Sky View Factor Research Articles

Urban Morphology Influencing the Urban Heat Island in the High-Density City of Xi’an Based on the Local Climate Zone

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

Impact of urban street geometry on outdoor pedestrian thermal comfort during heatwave in Nagpur city

Rapid urbanization poses challenges to sustainable transportation, impacting walking as a common mode for accessing transit stations. The Urban Heat Island (UHI) effect, exacerbated by the built environment's heat-absorbing properties, intensifies urban heat stress, hindering outdoor activities and reducing walkability. Urban street geometry impacts thermal comfort through its influences on microclimate. This study aims to investigate the relationship between urban street geometry and pedestrian thermal comfort (PTC), examining how different geometries impact microclimatic conditions and thermal comfort along walking routes in Nagpur city during May 2022. It focuses on four streets with non-uniform urban geometries to investigate their impact on PTC. Field surveys were conducted to collect data at four different hours, from 9:00 a.m. to 6:00 p.m., utilizing MS 6252B Digital Anemometer to measure microclimatic parameters. Using RayMan Pro-software, Sky View Factor (SVF), Mean Radiant Temperature, and modified Physiologically Equivalent Temperature (mPET) were simulated. Correlation analysis revealed that SVF significantly influences PTC, with varying effects throughout the day. Results highlight temporal and spatial variations in microclimate and thermal comfort along street sides. Optimizing street orientation, aspect Ratio, and vegetation improves PTC. The study provides quantitative insights, emphasizing the importance of urban street geometry parameters in enhancing overall PTC.

Investigating the potential of integrated urban greening strategies for reducing outdoor thermal stresses: a case of asymmetrical configuration in the tropical city of Bhopal.

In Indian cities where streets are the only affordable outdoor public space, pedestrians are always exposed to extreme heat related health risk. However, it's a challenge to reduce heat stress in existing streets characterized by asymmetrical urban configuration. Integrating vegetation without reconstituting the original orientation and geometry is one of the feasible ways to alleviate stress. Therefore, current study focuses to analyse the heat stress reduction potential of urban greenery strategy in asymmetrical urban configuration from spatiotemporal perspective. It initiates with the selection of commercial streets in extreme hot climate with an on-site measurement of its climatic and morphological attributes. Furthermore, it leads to the classification and prioritizing of street's sections linked to hot-spots determined by varied sky view factor and asymmetrical aspect ratio. Finally, an Envi-Met model with iterated scenarios at the building and street levels is developed, incorporating three strategies (trees, grass, green-walls). The impact of heat related health risk is quantified using a thermal index Universal Thermal Climate Index along with air temperature and mean radiant temperature. The results suggested that due to asymmetricity a fixed strategy would not be applicable across the street. The highest reduction was observed by trees in asymmetrical sections while lowest was recorded by green-wall. However, it would be worthwhile to adopt green-wall along with dense tree's (leaf area density, 0.3) in order to reduce the heat stress in deeper sections. The evidence-based integration of Urban greenery can assist planners and designers in mitigating extreme heat stress in similar complex urban environment.

Exploring the Effects of Topography on Leaf Area Index Retrieved from Remote Sensing Data at Various Spatial Scales over Rugged Terrains

Topography significantly affects remotely sensed reflectance data and subsequently impacts the retrieval of the leaf area index (LAI) from surface reflectance data over rugged terrains. However, most LAI inversion algorithms ignore the influence of terrain. This paper quantitatively analyzes the topographic effects on LAI values retrieved from remote sensing data at various spatial scales (30, 90, 270, 540, 1080, and 5400 m) over rugged terrains. The PRO4SAILT (PROSPECT + 4SAILT) model and the Proy algorithm were used to simulate multiscale surface reflectance for different LAI values over rugged terrains. Based on Gaussian process regression (GPR), an LAI inversion algorithm that ignores terrain effects was first developed. The simulated multiscale reflectance data were subsequently input into the inversion algorithm to retrieve LAI values. Finally, the retrieved LAI values were compared with the corresponding reference LAI values. The results demonstrate that the finer the spatial resolution is, the more significant the topographic effects on the retrieved LAI values are. When the reference LAI is five, as the spatial resolution increases from 5400 m to 30 m, the mean percentage error (MPE) of the retrieved LAI increases from 10.46% to 13.72%, and the root mean square error (RMSE) increases from 0.5376 to 1.005. Regardless of the spatial resolution, the error in the retrieved LAI values increases with an increasing terrain slope. When the reference LAI is five and the spatial resolution is 30 m, the MPE at a slope of 15°–30° is close to 5%, and the RMSE is close to 0.3. The MPE at a slope of 30°–45° is close to 20%, and the RMSE is close to one. In addition, the accuracy of the retrieved LAI values is closely related to the sky view factor (SVF). In general, the larger the SVF is, the smaller the error in the retrieved LAI values. In addition, the conversion relationships between the retrieved LAI values using the algorithm that ignores terrain effects and the true LAI values are provided in this study.

Urban morphology clustering analysis to identify heat-prone neighbourhoods in cities

Exposure to heat is a major health concern to urban populations. Cities aim to reduce outdoor thermal stress by adapting the built environment, but the spatial heterogeneity within cities makes it difficult to establish universal mitigation strategies. We present a methodology that identifies the hottest neighbourhoods in a city and links them to underlying patterns in urban form and function, to derive heat mitigation measures for individual neighbourhoods according to their characteristics, mitigation potential, and average surface temperature. The method applies k-means clustering and is applicable to any city using available datasets on surface cover and building morphology, as well as globally available satellite measurements of surface temperatures. Here, we present a heat-mitigation analysis for the city of Zurich. The clustering differentiates seven neighbourhood types, including two types of residential areas, modern neighbourhoods with high-rise buildings, historical districts, and industrial zones. The hottest temperatures are in neighbourhoods with extensive impervious ground cover such as railway tracks and airport parking. Surface temperatures strongly correlate with impervious surface cover and vegetation cover for all neighbourhoods, with building cover only for non-industrial built neighbourhoods, and with sky-view factor for all neighbourhoods except those with large vegetation cover. Historical, modern, and industrial neighbourhoods are particular heat-prone, and increasing vegetation for evaporative cooling is a suggested mitigation strategy for all. Modern and industrial areas could benefit from shading through increase of tree cover, while historical centres may adapt vertical greening as suitable heat mitigation strategy.

How Does the 2D/3D Urban Morphology Affect the Urban Heat Island across Urban Functional Zones? A Case Study of Beijing, China

Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.

Characterizing the local climate of large-scale archaeological parks in the tropics

PurposeThis study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.Design/methodology/approachThe study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.FindingsThe study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.Practical implicationsBased on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.Originality/valueThe local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

Influence of Various Urban Morphological Parameters on Urban Canopy Ventilation: A Parametric Numerical Study

Numerical simulation is vital for evaluating urban ventilation. However, accurate urban-scale ventilation modeling requires extensive building surface simulation for computational demand. The distributed drag force approach simplifies the urban canopy by modeling buildings as a porous volume that accounts for momentum and turbulence. This method is a practical solution for simulating urban airflow. The drag force coefficient (Cd) is a crucial aerodynamic parameter in this approach. This study examines how Cd varies with urban design parameters such as plan area density (λp), average building height (H), frontal area density (λf), floor aspect ratio (AR), and sky view factor (SVF). Employing extensive numerical simulations conducted under neutral atmospheric conditions, we explore ranges of λp = 0.04–0.07 and λf = 0.1–1.2. The numerical model has been validated against existing wind tunnel data. The results show that Cd is insensitive to the model scale and background wind speed. We discover a nonlinear relationship between Cd and the parameters λp, λf, and SVF. For urban layouts with cubic-shaped buildings, Cd peaks at different λp within the range of 0.2~0.8. When λp and H are constant, Cd has a linear relationship with AR and λf. It is recommended to use λp, SVF, and AR as predictors for Cd across various urban configurations.

Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China

The Sky View Factor (SVF) stands as a critical metric for quantitatively assessing urban spatial morphology and its estimation method based on Street View Imagery (SVI) has gained significant attention in recent years. However, most existing Street View-based methods prove inefficient and constrained in SVI dataset collection. These approaches often fall short in capturing detailed visual areas of the sky, and do not meet the requirements for handling large areas. Therefore, an online method for the rapid estimation of a large area SVF using SVI is presented in this study. The approach has been integrated into a WebGIS tool called BMapSVF, which refines the extent of the visible sky and allows for instant estimation of the SVF at observation points. In this paper, an empirical case study is carried out in the street canyons of the Qinhuai District of Nanjing to illustrate the effectiveness of the method. To validate the accuracy of the refined SVF extraction method, we employ both the SVI method based on BMapSVF and the simulation method founded on 3D urban building models. The results demonstrate an acceptable level of refinement accuracy in the test area.

Low-carbon-oriented commercial district urban form optimization and impact assessment analysis

Commercial buildings have become one of the main sources of carbon emissions. However, the current research on urban form optimization has lacked analyses of the low-carbon indicators of buildings. With the Beijing commercial district as an example, a low-carbon-oriented urban form optimization path is established in this study, and a new method of urban form optimization is developed from a low-carbon perspective. With the help of the parametric platform and multiobjective optimization algorithm, a dynamic optimization model of urban morphology based on parametric modeling, numerical simulation and algorithm optimization is developed, and the morphological characteristics of low-carbon commercial blocks in Beijing are assessed with a Pareto-optimal solution set. Through principal component regression analysis, the correlations between urban form factors and low-carbon evaluation indicators are discussed. The study shows that the multiobjective optimization algorithm effectively optimizes the low-carbon indicators of buildings at the block scale. The urban forms obtained from the Pareto solution set display several commonalities reflected from three perspectives: building type, vertical dimension and building orientation. It is found that the optimal block layout form reduces the building carbon emission intensity and the building outer skin radiation difference, and the renewable energy-based carbon reduction is large. The urban form factors that have a significant impact on the carbon emission intensity of buildings include the maintenance coefficients, the average area to perimeter ratio, and the sky view factor, with respective impact coefficients of 0.301, −0.309, and −0.319.

Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods

The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development.

A Systematic Review on Human Thermal Comfort and Methodologies for Evaluating Urban Morphology in Outdoor Spaces

The exponential growth of urban populations and city infrastructure globally presents distinct patterns, impacting climate change forecasts and urban climates. This study conducts a systematic review of the literature focusing on human thermal comfort (HTC) in outdoor urban environments. The findings indicate a significant surge in studies exploring HTC in open urban spaces in recent decades. While historically centered on Northern Hemisphere cities, there is a recent shift, with discussions extending to various metropolitan contexts in the Southern Hemisphere. Commonly employed urban categorization systems include Sky View Factor (SVF), Height × Width (H/W) ratio, and the emerging Local Climate Zones (LCZs), facilitating the characterization of urban areas and their usage. Various thermal indices, like Physiological Equivalent Temperature (PET), Predicted Mean Vote (PMV), Universal Thermal Climate Index (UTCI), and Standard Effective Temperature (SET), are frequently utilized in evaluating external HTC in metropolitan areas. These indices have undergone validation in the literature, establishing their reliability and applicability.

Sky view factors of curved surfaces

Photovoltaic modules are integrated into rooftop buildings, canopies, solar carports, bus stops, greenhouses, known as Building-Integrated Photovoltaics (BIPV). Rooftops may have curved surfaces. The incident diffuse irradiation on curved surface is not uniformly distributed along the surface because the slope of the surface varies with the distance along the curvature. The non-uniform diffuse irradiation stems from the variation of the local sky view factor along the curved surface, resulting in mismatch power losses. The variation of local sky view factors of curved surfaces, in presence of obscuring structures, was not investigate in the past. The purpose of the present study is to develop a methodology for calculating the local sky view factors and the annual incident diffuse irradiation along the curvature of the surfaces. The demonstrations apply to convex and concave parabolic and elliptic surfaces, including curved surfaces obscured by flat, convex and concave nearby rooftops. The effect of an obscuring building near a curved parabolic rooftop results in a decrease of the annual incident diffuse irradiation by 9 percent for 10 m separation between the rooftop and the building. The present study may assist the designer of BIPV systems to assess the generated electric energy on curved rooftops.

Urban microclimate analysis: residential block morphology impact on outdoor thermal comfort

In many cities, protracted exposure of urban structures to excessive solar radiation and the urban heat island (UHI) phenomenon may cause thermal discomfort for pedestrians in outdoor spaces, particularly in hot-arid regions. This study investigated the impact of urban residential block morphology on microclimate in Isfahan, Iran, using ENVI-met numerical models. The predicted mean vote (PMV) model for residential outdoor spaces was calculated using simulation data for the hottest day of the summer to assess outdoor thermal comfort. Comparison of the alternatives – namely, diagonal blocks, compact linear blocks, continuous curved blocks and dispersed cubic blocks – showed that the microclimate performance of the diagonal type was better than those of the other design options, as the PMV index represented the minimum distance from the ideal thermal comfort range due to a lower sky view factor (SVF) and optimal orientation. The curved blocks performed better in urban open spaces, promoting airflow and reducing UHI, than linear and cubic blocks. The results showed that the PMV index could predict thermal comfort in various urban design patterns and could be affected by the morphology of urban blocks. Variations in building morphology alter the SVF and microclimate parameters, which have an impact on outdoor thermal comfort.

Assessing the thermal risks for residents and visitors in traditional Street-facing Dwellings with Eaves Gallery during an extreme heatwave event

Street-facing Dwelling with Eaves Gallery (SDEG) was built for both commercial activities and residential needs. The Eaves Gallery (EG) and courtyards with small sky view factor (SVF) in those dwellings were shaped to prevent the residences or pedestrians from strong sun radiation, heavy rain, or snow. As a climate-adaptive vernacular architecture, it has lasted for hundreds of years in the large areas along the Yangtze River Basin (YRB) of China. Yet, few studies systematically investigate the thermal conditions of these dwellings and validate their climate resilience quantitatively under heat waves. Concerning the extreme weather caused by climate change, this study investigated indoor, semi-outdoor, and outdoor thermal comfort (OTC) of the SDEG in Xiaohe Town of Hubei province, China during the heatwave event in 2022. While mobile and fixed measurements were carried out to capture the data of thermal environments, the skin temperature measurements were implemented to understand the thermal responses of the households. The results found that SDEG was qualified to help indoor households avoid the heat risk with proper thermal behaviors but cannot provide a safe thermal environment for the pedestrians in the semi-outdoor or outdoor space of the EG street throughout the day. This study demonstrated the negative effects of heatwaves on not only the walking visitors but also the livelihood of the residents, which is an appeal to scholars to attention to the heat risks and thermal behaviors of marginalized groups living in various vernacular buildings within the context of high-temperature scenarios under the heatwave conditions.

The Impacts of Urban Morphology on Urban Heat Islands in Housing Areas: The Case of Erzurum, Turkey

The growing importance of climate change underlines the need to comprehend Urban Heat Islands (UHI), particularly those influenced by urban morphology. As progress has been made in understanding the macroscale relationship between urban morphology and UHIs, the microscale effects are often overlooked. This study, conducted in the city of Erzurum in Turkey, delves into the complex relationship between urban morphology and UHI intensity in different housing areas with distinct microclimates, focusing particularly on street networks, building systems, and land use. Pearson correlation analysis was performed to investigate the relationships between morphological indicators and UHIs in different housing areas. Key findings include that (1) noticeable UHI effects were observed, especially in dense areas with high-rise buildings. (2) UHIs reveal a strong correlation with both 2D and 3D urban morphological indicators. A moderate-to-high Sky View Factor (SVF) tends to reduce UHIs, while an extremely high SVF aggravates UHIs. (3) Enhancing street network integration emerges as a more effective strategy for mitigating UHI effects in mid-rise buildings compared to other morphological factors. The Normalised Difference Built-Up Index (NDBI) and Normalised Difference Vegetation Index (NDVI) may not reliably indicate UHIs in housing areas with a predominantly rural character. Consequently, this article recommends that urban morphology optimisation for UHI mitigation should prioritise spatial and indicator specificity in urban design and spatial planning for cities. Future research endeavours should investigate the influence of morphological indicators on UHI dynamics in different seasons, including various remote sensing indicators related to morphological structure, to enrich our understanding of daily UHI fluctuations within urban morphology research.

Mechanisms of non-stationary influence of urban form on the diurnal thermal environment based on machine learning and MGWR analysis

The non-stationary impact of urban form at block scale on the diurnal thermal environment has not been extensively studied. To fill the gap, a comprehensive multi-scale research framework was constructed, integrating machine learning algorithms and multiscale geographically weighted regression (MGWR) analysis. The urban form types were extracted by machine learning algorithms from 2282 blocks of Dalian, a coastal city, and the contribution to the diurnal land surface temperature (LST) was evaluated. Based on MGWR, the non-stationary effects of urban form, human activity and spatial location on the diurnal LST were quantified, which indicated the contributing factors to the diurnal thermal differences among the form types. The result was as follows: 1) Blocks characterized by low vegetation and mid/low-rise buildings had the highest warming contribution for diurnal LST. 2) The impact of sky view factor (SVF) on diurnal temperature amplitude exhibited no significant spatial-temporal heterogeneity. 3) Building density had a prominent effect on diurnal temperature amplitude. 4) High-vegetation and open (SVF > 0.5) mid/mid-high/low-rise (15–50 m) buildings were recommended. This study provides a more precise basis for policymakers to develop climate adaptation strategies throughout day and night, particularly for coastal cities.

Transparent energy-saving windows based on broadband directional thermal emission

Abstract Passive radiative cooling has emerged as a sustainable energy-saving solution, characterized by its energy-free operation and absence of carbon emissions. Conventional radiative coolers are designed with a skyward orientation, allowing for efficient heat dissipation to the cold heat sink. However, this design feature presents challenges when installed on vertical surfaces, as nearby objects obstruct heat release by blocking the cooler’s skyward view. Here, we introduce a directional radiative cooling glass (DRCG) designed to facilitate efficient heat dissipation through angular selective emission. The DRCG is constructed as a multilayer structure incorporating epsilon-near-zero materials, specifically Si3N4 and Al2O3, layered on an indium-tin-oxide thermal reflector. This innovative design restricts thermal emission to specific angular ranges, known as the Berreman mode. Additionally, the transparent layers enable a visible transmittance exceeding 84 %. Theoretical simulations validate the enhanced cooling performance of the DRCG, exhibiting a temperature reduction of over 1.5 °C compared with conventional glass in hot urban environments characterized by a nearby object temperature exceeding 60 °C and a sky view factor of 0.25. Furthermore, outdoor experiments demonstrate that employing the DRCG as a window enhances space-cooling performance by ∼1.5 °C. These findings underscore the potential of transparent energy-saving windows in mitigating the urban heat island effect.

Quantifying the Impact of Street Greening during Full-Leaf Seasons on Emotional Perception: Guidelines for Resident Well-Being

Quantifying the emotional impact of street greening during the full-leaf seasons in spring, summer, and fall is important for well-being-focused urban construction. Current emotional perception models usually focus on the influence of objects identified through semantic segmentation of street view images and lack explanation. Therefore, interpretability models that quantify street greening’s emotional effects are needed. This study aims to measure and explain the influence of street greening on emotions to help urban planners make decisions. This would improve the living environment, foster positive emotions, and help residents recover from negative emotions. In Hangzhou, China, we used the Baidu Map API to obtain street view images when plants were in the full-leaf state. Semantic segmentation was used to separate plant parts from street view images, enabling the calculation of the Green View Index, Plant Level Diversity, Plant Color Richness, and Tree–Sky View Factor. We created a dataset specifically designed for the purpose of emotional perception, including four distinct categories: pleasure, relaxation, boredom, and anxiety. This dataset was generated through a combination of machine learning algorithms and human evaluation. Scores range from 1 to 5, with higher values indicating stronger emotions and lower values indicating less intense ones. The random forest model and Shapley Additive Explanation (SHAP) algorithm were employed to identify the key indicators that affect emotions. Emotions were most affected by the Plant Level Diversity and Green View Index. These indicators and emotions have an intricate non-linear relationship. Specifically, a higher Green View Index (often indicating the presence of 20–35 fully grown trees within a 200 m range in street view images) and a greater Plant Level Diversity significantly promoted positive emotional responses. Our study provided local planning departments with support for well-being-focused urban planning and renewal decisions. Based on our research, we recommend the following actions: (1) increase the amount of visible green in areas with a low Green View Index; (2) plant seasonal and flowering plants like camellia, ginkgo, and goldenrain trees to enhance the diversity and colors; (3) trim plants in areas with low safety perception to improve visibility; (4) introduce evergreen plants like cinnamomum camphor, osmanthus, and pine.

Exploring the Nonlinear Interplay between Urban Morphology and Nighttime Thermal Environment

The nighttime thermal environment affects people's nighttime leisure activities and energy consumption. While increasing studies have examined the interplay between urban morphologies and daytime temperature, a gap exists in understanding the nonlinear relationship in the nighttime thermal environment. To address this, the study employs a data-driven ensemble model to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) thermal environment data for four seasons, aligning it with the scale of urban morphology. Furthermore, an additive interpretation algorithm investigates the impact of nonlinear factors shaping the urban thermal environment during nighttime. The findings reveal that in contrast to daytime temperatures, built environment variables exert a more pronounced effect on nighttime surface temperatures than natural variables. Specifically, plot ratio and building height are the greatest contributors to the warming observed across all seasons. On the other hand, increasing the sky view factor of streets proves to be an effective strategy for mitigating nighttime temperatures. Overall, our study sheds new light on the complex interplay between urbanization and the nighttime thermal environment, assisting planners in understanding how the built environment affects the urban temperature and sustainable development path.