Understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach

Han, Dongrui; An, Hongmin; Wang, Fei; Xu, Xinliang; Qiao, Zhi; Wang, Meng; Sui, Xueyan; Liang, Shouzhen; Hou, Xiudan; Cai, Hongyan; Liu, Yihui

doi:10.1016/j.buildenv.2022.109770

Cited by 69 publications

(14 citation statements)

References 70 publications

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“…BD accounts for a relatively large proportion of each regression model parameter and has a large impact on LST; study areas with high density buildings tend to have poor ventilation, less green permeable surfaces, and large impervious surfaces are able to absorb a large amount of radiation during the day, causing a sharp increase in temperature in the study area and seriously affecting living comfort; this has similarities to the conclusions of other scholars under other climatic regions in China that urban density and height are the main factors affecting the urban thermal environment (37)(38)(39). High temperatures in urban neighborhoods caused by high building density can be mitigated by adding vegetation to the building envelope and changing the substrate (40,41).…”

Section: Regression Model Analysis Resultssupporting

confidence: 61%

Study on the mechanism of urban morphology on river cooling effect in severe cold regions

Guo

Zhao

et al. 2023

Front. Public Health

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In the context of global warming, urban climate problems such as heat waves, urban heat islands and air pollution are becoming increasingly prominent, and the cooling effect of rivers is an effective way to mitigate urban hot climate. This study investigates the surrounding urban area of the Hun River in Shenyang, a severe cold region of China, by calculating satellite inversion surface temperature and urban morphology data, and explores the cooling effect of rivers using linear regression models and spatial regression models. The results show that (1) water bodies have a cooling effect on the surrounding environment, with the farthest cooling distance being 4,000 m, but the optimal cooling distance being 2,500 m. (2) In the results of the spatial regression model analysis, the R2 value stays above 0.7 in the range of 0–4,000 m, indicating that urban morphological factors are closely related to LST (land surface temperature). The negative correlation is most pronounced for NVDI (normalized vegetation index), with a peak of −14.8075 calculated by the regression model, and the positive correlation is most pronounced for BD (building density), with a peak of 8.5526. (3) The urban thermal environment can be improved and the heat island effect mitigated through measures such as increasing urban vegetation cover and reducing building density, and these findings can provide data references and case studies to support urban planning and development departments.

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Section: Regression Model Analysis Resultssupporting

confidence: 61%

Study on the mechanism of urban morphology on river cooling effect in severe cold regions

Guo

Zhao

et al. 2023

Front. Public Health

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“…there are correlations between the urban population, the built environment, and land surface temperatures [39][40][41][42], and that land surface temperatures are influenced more by the built than natural environment [43]. Another study shows that the relationship between these factors and urban temperature is nonlinear [44]. These pieces of evidence suggest that populations might begin to decline as temperatures exceed a threshold.…”

Section: Plos Onementioning

confidence: 99%

Urban modeling of shrinking cities through Bayesian network analysis using economic, social, and educational indicators: Case of Japanese cities

Kato¹

2023

PLoS ONE

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Shrinking cities due to low birthrates and aging populations represent a significant urban planning issue. The research question of this study is: which economic, social, and educational factors affect population decline in Japanese shrinking cities? By modeling shrinking cities using the case of Japanese cities, this study aims to clarify the indicators that affect the population change rate. The study employed Bayesian network analysis, a machine learning technique, using a dataset of economic, social, and educational indicators. In conclusion, this study demonstrates that social and educational indicators affect the population decline rate. Surprisingly, the impact of educational indicators is more substantial than that of economic indicators such as the financial strength index. Considering the limitations in fiscal expenditures, increasing investment in education might help solve the problem of shrinking cities because of low birthrates and aging populations. The results provide essential insights and can function as a planning support system.

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“…In addition, the data on the administrative divisions of China's prefecture-level cities used to produce the thematic maps came from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences(RESDC). Some scholars used the architectural data, meteorological data and GF1 images provided by this database to study the influence of different urban forms on urban surface temperature in Beijing and Guangzhou [22][23][24]. The above articles provide good data source and method basis for the work of map editing and data analysis in this paper.…”

Section: Data Sourcesmentioning

confidence: 99%

Characteristics and evolution of knowledge innovation network in the Yangtze River Delta urban agglomeration——A case study of China National Knowledge Infrastructure

Gao

2023

PLoS ONE

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With the development of economic globalization, urban agglomerations have become growth poles and core areas of economic development. By building knowledge innovation networks in urban agglomerations, we can effectively improve the strength of inter-city knowledge innovation links and better realize the integrated and synergistic development of the region. This study selected core cities in the Yangtze River Delta urban agglomeration as the study area, constructed the knowledge innovation network based on inter-city dissertation cooperation data from 2010 to 2020, and analyzed the characteristics and evolution of its knowledge network by combining social network analysis and geospatial analysis. The research results show that: (1) with changes in policies and investment in scientific research and innovation, intra-regional thesis cooperation in the Yangtze River Delta urban agglomeration has been increasing and the scale of the knowledge innovation cooperation network is growing; (2) in addition to the core cities radiating innovation resources outward to drive the development of other node cities, other cities are continuously improving their own innovation capabilities, taking the initiative to strengthen knowledge innovation cooperation with core cities and enhancing their own position in the network; (3) there are no longer isolated cities within the Yangtze River Delta urban agglomeration, and a multi-core knowledge network structure centered on Shanghai, Nanjing, Hangzhou, and Suzhou has initially formed, but the network is still spatially heterogeneous; (4) there are still problems within the Yangtze River Delta urban agglomeration such as uneven development of knowledge innovation and low participation of peripheral cities, which need to be addressed jointly by all regions. The article concludes with some suggestions for countermeasures to provide a reference for the Yangtze River Delta urban agglomeration to continuously strengthen intra-regional knowledge cooperation in the future, enhance regional competitiveness, and ultimately achieve synergistic development among cities.

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Understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach

Cited by 69 publications

References 70 publications

Study on the mechanism of urban morphology on river cooling effect in severe cold regions

Study on the mechanism of urban morphology on river cooling effect in severe cold regions

Urban modeling of shrinking cities through Bayesian network analysis using economic, social, and educational indicators: Case of Japanese cities

Characteristics and evolution of knowledge innovation network in the Yangtze River Delta urban agglomeration——A case study of China National Knowledge Infrastructure

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