Case Study Of Wuhan Research Articles

A comparative analysis of cross-sectional study and natural experiment in rail transit-travel behavior research: A case study in Wuhan, China

There has been a global increase in investment in rail transit, driven by its potential to enhance transportation efficiency, reduce air pollution, and stimulate economic growth. Both cross-sectional studies and natural experiments have contributed to the growing body of evidence supporting these claims. While natural experiments are commonly preferred for evaluating the impact of rail transit, cross-sectional studies remain popular due to their ease of data collection. However, there is a scarcity of studies that compare these two approaches using the same dataset to assess the robustness of cross-sectional studies. Using a two-wave panel dataset from Wuhan, China, this study used both cross-sectional and natural experimental analyses to examine the relationship between urban rail transit and travel behavior. The study attempted to enhance the credibility of the cross-sectional analysis by controlling for confounding variables and by combining it with the propensity score matching (PSM) method, respectively. The results revealed that the cross-sectional analyses could produce similar results, when setting a more stringent significance level. The findings suggested that well-designed cross-sectional studies can be reliable and represent a cost-effective alternative to resource-intensive natural experiments.

Spatio-Temporal Analysis of Urban Emergency Response Resilience During Public Health Crises: A Case Study of Wuhan

In recent years, public health emergencies have severely disrupted city functions and endangered residents’ health and lives, enhancing the emergency response capacity, a crucial aspect of building resilient cities. Based on the Wuhan Statistical Yearbook and local economic and social big data, this study constructed a resilience assessment system that covers resistance, adaptability, and resilience. Evaluate the resilience level of each region and analyze its spatiotemporal characteristics using the entropy weight method, Theil index, and natural breakpoint method. The central area exhibited higher resilience levels, while peripheral areas showed lower resilience, owing to location advantage and developmental disparities. The obstacle degree model revealed that scientific and technological innovation, social rescue capabilities, and population size are the primary weak links in building urban emergency response capacity. Based on these findings, this study aims to provide policy recommendations and improvement measures for multiple stakeholders, guide the construction of resilient cities, enhance the ability to respond to public health crises and recovery speed, and ensure urban functions and residents’ well-being.

Causal inference of urban heat island effect and its spatial heterogeneity: A case study of Wuhan, China

The accelerated urbanization process has exacerbated the urban heat island effect, leading to significant negative impacts on the physical and mental health of urban residents. A deeper understanding of the mechanisms underlying the urban heat island phenomenon is essentially beneficial for providing scientific supports towards improving the urban thermal environment. To address the challenge of effectively depicting the complex interactions among urban environment factors, this study employed the Peter-Clark causal discovery algorithm to analyze the causal structure of urban thermal driving factors, and validated the effectiveness of the 6 key factors directly influencing the surface urban heat island intensity (SUHII). In response to the inadequacy of existing big data causal inference tools in assessing the spatial heterogeneity of causal effects, this study proposed a method for evaluating the causal effects on SUHII and their spatial heterogeneity based on local analysis and geospatial causal principle. The result for 4 different intervention scenarios in this study show that there is obvious spatial heterogeneity in the causal effects of different interventions on SUHII in Wuhan, and that increasing greenery and preserving natural environments is an effective way to mitigate the urban heat island (UHI) effect. This approach, provides a new perspective for studying the phenomenon of UHI and insights of potential approaches for mitigating UHI.

Comparative Study of Small-Area Population Prediction Methods, GWR, and CCR: A Case Study of Wuhan, China

Comparative Study of Small-Area Population Prediction Methods, GWR, and CCR: A Case Study of Wuhan, China

A Post Occupancy Evaluation of the Space Utilization of Cultural Heritage in Children’s Education: A Case Study of Wuhan’s Historical Districts, China

Cultural heritage has educational value because it provides children with a realistic learning environment and a wealth of educational resources. This study intends to fill a gap in academic research by evaluating the efficiency of utilizing cultural heritage in children’s education. This study uses the Post Occupancy Evaluation (POE) method to create a comprehensive evaluation index system. Through research and analysis of six key elements—inheritance of historical context, improvement in environmental quality, completion of infrastructure, achievement of educational goals, participation in children in activities, and sustainable development—it was discovered that rational utilization of cultural heritage space significantly improves the educational experience and promotes cultural heritage transmission. This study is significant since it provides an empirical foundation for educational practice and serves as a valuable guide for innovative heritage transmission and policy creation. Our findings emphasize the necessity of incorporating educational goals into preserving and revitalizing cultural heritage, advocating for a forward-thinking approach that honors the past while fostering learning and progress for future generations.

The impact of street-scale built environments on urban park visitations: A case study in Wuhan

The COVID-19 pandemic has changed human life globally. Existing studies have revealed that citizens' visitations to urban parks varied before and after the COVID-19 outbreak. However, few studies have examined how street-scale built environments (SBEs) on routes affect visitations to urban parks at varying COVID-19 risk levels. In this study, a stated-preference survey was conducted to investigate 3,218 visitors' changes in urban park visitation under various COVID-19 risk levels. In addition to park visit influencing factors, including park features, neighborhood built environment, socio-demographic attributes, and travel distances, multiple SBE indexes on visitors' routes to parks were obtained from 34,780 Baidu Map street view images using a deep neural network (DeepLabv3+) method. The results suggest that a high GVI and high traffic congestion on the route from the visitor's home to the urban park led to an increased probability of visiting the urban park by 188.1% (p = 0.044, OR = 2.881) and a decreased probability by 32.3% (p = 0.049, OR = 0.677), respectively. The high probability of visitation was also associated with socio-demographic attributes (including male gender, high income, high and medium education levels, and the elderly) and short travel distances.

Meteorological effects of ventilation corridor in central urban areas: A case study of Wuhan

How ventilation corridors affect urban climate is attracting researchers' attention. Taking the inland Chinese city of Wuhan as an example, this paper first uses remote sensing image technology to evaluate the urban thermal environment. Additionally, based on the GIS/RS spatial analysis method, the ventilation corridors in the central urban area are identified and constructed. Finally, the mesoscale meteorological model WRF-UCM is used to simulate four cases with different corridor forms to explore the impact of different corridors on the climate environment in Wuhan during the summer. The results indicate that: (1) The WRF-UCM model, when coupled with LCZ classification, can significantly improve the accuracy of mesoscale urban canopy meteorological field simulations. (2) The water corridors located in the central urban area can effectively regulate the temperature and wind environment during summer. In the high-temperature period of the day, the average temperature in the central city decreases by 0.3–0.4 °C, the heat island proportion index decreases by 1.61 %, and the strong heat island proportion index decreases by 1.89 % in the afternoon. During the period of low temperature, the average wind speed in the central urban area increased by 0.05 m/s increase, and even increased by 0.1 m/s. (3) The specific humidity value of the green corridor is reduced by 0.0000136 kg/kg in comparison to the construction land in the corridor, while the water corridor can increase by 0.000133 kg/kg. If the two kinds of surface, water and green land, are organically combined in the corridor, it will be able to improve the hot and humid conditions in Wuhan in summer. (4)Low-rise and low-density construction land as the corridors in the central urban area can not improve the urban thermal and wind environment. Through an attempt to conduct a complete workflow of urban heat island analysis, ventilation corridor identification and setting, urban climate simulation, analysis and summary, the authors believe that it is an effective set of working methods in sustainable urban planning, design, and policy-making. The implementation of pertinent research findings in the domain of urban planning and design demonstrates its universal applicability and has the potential to extend to analogous research and practice.

How does urban form explain water cooling effect heterogeneity: A case study in Wuhan, China

Water bodies in urban areas are considered to be natural and effective cooling factors that can help mitigate the urban heat island (UHI) effect. Previous studies highlighted the cooling effect is determined by the size and landscape of the water bodies. However, it is unclear how the urban form surrounding water bodies influences the water cooling effect received by human activity space. Using high resolution data and a multiple linear regression model, the effect of various 2D/3D urban form factors on the average water cooling intensity (AWCI) and water cooling intensity (WCI) at the fine scale were examined in Wuhan City on hot days. We further verified the spatial heterogeneity of the relationship between urban form factors and WCI according to the distance to and size of adjacent water bodies. The AWCI of the entire surrounding area were correlated with building morphology indicators. Urban form indicators effectively explained the variations of WCI at the fine scale, indicating that a mixture of large building volume density (BVD) with small building density (BD), and an aggregated impervious surface area (ISA) with a complex shape were conducive to WCI. Additionally, building morphology had a larger impact on the cooling effect around small water bodies and in the outer buffer area of water bodies. In comparison, the cooling effect of the area surrounding large water bodies was more affected by the landscape of the ISA. We therefore propose a differentiated urban planning strategy to take full use of the limited water cooling effect.

3D visualisation method for urban road waterlogging based on mobile augmented reality

ABSTRACT 3D waterlogging visualisation is an immersive method to understand how water accumulates in specific areas. Since urban waterlogging involves complex subsystems above and below the ground, augmented reality (AR) visualisation is especially effective for risk perception. In this paper, a 3D visualisation method for urban road waterlogging based on mobile augmented reality is proposed. The waterlogging situation is simulated by integrating open-source Storm Water Management Model (SWMM) and Weighted Cellular Automata 2D Inundation Model (WCA2D). The 3D pipe network and road inundation volume objects are constructed and organised as the basic geospatial objects for AR visualisation. Then, the simulated flow results are mapped and fused with 3D objects, achieving enhanced waterlogging visualisation based on mobile augmented reality. The method is applied to a case study in Wuhan, China. The results demonstrate that underground pipe drainage and surface runoff processes can be presented intuitively from the perspective of mobile AR. The scene rendering organised by the multilevel index is smooth, with an FPS of more than 30 frames. This research provides methodological and practical references for the general public to increase waterlogging awareness and make emergency decisions.

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53 %. Building energy consumption for high-rise pavilion typology is saved by 9.64 % than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design factors on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

A novel collocation method with a coronavirus optimization algorithm for the optimal control of COVID-19: A case study of Wuhan, China

In this study, we develop a numerical optimization approach to address the challenge of optimal control in the spread of COVID-19. We evaluate the impact of various control strategies aimed at reducing the number of exposed and infectious individuals. Our novel approach employs Legendre wavelets, their derivative operational matrix, and a collocation method to transform the COVID-19 transmission optimal control model into a nonlinear programming (NLP) problem. To solve this problem, we employ a coronavirus optimization algorithm (COVIDOA) to determine the optimal control, state variables, and objective value. We investigate three control plans for this highly contagious disease, focusing on individual protection, rapid detection and treatment, detection with delay in treatment, and environmental viral dispersion as time-based control functions. These strategies are applied within an SEIR-type control model specific to COVID-19 in China, designed to mitigate disease spread. Lastly, we analyze the effects of various parameters within the COVID-19 spread model. Our numerical results highlight the significant impact of strategies that minimize the number of exposed and infectious individuals, particularly those related to rapid detection, detection delay, and environmental viral dispersion, in controlling and preventing the transmission of the COVID-19 virus.

Enhancing outlying growth simulation in urban cellular automata via intelligent extraction-fusion of land suitability and neighborhood effects: a case study of Wuhan, China

ABSTRACT Urban growth has accelerated significantly in recent decades, categorized into two spatial patterns: adjacent and outlying growth. Traditional Cellular Automata (CA)-based models excel at simulating adjacent growth but exhibit limitations in modeling outlying growth, which can be summarized into two issues: 1) the overestimation of neighborhood effects caused by models’ excessive dependence on neighboring cell states; and 2) the ignorance of spatial heterogeneity in the relative importance of land suitability and neighborhood effects on urban growth. To address these problems, a novel CA model with Separate Extraction and Adaptive Fusion of land suitability and neighborhood effects (SEAF-CA) is proposed. In this model, a dual-path convolution structure is employed to extract spatial features from driving factors and cell states; geographical coordinates of each cell then input into a multilayer perceptron to derive spatially varying weights for feature fusion. Finally, the derived conversion probability is integrated with CA to simulate urban growth. Land use data collected from 2000 to 2020 in Wuhan are selected to evaluate the proposed model. Experimental results illustrate that SEAF-CA outperforms three typical CA models, achieving the closest outlying growth proportion to reality and the highest simulation accuracy. In addition, the source code of SEAF-CA is now available at GitHub (https://github.com/ohXu/SEAF-CA).

A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan

The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena.

The Synergistic Relationship between Low-Carbon Development of Road Freight Transport and Its Economic Efficiency—A Case Study of Wuhan, China

Road freight transport, an essential component of the logistics sector, faces challenges: high cost, low efficiency, and environmental impact. The need has become urgent to achieve a synergistic balance between low-carbon and high-efficiency development. This study used a three-stage DEA–Malmquist index model to analyze the road freight efficiency of Wuhan and 16 other cities in China from 2015 to 2020, and we compared Wuhan’s performance with its peers. In addition, grey correlation analysis was used to evaluate the low-carbon development of urban road freight transport in Wuhan. Through the calculation of the degree of synergy between low-carbon development and freight transport efficiency, this study provides insights into the synergistic development of low-carbon and efficient road freight transport in Wuhan. The key findings show that the total factor productivity of road freight transport in Wuhan was generally on a downward trend from 2015 to 2020, and was lower than that of the average of the 17 selected Chinese cities. The carbon emissions of road freight transport in Wuhan inversely related to its scale efficiency. This study also points out that the synergy between low-carbon development and the economic efficiency of road freight transport in Wuhan is not high and needs to be further integrated and optimized.

Tree form characteristics as criteria for tree species selection to improve pedestrian thermal comfort in street canyons: Case study of a humid subtropical city

The combined effect of tree characteristics on heat stress in different street canyons has been widely reported. However, there remains a lack of effective methods for improving pedestrian thermal comfort through the use of integrated knowledge of tree characteristics. Here, a tree species selection approach was developed for assessing the dynamic effects of tree forms and canyon geometries on pedestrian thermal comfort to address the issue of the right tree in the right street canyon. The leaf area density (LAD), crown diameter (CD), tree height (HT), and trunk height (TH) were used as criteria for determining tree species characteristics, and the weighted outdoor thermal comfort autonomy (OTCAw) was adopted as a quantitative index of thermal comfort variation over time. In a case study of Wuhan, China, we evaluated the thermal comfort enhancement potential of 45 tree forms in street canyons with varying aspect ratios and orientations from 7:00 to 21:00 by using a validated ENVI-met model. Accordingly, tree pruning strategies and 19 optimal tree forms were proposed for corresponding street canyons. Our study provides insight into the optimal street tree species for minimizing heat exposure of pedestrians under various urban canyon configurations.

Assessment of land use change of ecological green wedge and cooling island effect: A case study of Wuhan, China.

The construction of urban ecological green wedges, which can mitigate the heat island effect through cooling and ventilation effects, is an important way to enhance the adaptation of cities to climate change. Dynamic monitoring and periodic assessment of both the conservation status and cooling effect of ecological green wedges is a key to ensure the heat mitigation benefits. Based on multi-source remote sensing data, we systematically analyzed the land use changes of six ecological green wedges in Wuhan in 2013 and 2020 using the methods of Markov transfer matrix, land use dynamics, and comprehensive index of land use degree, and evaluated the changes in surface temperature of the ecological green wedges and their cooling island effect. Results showed that the ecological green wedges in Wuhan generally had a large amount of construction land encroaching on ecological land from 2013 to 2020, with the water decreased the most. With the continuous deterioration of ecological green wedges, their land surface temperatures showed rising trends, together with significant weakening trends in cooling island effects. Among all the six wedges, the Dadonghu, Tangxun, and Wuhu exhibited relatively better ecological conservation, slighter land use change and lower overall development degree. Qinglinghu and Houguanhu demonstrated average levels of conservation. Fuhe experienced the most severe change under the significant influence of the westward policy of Wuhan City, with the proportion of water decreasing by 7.1%, warming up by 3.00 ℃, and the largest reduction in cooling distance for the cooling island effect, amounting to about 210 m. The results provided scientific evidence for the urban heat island mitigation-oriented planning and management of ecological green wedges for Wuhan City.

Exploring the Willingness of Chinese Urban Dwellers to Support Community Gardening: A Case Study of Wuhan, China

As a new project associated with urban agriculture and community forestry in China, developing community gardens has been a concern of urban planners and managers. However, the response of urban residents to this project is still unknown, creating an awareness gap between communities and policy makers. This gap limits the successful implementation of the project. A combined method of on-site and online questionnaire was performed in eight sample residential areas in Wuhan. Results showed that the majority of the 496 respondents had favorable views, although the specific willingness varied. Lack of awareness about community gardens was considered the biggest obstacle restricting residents’ willingness, accounting for 69.15%. Other factors included confused relationships with neighbors (66.13%), imperfect policies (55.44%), and high planting costs (41.94%). The local government was highly expected to plan more land for agriculture use by 76.61% of the respondents. In addition, the respondents desired the government to conduct training on cultivating technologies (58.67%), address possible conflicts (57.46%), supply seeds and fertilizer (54.23%), and provide guidance in processing food products (34.48%). These findings will help local governments and managers draft scientific proposals closely related to the public’s perspectives in implementing community gardens in urban areas and provide informative references for other cities.

Heterogeneity, Differentiation Mechanisms and Social Effects of Urban Residential Space in China’s Large Cities: A Case Study of Wuhan

Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China’s big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources.

Multi-source data-driven estimation of urban net primary productivity: A case study of Wuhan

Amidst urban expansion, numerous ecological challenges have surfaced, notably the swift alterations in the net primary productivity (NPP), presenting complexities in discerning their scale and spatial distribution. Leveraging meteorological data, multi-modal remote sensing datasets, field sampling data, and a digital elevation model (DEM), this research amalgamates field assessments, model simulations, and cutting-edge remote sensing techniques to craft a city-scale NPP estimation model for vegetation rooted in the Carnegie-Ames-Stanford Approach (CASA). This improved CASA model facilitated the computation of monthly NPP for Wuhan over 2017–2020. Our analysis divulged NPP spatiotemporal trends and distribution nuances in Wuhan, and assessed meteorological influences on the same. Salient conclusions encompass: (1) Wuhan's annual NPP maximum between 984.40 and 1310.51 gC/(m2 a), averaging from 316.78 to 430.32 gC/(m2 a), and culminating in totals from 170.15 to 231.60 × 1010 gC/a. (2) Monthly NPP trajectories in Wuhan exhibited a unimodal pattern: ascending from January to June, peaking in July–August, and tapering off through September-December, with a spatial distribution characterized by lower NPP values centrally and higher values peripherally. (3) Regionally, temperature markedly influenced NPP over precipitation. This research underpins urban ecological planning, offering empirical insights and strategic guidance for fostering a sustainable coexistence between humans and their environment, and charting future urban development paradigms.

Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China.

COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information. Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk. Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points. Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.