Optimizing Thermal Comfort in Urban Squares of Hot-Humid Regions: A Case Study Considering Tree Growth, Species, and Planting Intervals

Atmosphere, Journal Year: 2025, Volume and Issue: 16(1), P. 63 - 63

Published: Jan. 9, 2025

The worsening urban thermal environment has become a critical challenge in many cities. Trees, as vital components of green spaces, provide multiple ecosystem services, especially improving the microclimate. However, limited studies address how morphological changes during tree growth influence their cooling benefits. This study combined model with ENVI-met to simulate 27 scenarios subtropical square, considering three planting intervals, species, and stages evaluate daytime impacts. key findings include: (1) Tree size intervals are more important than quantity enhancing comfort. (2) Reducing by 2 m enhances effects but minimally affects PET (physiological equivalent temperature). (3) Increasing DBH (diameter at breast height) significantly improves cooling. For every 10 cm increase DBH, Michelia alba, Mangifera indica, Ficus microcarpa L. f. reduced solar radiation 19.54, 18.09, 34.50 W/m2, mean radiant temperature 0.61 °C, 0.68 1.35 respectively, while decreasing 0.23 0.46 °C. These empirical evidence practical recommendations for designing comfortable open spaces

Language: Английский

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Interpretable Machine Learning Insights into the Factors Influencing Residents’ Travel Distance Distribution

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(1), P. 39 - 39

Published: Jan. 20, 2025

Understanding intra-urban travel patterns through quantitative analysis is crucial for effective urban planning and transportation management. In previous studies, a range of distribution functions were modeled to lay the groundwork human mobility research. However, few studies have explored nonlinear relationships between distance environmental factors. Using data from ride-hailing services, this research divides study area into 1 × km grid cells, modeling best calculating coefficients each grid. A machine learning framework (Extreme Gradient Boosting combined with Shapley Additive Explanations) introduced interpret factors influencing these distributions. Our results emphasize that movement tends follow log-normal exhibits spatial heterogeneity. Key affecting distributions include city center, bus station density, land use entropy, density companies. Most variables exhibit threshold effects on coefficients. These findings significantly advance our understanding offer valuable insights dynamics mobility.

Language: Английский

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Research on the Nonlinear and Interactive Effects of Multidimensional Influencing Factors on Urban Innovation Cooperation: A Method Based on an Explainable Machine Learning Model

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 187 - 187

Published: March 7, 2025

Within globalization, the significance of urban innovation cooperation has become increasingly evident. However, faces challenges due to various factors—social, economic, and spatial—making it difficult for traditional methods uncover intricate nonlinear relationships among them. Consequently, this research concentrates on cities within Yangtze River Delta region, employing an explainable machine learning model that integrates eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs) investigate interactive effects multidimensional factors impacting cooperation. The findings indicate XGBoost outperforms LR, SVR, RF, GBDT in terms accuracy effectiveness. Key results are summarized as follows: (1) Urban exhibits different phased characteristics. (2) There exist between factors, them, Scientific Technological dimension contributes most (30.59%) significant positive promoting effect later stage after surpassing a certain threshold. In Social Economic (23.61%), number Internet Users (IU) individually. Physical Space (20.46%) generally mutation points during early stages development, with overall predominantly characterized by trends. (3) Through application PDP, is further determined IU synergistic per capita Foreign Direct Investment (FDI), public library collections (LC), city night light data (NPP), while exhibiting negative antagonistic Average Annual Wage Staff (AAS) Enterprises above Designated Size Industry (EDS). (4) For at developmental stages, tailored development proposals should be formulated based single-factor contribution multifactor interaction effects. These insights enhance our understanding elucidate influencing factors.

Language: Английский

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Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 1056 - 1056

Published: March 17, 2025

Urban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing direct effects intensity (VI) its influencing factors, while paying less attention to diversity (VD) indirect impact mechanisms. Supported by multisource remote sensing data, this study establishes five-dimensional evaluation system employs Partial Least Squares Structural Equation Model (PLS-SEM) quantify interrelationships between these multidimensional factors VI/VD. The findings are follows: (1) Spatial divergence VI VD: exhibited stronger clustering (I = 1.12), aggregating in central areas, whereas VD demonstrated moderate autocorrelation 0.45) concentrated mixed-use or suburban zones. (2) Drivers intensity: strongly associated with commercial density (β 0.344) transportation accessibility 0.253), but negatively correlated natural environment quality (r −0.166). (3) Mechanisms diversity: is closely linked public service 0.228). This research provides valuable insights city decision-making, particularly strengthening optimizing functional layouts.

Language: Английский

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Research on the influence of spontaneous commercial space on the commercial vitality of historical and cultural districts

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 26, 2025

Spontaneous commercial spaces play a crucial role in shaping the vitality of historic districts, yet their spatial characteristics and impact on activity remain understudied. This study employs Mask R-CNN deep learning, random forest regression analysis, SHAP (Shapley Additive Explanations) to systematically identify quantify influence spontaneous vitality. Based dataset comprising 4217 annotated images collected from Wuhan's Tanhualin Historic District, classifies into five types examines correlation with distribution. The results reveal that convex scatter-occupying have most significant positive impact, increasing by an average 22.4% 16.8%, respectively. analysis further highlights nonlinear interactions between crowd density typology, demonstrating high-density areas amplify contribution Additionally, shows strong pedestrian flow intensity (R2 = 0.9062, p < 0.01), indicating critical local economic dynamics. Compared traditional manual research methods, computer vision interpretable machine learning approaches employed this enhance analytical efficiency causal clarity, providing urban planners robust framework for monitoring evaluating spaces. Furthermore, we propose predictive evaluate potential existing streets future development. model suggests 12–18 points per 100 square meters exhibit highest vitality, offering reference renewal strategies.

Language: Английский

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Predicting Urban Vitality at Regional Scales: A Deep Learning Approach to Modelling Population Density and Pedestrian Flows

Smart Cities, Journal Year: 2025, Volume and Issue: 8(2), P. 58 - 58

Published: March 30, 2025

Understanding and predicting urban vitality—the intensity diversity of human activities in spaces—is crucial for sustainable development. However, existing studies often rely on discrete sampling points single metrics, limiting their ability to capture the continuous spatial distribution vibrancy. This study introduces UVPN (urban vitality prediction network), a novel deep-learning architecture designed generate high-resolution predictions static dynamic at regional scales. The integrates two key innovations: SE (squeeze-and-excitation) block adaptive feature recalibration an RCA (residual connection with coordinate attention) bottleneck position-aware learning. Applied New York City, leverages diverse morphological features such as streetscape attributes land use patterns predict distributions. model outperforms architectures, achieving reductions 34.03% 38.66% mean squared error population density pedestrian flow predictions, respectively. Feature importance analysis reveals that road networks predominantly influence density, while strongly affect flows, built interest contributing both dimensions. By advancing prediction, provides robust framework evidence-based planning, supporting creation more sustainable, functional, livable cities.

Language: Английский

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Graph-based machine learning for high-resolution assessment of pedestrian- weighted exposure to air pollution

Resources Environment and Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100219 - 100219

Published: April 1, 2025

Language: Английский

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Nonlinear effects of multilevel factors on public transport commuting in China’s cities

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: 143, P. 104724 - 104724

Published: April 9, 2025

Language: Английский

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Spatiotemporal Analysis of Urban Vitality and Its Drivers from a Human Mobility Perspective

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(4), P. 167 - 167

Published: April 11, 2025

Urban vitality is a critical metric for assessing the development and appeal of urban areas, playing pivotal role in planning management. Traditionally, surveys census data have been used to measure vitality; however, these methods are often time-consuming, resource-intensive, limited coverage. This study addresses limitations by employing mobile phone signaling develop model quantifying exploring its spatiotemporal distribution patterns. By integrating socioeconomic, street view, points-of-interest (POI) data, this utilizes linear regression geographically weighted (GWR) models analyze influence various factors on vitality. The SHapley Additive exPlanations (SHAP) method then applied interpret predictions identify key determinants Using Shenzhen as case study, results reveal pronounced spatial disparities Among all variables, bus stop density, cultural services, employment density consistently exhibit significant effects proposed quantification framework enables high-resolution wide-coverage monitoring vitality, providing scientific support decision-making guidance understanding dynamic characteristics spaces optimizing functional layouts.

Language: Английский

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Construction and analysis of atmospheric visibility and fog-haze datasets in China (2001−2023) based on machine learning models

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108160 - 108160

Published: April 1, 2025

Language: Английский

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