Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China

Liu-qing, Yang; Yu, Kunyong; Ai, Jingwen; Liu, Yanfen; Yang, Wufa; Liu, Jian

doi:10.3390/rs14051266

Cited by 43 publications

(28 citation statements)

References 65 publications

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“…Meanwhile, previous studies have mainly investigated the model's fitting condition for the surface temperature in summer, meaning there is a lack of studies reported for other seasons [34,35]. This paper's results indicate that the surface temperature and spatial morphological parameters of Tianjin's old urban area have significant spatial non-smoothness, that the MGWR model's fit performance is better than the GWR model's in several seasons, and that these results are similar to those Basu [22] and Yang [36], where the summer fit superiority was higher than 0.75. The study by Liu [37] on the central city of Wuhan's surface temperature also proved the MGWR model's excellent performance.…”

Section: Does Mgwr Provide a New Perspective For Urban Thermal Enviro...mentioning

confidence: 59%

Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation

2023

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High-density urban areas have spatial characteristics, such as complex functions, population gathering, and complex forms, that lead to more severe urban heat island effects. Systematically evaluating the thermal environmental benefits of urban spatial forms to optimize the urban physical environment is important. In this study, Tianjin’s central urban area, which is a typical representative of high-density urban areas, was selected to invert the multi-period land surface temperature by relying on the existing two- and three-dimensional morphological data set of communities. The multi-scale geographically weighted regression model was used to fit the regression relationship between the urban land surface temperature and spatial morphological parameters. From this, the influencing factors of different types of existing community spaces and their spatial stabilities were explored. The results show the following: (1) The summer surface temperature varies greatly in the central urban area, and the high-temperature areas are mainly distributed in the industrial, residential, and commercial districts. (2) The MGWR model has the better model-fitting ability. The positive influence coefficients of temperature include ISP and BD, while the negative influence coefficients are BSD, BH, NDVI, and SVF. (3) There is significant spatial heterogeneity in the impact coefficients among the blocks that can be targeted to mitigate the heat island effect. This study provides ideas for optimizing the spatial morphological parameters of surface temperature in urban centers. Future challenges include increasing the spatial morphological parameter selection range, dissecting the interactive relationships between spatial morphological parameters and their effectiveness on the surface temperature, and refining the study’s spatial and temporal granularity.

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Section: Does Mgwr Provide a New Perspective For Urban Thermal Enviro...mentioning

confidence: 59%

Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation

2023

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“…GWR is a local modeling tool based on the optimization of global regression models, which complements the global model by providing a set of coefficients for each geographic unit to determine the spatial variability of the observations [ 38 ]. GWR was used to explore the spatial heterogeneity of risk factors in this study.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Wang

Dong

Yang

2022

IJERPH

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Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing–Tianjin–Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing–Tianjin–Hebei region are high-risk areas for diabetes.

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“…The OLS model is a global regression model applied to evaluate the relationship between two or more elements. The advantage of the OLS model is that it minimizes the sum of squares of the residuals for all observations (Yang et al, 2022). This study initially explored the factors influencing the spatial layout of family farms using the OLS model.…”

Section: Ordinary Least Squares Regression (Ols)mentioning

confidence: 99%

“…To determine the best bandwidth value, we adopted the corrected Akaike information criterion (AICc) to search for the best value (Yang et al, 2022).…”

Section: Geographically Weighted Regression (Gwr)mentioning

confidence: 99%

Spatiotemporal dynamic evolution and influencing factors of family farms in urban agglomerations in the middle reaches of the Yangtze River

Ren

et al. 2022

Front. Environ. Sci.

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Family farms, considered the most desirable form of Chinese agriculture, play a pivotal role in promoting rural revitalization and agricultural modernization. The purpose of this study was to summarize the spatiotemporal evolution characteristics and influencing factors of family farms to better promote the development of modern agriculture. Using provincial demonstration family farms in the urban agglomeration in the middle reaches of the Yangtze River (MYR-UA) as the research object, this study applied the nearest neighbor index, kernel density analysis, multiscale spatial clustering analysis (Ripley’s K-function), and geographically weighted regression (GWR) model to reveal the spatiotemporal dynamic evolution and influencing factors of family farms. The results indicate that: 1) from 2013 to 2021, family farms exhibited annual increases, and their development stages could be divided into rapid, stable, and slow growth periods. 2) The spatial agglomeration pattern of family farms was significant, and the intercepted points at different time periods show the distribution characteristics of the entire dispersion and local concentration. The spatial evolution characteristics of different types of family farms are nearly consistent with those of the overall family farms. 3) The overall family farms and various types of family farms show a scale effect, which first strengthens and then weakens with the change in geographical distance. 4) The spatial pattern of family farms in MYR-UA is affected by both natural and social factors, of which, social factors had the greatest influence. Finally, based on the findings of the study, policy recommendations for promoting the high-quality development of family farms are proposed.

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Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China

Cited by 43 publications

References 65 publications

Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation

Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Spatiotemporal dynamic evolution and influencing factors of family farms in urban agglomerations in the middle reaches of the Yangtze River

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