ATTM should adopt the S-O pioneering strategy and leverages its advantages and opportunities to promote further development. The findings indicate that ATTM with advanced organizational mode, has good internal strengths and external opportunities, which can enlighten the development of amateur table tennis matches for other regions and countries. Future research should apply the hybrid model to a broader range of events and conduct comparative analyses across countries and regions.

Emerging Quasi-market of Home Care for Older People in Urban China: A Case Study in Shanghai

Emerging Quasi-market of Home Care for Older People in Urban China: A Case Study in Shanghai

A waste extended input-output-based Transformer-LSTM method for analyzing hazardous waste reduction patterns: A case study of shanghai

Population growth, economic development, and industrialization have resulted in a notable surge in the hazardous waste (HW) generation, consequently leading to adverse effects on human health and environmental safety. In this work, a Waste Extended Input-Output-based Transformer-Long Short-Term Memory (WEIO-TL) method was developed to simulate the HW generation patterns of various economic industries in the supply chain and generate the desired strategies for reducing HW generation. The WEIO-TL method was applied to Shanghai to identify its HW reduction path and potential by 2035. Multiple scenarios were designed to investigate the impacts of different degrees of economic and technological development on HW generation. The primary results indicated that the economic proportion of each key industry and the technological innovation capability were the main factors influencing the HW generation in Shanghai. The six key industries identified by the WEIO were the chemical industry (CI), other services (OS), transportation equipment (MTE), computers, communication, and other electronic equipment (MCCOEE), wholesale and retail trades (WRT), and construction (C). HW generation would peak at 1570.9 kt in 2025 and subsequently decrease to 949.0 kt by 2035 under the optimal scenario based on the optimal TL model (R2 and RMSE values of 0.976 and 5.968), associated with industry structure optimization (i.e., part of high-polluting industries would be shifted to service) and technological innovation capability improvement. The results of the study will serve as a theoretical foundation to guide other cities toward achieving "zero waste."

The moderating effects of Airbnb between urban conservation and housing rental market: A case study of Shanghai

The advent of peer-to-peer (P2P) home-sharing platforms such as Airbnb has noticeably impacted the housing rental market, as they have caused an increase in short-term rentals (STRs). Despite a growing body of research showing a positive correlation between Airbnb and housing rentals, the role of urban conservation efforts has received limited attention. In this study, we examine the moderating effects of Airbnb on the relationship between officially designated conservation zones and the housing rental market using data from township-level observations in Shanghai. The empirical evidence demonstrates that both the presence of historic conservation areas and the number of Airbnb listings have a considerable positive impact on property rental pricing. Furthermore, there is a direct correlation between the presence of more extensive conservation areas and an increase in house rental prices. Heterogeneity effects are evident in several townships in Shanghai with different levels of Airbnb activity, as the moderating effects are significant in townships with a large number of Airbnb listings. These results suggest that rising housing rents cannot be attributed entirely to the proliferation of STR services facilitated by home-sharing platforms. Future regulations for the housing rental market should address the impacts of home-sharing accommodations and the preservation and revitalization of designated conservation zones.

Evaluating efficiency of metro-led urban underground public space: a case study in Shanghai

Metro-led urban underground public space (UUPS) are among the most common and important types of underground and public spaces. The need to measure metro-led UUPS performance and improve urban planning and public services has facilitated the development of various evaluation methods. However, the essential performance aspects of efficiency and effectiveness are rarely balanced. Most methods focus on evaluating effectiveness while neglecting the cost incurred. This study proposed two sets of efficiency indicators of cost (input) and effectiveness (output) separately and used data envelopment analysis (DEA) to analyze the relative efficiency of 20 metro-led UUPS in Shanghai, China. The results indicated that the UUPS of People’s Square, Hanzhong Road, and Huamu Road were the most efficient. Whereas, the South Shanghai Railway Station UUPS and other five cases were neither technically efficient nor scale efficient; a waste of space volume was observed in half of the inefficient cases. Through comparisons of the results with previous studies, it was determined that different conclusions could be drawn when considering efficiency. Moreover, the evaluation framework based on the DEA method proposed in study paper can provide a comprehensive and holistic approach to guide metro-led UUPS planning optimization.

Urban mobility analytics amid COVID-19 pandemic: A framework for promoting work resumption based on mobile phone data

The Corona Virus Disease 2019 (COVID-19) pandemic had a pernicious influence on the whole world, so that the international community implemented various travel policies to inhibit the viral spread. With the COVID-19 pandemic under control, the overly lenient measures allowed the new variant to take advantage. Also, it is difficult to determine the geographical scope of transport policy that has the least impact on work resumption. Therefore, it is necessary to find policies to promote work resumption while preventing and controlling the pandemic in a geographical perspective. In this paper, a framework based on mobile phone data is proposed for promoting work resumption during the pandemic. First, human mobility networks are constructed through the origin-destination flow extracted from mobile phone data. Second, the singular value decomposition (SVD) algorithm is utilized to recognize the mobility patterns and explore the urban mobility dynamics of work-resumption-related travel during the pandemic. Third, the nonoverlapping community detection algorithm is used to generate policy zoning for work-resumption-related travel. Finally, the policy zoning is validated by public health data based on the overlay analysis and spatial statistics method, and policy implications are proposed. A case study of Shanghai, China is applied to verify the proposed framework. The results show that the framework can capture the mobility patterns and implement the policy zoning based on mobile phone data to balance the economic activity and public health during the pandemic, and the proposed framework can be applied to other cities with future public health events.

Spatial and social inequality of hierarchical healthcare accessibility in urban system: A case study in Shanghai, China

Urbanization is intensifying in cities, accompanied by burgeoning inequalities, notably in the healthcare sector. Limited research offers a granular examination of healthcare resource accessibility across diverse population groups. In this study, we employ census data from all 6164 communities in Shanghai to investigate the relationship between multi-scalar spatial dimensions and the socio-economic (dis)advantage of populations concerning healthcare resource accessibility. Distinct from previous studies, this research quantifies social inequality by considering the demographics of community residents and their interactions with healthcare facilities. We elucidate the stratified spatial distribution of healthcare accessibility across various spatial scales in Shanghai, leveraging Web Application Programming Interface (API) technology and examine the pervasive social inequalities across sub-district, district, and the central urban-suburban divide, focusing on specific population subgroups including gender, age, education level, wealth, household registration, ethnicity, occupation, and housing living facilities. Findings reveal that disparities in healthcare accessibility correlate with the quality of healthcare facilities, the socio-spatial attributes of localities, and the socio-economic and demographic profiles of the populations. The insights from this research will aid policymakers in enhancing the distribution and provision of healthcare resources, thereby promoting spatial and social equity in public healthcare system planning and implementation.

Revealing spatiotemporal characteristics of EV car-sharing systems: A case study in Shanghai, China

Electric vehicle (EV) car sharing, as an emerging mode of transportation, has been paid increasingly amount of attention by scholars in recent years. However, at the operational level, we still know very little about its spatiotemporal characteristics, especially its temporal associations with land use patterns. In this study, we comprehensively investigated these two aspects by utilizing longitudinal data of the one-way EV car-sharing system in Shanghai, China, with a total count exceeding 50,000 vehicles in 2018. The results showed an unmatched coupling relationship in terms of the spatial dimension between EV car-sharing flow communities and intra-city administrative divisions. For the temporal dimension, we found that, unlike the conventional division of peak and non-peak hours, there are nine different types of car use behaviors in terms of temporal variations in EV car-sharing travel in Shanghai. We further revealed that such temporal variations, in terms of temporal attractiveness, can be significantly affected by land-use characteristics, including healthcare services, lifestyle services, companies and enterprises, and scientific, educational, and cultural services, indicating a strong and complex interaction between land use and the pattern in car use behaviors of this emerging mode of transportation. This data-driven study offers new insights into EV car-sharing operations and provides better understanding of EV car-use behaviors, which may promote evidence-informed decisions and policymaking for megacity authorities.

Resident Preferences for Urban Green Spaces in Response to Pandemic Public Health Emergency: A Case Study of Shanghai

The COVID-19 pandemic represents a quintessential public health crisis, profoundly impacting the utilization patterns of urban green spaces through stringent quarantine and lockdown measures. However, existing research inadequately addresses specific concerns regarding future urban green spaces and tends to oversimplify population divisions. This study delves into the needs and preferences of Shanghai residents affected by the pandemic and quarantine measures, focusing on various aspects such as specific types of green spaces, facilities, landscape elements, and landscape and spatial types. Multifactorial population clustering was also performed. This study delineates the following conclusions: (1) It is imperative to afford residents access to green spaces at least once a week, even during quarantine periods. (2) Residents exhibited a preference for accessible green spaces equipped with essential amenities, favoring unobstructed vistas and plant-centric ecological landscapes during the pandemic. Additionally, there is a notable preference for private green spaces among residents. (3) Post-pandemic, the “affluent” group displays a heightened overall demand for green spaces, the “middle-class” group shows a conspicuous inclination towards specific green space landscape elements, while the “low-income” group consistently exhibits a low preference for green spaces during and after the pandemic. This study underscores the necessity of developing human-centric green spaces to promote equity and resilience in the face of future emergencies, rooted in residents’ preferences amidst public health crises.

Designing a sustainable municipal solid waste management system over multiple periods using extended exergy accounting method – a case study of Shanghai

Waste-to-energy technologies can significantly alter urban metabolism and sustainability. This article proposes a multicriteria analysis framework based on the extended exergy accounting method to assess and plan a sustainable municipal solid waste management system from urban metabolism perspective. This framework includes three components. Firstly, an accounting model that integrates material, energy, social, economic, and environmental criteria was developed to identify the contribution of municipal solid waste management system to urban sustainability and to decide whether it is sustainable. Then, a multiperiod municipal solid waste management system planning model was developed to provide the optimal strategy for waste-to-energy facilities deployment and municipal solid waste allocation for different periods. Finally, multidimensional indicators were proposed for a sustainability comparative analysis in overdeployment, pressure on urban ecosystem and urban resource conversion efficiency. The proposed method was applied to the case study of household solid waste management in Shanghai and two scenarios were considered. Our comparison showed that the hybrid scenario (in which various waste recycling technologies were applied together) theoretically performs significantly more sustainable than the incineration scenario in terms of material investment, emissions, economic performance, resource depletion, and recovery. Sensitivity analysis of three parameters—the maximum deployment number of each unit waste-to-energy facility ([Formula: see text]), the minimum utilization rate ([Formula: see text]), and the capacity budget ([Formula: see text])—were carried out. The proposed hybrid method was found to be sensitive to [Formula: see text], but less sensitive to [Formula: see text] and [Formula: see text]. Lessons learnt can be used to plan similar waste-to-energy strategies in other countries.

Exploring the Influence of Dynamic Indicators in Urban Spaces on Residents’ Environmental Behavior: A Case Study in Shanghai Utilizing Mixed-Methods Approach and Artificial Neural Network (ANN) Modeling

The main aim of this article is to evaluate the impact of dynamic indicators associated with urban spaces on the environmental behavior of residents in Shanghai, China. With the city experiencing rapid urbanization and increasing environmental concerns, it is crucial to understand how the design and management of urban spaces can encourage pro-environmental attitudes and actions among the population. The study specifically focuses on dynamic indicators, namely vitality, accessibility, quality, and walkability, to develop a comprehensive understanding of the utilization and experience of urban spaces. The research outcomes will make valuable contributions towards Shanghai’s objectives of achieving environmental sustainability, while also providing insights that have broader relevance to sustainable urban development globally. As the built environment significantly influences energy consumption, resource utilization, pollution generation, and overall human wellbeing, attaining urban sustainability is of paramount importance. The findings of this study will aid in informing strategies and policies that promote sustainable practices, not only benefiting Shanghai but also serving as a valuable resource for urban development initiatives worldwide. Sustainable urban design principles, including compactness, density, mixed land use, greening, and walkability, have been associated with pro-environmental behaviors, including reduced reliance on automobiles, increased walking and cycling, and heightened environmental consciousness. Nevertheless, the relationship between the built environment and sustainability behaviors is intricate and is influenced by multiple factors. Consequently, further research is necessary to comprehend how specific spatial and temporal dynamics impact environmental behaviors within urban settings. In this study, an artificial neural network (ANN) was developed to estimate the quality and walkability of an area and environmental behaviors by considering the augmented vitality and accessibility factors. The ANN’s predictions demonstrate that higher levels of vitality and accessibility positively contribute to improved walkability and environmental behaviors. The accuracy of the ANN’s predictions was assessed using linear regression, which yielded acceptable error rates when compared with experimental results.

Influencing factors of spatial vitality in underground space around railway stations: A case study in Shanghai

Developing urban underground space for transportation and commercial purposes has become a vital strategy for sustainable urban growth, especially in dense urban contexts. The vitality of underground space has received wide attention as a key indicator of its efficiency and performance. The spatial vitality of underground space is affected by many factors, including function, spatial configuration, transport environment, and pedestrian-oriented design features. This study takes the underground space of Shanghai Hongqiao Business District (Phase I), one of the most successful integrated railway station-city development projects, as a case to explore the spatial and temporal distribution characteristics of spatial vitality and its influencing factors. Multilevel linear models were used to analyze the relationship between pedestrian flow and its influencing factors. Random forest models were used to investigate the relative importance of influencing factors on spatial vitality. This study verified that the distance to the transportation hub and spatial configurations calculated by space syntax are the two most essential factors for vitality. The number of retail stores and walkway width impact the vitality on weekends. The results shed light on our understanding of the urban vitality in the underground space and provide some tentative suggestions for the planning and design of underground space in high-density cities.

Evolution of Project-Based Collaborative Networks for Implementing Prefabricated Construction Technology: Case Study in Shanghai

Prefabricated construction (PC) is considered to be a low-carbon construction method. Implementing prefabricated construction projects (PCPs) requires multiple industry organizations to participate and collaborate. As different PCPs are initiated and implemented, industry organizations will gradually gather into a complex and evolving collaborative network at the industry level based on specific collaboration relationships with different project backgrounds. The evolution of the collaborative network is related to how industry organizations interact with each other, and how PC-related knowledge and innovation has spread among organizations in the long term. However, the laws of network evolution and the micro effects that drive network evolution are still unknown. This study analyzes 236 prefabricated construction projects (PCPs) in Shanghai during 2015–2023, using the stochastic actor-oriented models (SAOM) method to explore how the macro structure of project-based inter-organizational collaborative networks for prefabricated construction (PC) technology implementation evolves over time, and how micro effects jointly support the evolution of the networks. The macro-level descriptive analysis of the network indicates that the collaborative network has become increasingly dense over time and continues to show a core–peripheral structure, with a small number of super-connected organizations. The micro-level SAOM analysis further reveals that the evolution of the collaborative network structure is driven by structure-based preferential attachment and geographic proximity effects, as well as attribute-based ownership similarity effect. This exploratory effort applies a network dynamics model to investigate the micro mechanism of the evolution of inter-organizational collaboration. The research results provide theoretical guidance and decision-making references for PC industry organizations to develop efficient network action strategies. In addition, it can help industry managers to formulate appropriate network management strategies.

Measuring the Spatial-Temporal Heterogeneity of Helplessness Sentiment and Its Built Environment Determinants during the COVID-19 Quarantines: A Case Study in Shanghai

The COVID-19 outbreak followed by the strict citywide lockdown in Shanghai has sparked negative emotion surges on social media platforms in 2022. This research aims to investigate the spatial–temporal heterogeneity of a unique emotion (helplessness) and its built environment determinants. First, we scraped about twenty thousand Weibo posts and utilized their sentiments with natural language processing (NLP) to extract helplessness emotion and investigated its spatial–temporal variations. Second, we tested whether “helplessness” was related with urban environment attributes when other real estate economic and demographic variables were controlled using the ordinary least squares (OLS) model. Our results confirmed that helplessness emotion peaked in early April when the lockdown started. Second, residents in neighborhoods characterized by higher rents and property management fees, higher population density, lower housing prices, lower plot ratios, or surrounded by less tree view and higher perceived visual complexity, are found to exhibit higher degree of “helplessness”. This study provides an effective data-driven framework to utilize social media data for public sentiments monitoring. The helplessness emotion identified is a unique mental distress under strict quarantine measures, which expands the growing literature of urban governance in the post-pandemic era. Decision makers should pay attention to public opinions and design tailored management measures with reference to civic emotion dynamics to facilitate social sustainability and resilience in face of future crises.

Impact of the coronavirus disease 2019 pandemic on the diversity of notifiable infectious diseases: a case study in Shanghai, China.

The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.

Identifying urban built-up areas using CORONA imagery in the pre-Landsat era: a case study of Shanghai in 1965

ABSTRACT The Corona project (1959–1972) created a large number of high-resolution images that provided valuable historical research data before the advent of Landsat (1972) and even compensated for the lack of accuracy in early stages. In this study, we collected eight available Corona images to create a historical remote sensing image of Shanghai in the 1960 and developed an automatic image segmentation and classification methodology. In the experimental part, we verified its accuracy, and discussed the urbanization process in Shanghai. The results showed that 1) In 1965, the urban area of Shanghai was mainly distributed in Puxi, and the urbanization degree of the entire hinterland of Pudong was still very low. Built-up areas were highly concentrated in the main urban areas. 2) The overall accuracy was 91.5% and the kappa coefficient was 0.762. 3) We compared the built-up area in 1965 with the current urban area, which increased from 306.23 to 2,804.25 km2. Besides, historical imagery also captured the state of environmental pollution at that time. Overall, our method could inform the mapping and classification of remotely sensed land surfaces on a global scale in the 1960s, extending pre-Landsat era research to studies of urban history, archaeology and cultural heritage.

Achieving Neighborhood-Level Collaborative Governance through Participatory Regeneration: Cases of Three Residential Heritage Neighborhoods in Shanghai

Residential heritage neighborhoods in China are experiencing a transformation from large-scale demolishment, which is associated with relocating residents, to small-scale neighborhood regeneration, and distinctive models of participatory regeneration are emerging. Participatory regeneration is increasingly considered to be an effective way to achieve multiple goals in urban development; however, little research has investigated the impacts of participatory regeneration on achieving neighborhood-based collaborative governance. This study aims to explore which mechanisms associated with participatory regeneration facilitate or constrain neighborhood-based collaborative governance, using case studies in Shanghai. Based on the investigation of three nuanced pathways in participatory regeneration, this study compares four dimensions associated with participatory regeneration (i.e., participatory decision-making, inclusion, collective problem-solving, and power-sharing) and explores key mechanisms that are applicable for achieving collaborative governance in different scenarios, aiming to enhance social development and social sustainability in future. The findings indicate that although grassroots government played vital roles in participatory heritage neighborhood regeneration through financial support and supervision strategies, facilitating mechanisms could still be observed for achieving collaborative governance. Furthermore, this study provides suggestions for planners in building collaborative governance in other developing areas which are experiencing rapid urbanization with strong state interventions.