Street Greening Research Articles

Urban Street Greening and Resident Comfort: An Integrated Approach Based on High-Precision Shadow Distribution and Facade Visual Assessment

With the acceleration of global climate change and urbanization, the urban heat island effect has significantly impacted the quality of life of urban residents. Although numerous studies have focused on macro-scale factors such as air temperature, surface albedo, and green space coverage, relatively little attention has been paid to micro-scale factors, such as shading provided by building facades and tree canopy coverage. However, these micro-scale factors play a significant role in enhancing pedestrian thermal comfort. This study focuses on a city community in China, aiming to assess the thermal comfort of urban streets during the summer. Utilizing high-resolution 3D geographic data and street view images extracted from drone data, this study comprehensively considers the mechanisms affecting the urban street thermal environment and the human comfort requirements for shading and greening. By proposing quantitative indicators from multiple scales and dimensions, this study thoroughly quantifies the impact of the surrounding environment, greening, shading effects, buildings, and road design on the thermal comfort of summer streets. The results show that increasing tree canopy coverage by 10 m can significantly reduce the surrounding temperature, and a building layout extending 200 m can regulate temperature. The distribution of shadows at different times significantly affects thermal comfort, while the sky view factor negatively correlates with thermal comfort. Environments with a high green view index enhance visual comfort. This study reveals the specific contributions of different environmental characteristics to street thermal comfort and identifies factors that significantly impact thermal comfort. This provides a scientific basis for urban green space planning and thermal comfort improvement, holding substantial practical significance.

Spatial Sense of Safety for Seniors in Living Streets Based on Street View Image Data

As the global population ages, the friendliness of urban spaces towards seniors becomes increasingly crucial. This research primarily investigates the environmental factors that influence the safety perception of elderly people in living street spaces. Taking Dingzigu Street in Tianjin, China, as an example, by employing deep learning fully convolutional network (FCN-8s) technology and the semantic segmentation method based on computer vision, the objective measurement data of street environmental elements are acquired. Meanwhile, the subjective safety perception evaluation data of elderly people are obtained through SD semantic analysis combined with the Likert scale. Utilizing Pearson correlation analysis and multiple linear regression analysis, the study comprehensively examines the impact of the physical environment characteristics of living street spaces on the spatial safety perception of seniors. The results indicate that, among the objective environmental indicators, ① the street greening rate is positively correlated with the spatial sense of security of seniors; ② there is a negative correlation between sky openness and interface enclosure; and ③ the overall safety perception of seniors regarding street space is significantly influenced by the spatial sense of security, the sense of security during walking behavior, and the security perception in visual recognition. This research not only uncovers the impact mechanism of the street environment on the safety perception of seniors, but also offers valuable references for the age-friendly design of urban spaces.

Encouraging cycling through the improvement of streetscape perception: A bottom-up investigation into the relationship between street greening and bicycling volume

Enhancing neighborhood greenery quality proves to be an effective tactic in encouraging active travel among residents. From a cyclist's perspective, this study quantitatively evaluates street greening quality and its association with cycling behaviors. Utilizing shared bicycle usage data and SegNet-analyzed Baidu Street Views on Xiamen Island, we quantified comprehensive street greening quality. We then employed Spatial Design Network Analysis and a street greening quality rating scale to assess accessibility and aesthetics. Subsequently, models were developed using OLS, spatial autocorrelation, and Moran's I statistical methods, complemented by a spatial heterogeneity analysis of factors influencing shared bicycle riding behaviors through the Geographically Weighted Regression (GWR) model. Our study reveals that in arterial and commercial districts, the appeal and variety of street greening elements are more crucial than their quantity. These quality indicators exhibit a polycentric, continuous spatial distribution, aligning with the overall street layout. Beyond the visual impact of greenery on cycling behavior, factors like richness, openness, and safety of streets, along with diverse facilities, significantly influence cycling route preferences. In contrast, uniform street elements can disrupt or even discourage cycling. This research, by analyzing the relationship between street greening quality and cycling intensity, offers valuable insights for policymakers in prioritizing the transformation of streets into bike-friendly zones. Additionally, we propose a pragmatic, bottom-up approach to developing bicycle-friendly urban environments, indirectly fostering healthier travel habits among urban residents.

Health Perspectives on the Impact of Historic Neighborhood Street Environments on Children’s Activities

As street space is the most accessible outdoor area for kids, safeguarding their rights and interests on the streets is paramount for fostering kid-friendly communities. However, due to the absence of public space and inadequate security, the streets in historic neighborhoods are unsuitable for kids’ activities. To uncover the crucial influence of the street environment on children’s physical activity and Body Mass Index (BMI) health, we conducted a questionnaire survey for children aged 6–12 residing in the Xi’an Ming Dynasty City Wall Historical and Cultural Neighborhood (XMDN) and collected 343 valid responses. The analysis delved into children’s BMI statuses, their willingness to engage in street activities (WAS), the frequency (AF), duration (AD), and types (AT) of these activities, as well as their satisfaction with the street environment (SE). Using binary logit and linear regression models, we specifically examined the relationships between children’s BMI health, WAS, AT, AF, AD, and SE satisfaction. The key findings emphasize children’s inherent desire to be active on the streets. Based on the research findings, we suggest reducing traffic speed and widening sidewalks to make children’s street activities safer, optimizing street greening to improve the comfort of children’s activities, and enriching commercial activities on the street to create a fun atmosphere. These measures will collectively increase the duration and frequency of their outdoor activities and contribute to the overall improvement of children’s health and well-being.

The street space planning and design of artificial intelligence-assisted deep learning neural network in the Internet of Things

This study begins by discussing Internet of Things (IoT) technology and analyzing the classification of street space into four types, along with the Green Looking Ratio (GLR). Following this, the Fully Convolutional Network (FCN)-8s framework is employed to construct a street view image semantic segmentation model based on FCN principles. Subsequently, IoT technology is utilized to analyze the proportion of GLR and satisfaction in the street space within the historical urban area of T City. The findings reveal a significant positive correlation (significance level p < 0.05, R2 = 0.919) between the GLR satisfaction score of street view images and the average GLR of the area. Among the four types of street space—life leisure, historical streets, traffic areas, and landscape-style streets—the dissatisfaction rates with GLR are 35 %, 33 %, 20 %, and 18 %, respectively, correlating with varying GLR satisfaction levels. To enhance street space greening, planting ponds and boxes are proposed for "blind spots” and "dead corners,” thereby completing greenery in these areas. These initiatives aim to improve street greening policies, integrate street function zones, and advance the scientific greening of urban streets. The analysis of GLR and satisfaction in street spaces provides valuable insights for refining urban street space greening efforts.

Advanced Integration of Urban Street Greenery and Pedestrian Flow: A Multidimensional Analysis in Chengdu’s Central Urban District

As urbanization accelerates, urban greenery, particularly street greenery, emerges as a vital strategy for enhancing residents’ quality of life, demanding attention for its alignment with pedestrian flows to foster sustainable urban development and ensure urban dwellers’ wellbeing. The advent of diverse urban data has significantly advanced this area of study. Focusing on Chengdu’s central urban district, this research assesses street greening metrics against pedestrian flow indicators, employing spatial autocorrelation techniques to investigate the interplay between street greenery and pedestrian flow over time and space. Our findings reveal a prevalent negative spatial autocorrelation between street greenery and pedestrian flow within the area, underscored by temporal disparities in greenery demands across various urban functions during weekdays versus weekends. This study innovatively incorporates mobile phone signal-based population heat maps into the mismatch analysis of street greenery for the first time, moving beyond the conventional static approach of space syntax topology in assessing pedestrian flow. By leveraging dynamic pedestrian flow data, it enriches our understanding of the disconnect between street greening plans and pedestrian circulation, highlighting the concept of urban flow and delving into the intricate nexus among time, space, and human activity. Moreover, this study meticulously examines multiple street usage scenarios, reflecting diverse behavior patterns, with the objective of providing nuanced and actionable strategies for urban renewal initiatives aimed at creating more inviting and sustainable urban habitats.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019–2021 fatal collisions in the USA

ObjectiveThe USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas...

Nature-based solutions for fast-growing city regions: A new spatial equilibrium model for complementary urban green space planning

Urban green spaces assume a pivotal role in city-level nature-based solutions for sustainable development. Parks, street greening, and community greens play a significant role in regulating the urban microenvironment, enhancing subjective well-being, and increasing regional attractiveness. Additionally, urban greenbelts have the capacity to control urban sprawl, thereby influencing housing prices and population distribution. However, the exisiting urban models have typically addressed greenbelts and other green spaces in isolation, resulting in fragmented planning strategies. This study aims to establish a novel spatial equilibrium model that accurately assesses the socio-economic effects resulting from the combined impact of various urban green spaces. This model encompasses green infrastructure components and carefully balances their appeal against the consequent rise in housing costs. The proposed model can elucidate the latent synergistic interactions among multiple urban green policies, which enables urban policymakers to access the multifaceted impacts of greening policy combinations. and suggests that combining different urban greening policies can mitigate the negative effects of a single policy type, such as population redistribution. A case study based on empirical data from Shanghai, China, has been employed to identify the additional value associated with diverse urban green spaces. The scenario analysis suggests that combining different urban greening policies can alleviate the negative impacts of urban population relocation. These findings furnish urban planners and policymakers with novel insights to develop the distribution strategy of urban green spaces via judicious policy integration. Consequently, the proposed model furnishes a novel academic standpoint on the role of urban green spaces within the context of nature-based solution for sustainable development of metropolitan conglomerates and nascent urban agglomerations amidst the evolving post-COVID-19 environment.

Neighborhood built environment, obesity, and diabetes: A Utah siblings study

BackgroundThis study utilizes innovative computer vision methods alongside Google Street View images to characterize neighborhood built environments across Utah. MethodsConvolutional Neural Networks were used to create indicators of street greenness, crosswalks, and building type on 1.4 million Google Street View images. The demographic and medical profiles of Utah residents came from the Utah Population Database (UPDB). We implemented hierarchical linear models with individuals nested within zip codes to estimate associations between neighborhood built environment features and individual-level obesity and diabetes, controlling for individual- and zip code-level characteristics (n = 1,899,175 adults living in Utah in 2015). Sibling random effects models were implemented to account for shared family attributes among siblings (n = 972,150) and twins (n = 14,122). ResultsConsistent with prior neighborhood research, the variance partition coefficients (VPC) of our unadjusted models nesting individuals within zip codes were relatively small (0.5%–5.3%), except for HbA1c (VPC = 23%), suggesting a small percentage of the outcome variance is at the zip code-level. However, proportional change in variance (PCV) attributable to zip codes after the inclusion of neighborhood built environment variables and covariates ranged between 11% and 67%, suggesting that these characteristics account for a substantial portion of the zip code-level effects. Non-single-family homes (indicator of mixed land use), sidewalks (indicator of walkability), and green streets (indicator of neighborhood aesthetics) were associated with reduced diabetes and obesity. Zip codes in the third tertile for non-single-family homes were associated with a 15% reduction (PR: 0.85; 95% CI: 0.79, 0.91) in obesity and a 20% reduction (PR: 0.80; 95% CI: 0.70, 0.91) in diabetes. This tertile was also associated with a BMI reduction of −0.68 kg/m2 (95% CI: −0.95, −0.40) ConclusionWe observe associations between neighborhood characteristics and chronic diseases, accounting for biological, social, and cultural factors shared among siblings in this large population-based study.

Efficiency of urban greening systems with maximized latent heat effect in urban heat island and climate change mitigation

Street, wall, and rooftop greening systems are essential for urban heat reduction and carbon neutrality. In this study, we compared the temperature-reducing effect of current and developed technologies that maximize the latent heat of evaporation through such greening systems. A research site with the maximum urban heat island effect was selected by analyzing the vulnerability of Suwon City, Korea. The latent heat of evaporation for each method was determined by conducting actual measurements and verified by performing computational fluid dynamics simulations. Based on the results of statistical techniques, the validated model was highly reliable. When developed technologies were applied, the temperature of the entire city was reduced by approximately 2 °C. Compared with the existing street greening system, the developed technology achieved a temperature reduction effect even at a distance of 5 m. Current wall greening systems only have a temperature reduction effect at 1 m, but that of the developed technology was approximately 1 °C even at a distance of 2 m. The existing rooftop greening system had a temperature reduction effect only at the height of 1.2 m, whereas that of the developed technology was effective even at 6 m, contributing to a reduction in the temperature of the entire city.

Application of an Integrated Model for Analyzing Street Greenery through Image Semantic Segmentation and Accessibility: A Case Study of Nanjing City

Urban street greening, a key component of urban green spaces, significantly impacts residents’ physical and mental well-being, contributing substantially to the overall quality and welfare of urban environments. This paper presents a novel framework that integrates street greenery with accessibility, enabling a detailed evaluation of the daily street-level greenery visible to residents. This pioneering approach introduces a new measurement methodology to quantify the quality of urban street greening, providing robust empirical evidence to support its enhancement. This study delves into Nanjing’s five districts, employing advanced image semantic segmentation based on machine learning techniques to segment and extract green vegetation from Baidu Street View (BSV) images. Leveraging spatial syntax, it analyzes street network data sourced from OpenStreetMap (OSM) to quantify the accessibility values of individual streets. Subsequent overlay analyses uncover areas characterized by high accessibility but inadequate street greening, underscoring the pressing need for street greening enhancements in highly accessible zones, thereby providing valuable decision-making support for urban planners. Key findings revealed that (1) the green view index (GVI) of sampled points within the study area ranged from 15.79% to 38.17%, with notably better street greening conditions observed in the Xuanwu District; (2) the Yuhua District exhibited comparatively lower pedestrian and commuting accessibility than the Xuanwu District; and (3) approximately 139.62 km of roads in the study area demonstrated good accessibility but lacked sufficient greenery visibility, necessitating immediate improvements in their green landscapes. This research utilizes the potential of novel data and methodologies, along with their practical applications in planning and design practices. Notably, this study integrates street greenery visibility with accessibility to explore, from a human-centered perspective, the tangible benefits of green landscapes. These insights highlight the opportunity for local governments to advance urban planning and design by implementing more human-centered green space policies, ultimately promoting societal equity.

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.

Research on urban three-dimensional greening design from the perspective of climate change—a case study of Beilin District, Xi’an, Shaanxi Province, China

Climate change is an important issue for cities today and in the future. At present, China has a large population and complex climate conditions, and cities are also vulnerable to the adverse effects of climate change (Tian, Environ Sustain Dev 6: 153-155 2020). Three-dimensional greening can not only improve the green space system of a city but also have a far-reaching impact on the ecology, image, and economic benefits of a city. Therefore, the study of urban three-dimensional greening is an effective means to deal with climate change strategies. By exploring the influence of traditional greening and three-dimensional greening on Local Climate in Beilin District of Xi’an, Shaanxi Province, the mechanism of three-dimensional greening on urban ecological environment was discussed, and the ecological theory, urban three-dimensional greening theory, and urban local climate zone (LCZ) were referred to. Based on the methods of national climate monitoring, ENVI-met simulation, and field independent measurement, this paper selected a research sample site in the east section of Jianshe Road, Beilin District, Xi’an City, Shaanxi Province, China, and applied ENVI-met software to simulate the thermal stress relationship among building exterior surfaces, plants, and air in the street; quantified the overall ecology of the area; and used measuring instruments. The influence of different types of greening in the base on the site temperature, humidity, CO2 (carbon dioxide) concentration, wind speed, and other climate factors data was, respectively, measured and analyzed. The grid analysis was used to compare the traditional greening and three-dimensional greening, then the numerical differences of each impact factor were sorted out, and the effect of three-dimensional greening on the improvement of urban ecological environment was discussed by analyzing the climate factors with greater impact. The results show that (1) three-dimensional greening plus traditional greening is the most beneficial mode; (2) in the same environment, according to the parameter of 1.5 m from the ground in the model environment, it can be seen that the temperature of the space treated with three-dimensional greening of buildings is reduced by 3.5–3.6 ℃ compared with the control group, the relative humidity is different by 7–8%, the CO2 concentration is reduced by about 5%, and the spatial wind speed is relatively small. (3) When the urban green coverage rate is more than 40%, the improvement of temperature is more obvious, if it reaches 50%, the cool phenomenon in summer can be fundamentally changed. From the perspective of human perception, the PMV index increased by 0.27 on average. This paper discusses and analyzes the three-dimensional greening of urban streets in Beilin District, Xi’an City, Shaanxi Province, China, and studies its influence on urban ecology to different degrees. The conclusions are as follows: Different types of greening have different degrees of influence on urban climate. Meanwhile, the experimental results of this paper show that in cities like Xi’an, Shaanxi Province, China, where summer is hot, adding three-dimensional greening to traditional street greening can significantly improve the environmental microclimate, which is an effective means to cope with climate change, improve the site environment, and stabilize the urban ecosystem.

Assessment of street-level greenness and its association with housing prices in a metropolitan area

Rapid global urbanization has made environmental amenities scarce despite their considerable advantages, ranging from aesthetics to health benefits. Street greenness is a key urban environmental amenity. This study developed a green index as an objective measure of greenness using street view images and assessed its predictive power along with that of other environmental amenities for metropolitan housing prices. Spatial interpolation was used to transform point data into areal data, enabling effective analysis of a dataset covering an entire metropolis. A series of hedonic models revealed that (1) street greenness is significantly and negatively associated with housing prices, (2) a traditional greenness indicator and the green index provide complementary information, indicating that they could be used for different purposes, and (3) environmental amenities, in general, demonstrated significant relationships with housing prices. Our analysis strategy including spatial interpolation can be widely employed for studies using different types of data. The findings demonstrating a complementary relationship between our two greenness indicators provide valuable insights for policymakers and urban planners to improve street-level greenness and green accessibility. Considering the significance of environmental amenities, this study provides practical approaches for executing sustainable and healthy city development.

Urban green spaces and behavioral and cognitive development in children: A health impact assessment of the Barcelona “Eixos Verds” Plan (Green Axis Plan)

BackgroundUrban environments lack natural features, while nature exposure in cities has been associated with health benefits, including children's neurodevelopment. Through extensive street greening, Barcelona's Eixos Verds (Green Axis) Plan enhances safety, environment, and climate resilience. We aimed to assess the Eixos Verds Plan's potential impact on children's behavioral and cognitive development due to the increased green space expected under the Eixos Verds implementation. MethodsWe performed a quantitative health impact assessment for Barcelona children at census-tract level (n = 1068). We assessed the Eixos Verds Plan's impact by comparing baseline green space distribution with the proposed plan, translating it into percentage green area (%GA) and Normalized Difference Vegetation Index (NDVI). By combining these exposure metrics with child-specific risk estimates and population data, we estimated potential improvements in children's behavioral and cognitive development due to full Eixos Verds implementation. ResultsWith the full Eixos Verds implementation, citywide, %GA increased by 6.9% (IQR: 6.4%; range: 0–23.1%) and NDVI by 0.065 (IQR: 0.083; range: 0.000–0.194). Child behavioral and cognitive development outcomes are expected to improve compared to the baseline. Based on NDVI increases, children's Total Difficulties and Hyperactivity/Inattention scores, based on the Strengths and Difficulties Questionnaire (SDQ), are projected to decrease by 5% (95% CI: 0–15%) and 6% (95% CI: 0–17%). Working Memory and Superior Working Memory scores are expected to increase by 4% and 5%, respectively, based on the computerized n-back test, while the Inattentiveness score could be reduced by 1%, based on the computerized attentional test (ANT). InterpretationUrban greening as planning tool can improve behavioral and cognitive development in city children. Methods and results of our study are applicable to many cities worldwide, and similar results for children of real-life urban greening interventions can be expected.

Analysis of spatial variation of street landscape greening and influencing factors: an example from Fuzhou city, China

Urban street greening is an important part of urban green infrastructure, and Green View Index (GVI) is widely used to assess urban street quality and ecosystem service value as an important indicator to quantify the perception of green street landscape from a pedestrian perspective. However, the distribution of street greenery is imbalanced. Therefore, to explore the differences in street greening levels within urban cities, we crawled streetscape data using the Internet to assess the spatial distribution patterns of urban street GVI using deep learning and spatial autocorrelation, and combined 11 surrounding environmental features with multi-source geographic data to further analyze the key factors influencing the spatial variation of block GVI using ordinary least squares, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR) models. The results show that the mean value of GVI in Fuzhou city is low (23.08%), with large differences among neighborhoods and a significant spatial autocorrelation. Among the regression models, MGWR has the best fit with an R2 of 0.702, where the variables of NDVI, house price, accessibility of water bodies and parks, and the proportion of built-up land have a greater impact on GVI, and the factors do not have the same spatial effect size. The results can provide a scientific basis for promoting green visual equity in different blocks.

A Simulation Study of the Impact of Urban Street Greening on the Thermal Comfort in Street Canyons on Hot and Cold Days

The urban heat island effect has become a widely concerning issue worldwide. Many researchers have made great efforts to improve the summer thermal comfort of urban street canyons by optimizing street greening. Relatively less research has focused on how to improve the thermal comfort of street canyons by optimizing street greening during cold days. Many researchers have proposed strategies to improve the summer thermal comfort of street canyons using road greening. This may have a significant negative impact on the winter thermal comfort of street canyons due to the lack of consideration of the impact on hot and cold days simultaneously, especially when the road green space is mainly composed of evergreen tree species. We aimed to explore the impacts of urban street greening on thermal comfort on hot and cold days at the same time. We used Zhutang West Road in Changsha, China, as an example and built six different models to explore the impacts of the street vegetation types, number of street trees, tree heights, crown widths, and Leaf Area Index on the thermal comfort of the street canyon. In addition, we also considered the impact of different building features and wind directions on the thermal comfort of the street canyon. We employed ENVI-met (version 5.5.1) to simulate different urban street greening models. The results show that the model with a high tree canopy density, tall trees, large and dense crowns, and sufficient building shade has good thermal comfort on hot days (the average physiological equivalent temperature (PET) is 31.1 °C for the study period) and bad thermal comfort on cold days (the PET is 13.3 °C) when it is compared with the other models (the average PETs are 36.2 °C, 31.5 °C, 41.5 °C, 36.2 °C, and 35.5 °C, respectively, on hot days and for other models). In addition, the model with a very large number of short hedges has a positive impact on thermal comfort during hot days (the PET is 31.1 °C). The PET value of another comparable model which does not have hedges is 31.5 °C. Even if the model with a small building area has good ventilation, the small building shade area in the model has a more obvious impact and the model has relatively good thermal comfort during cold days (the PET is 14.2 °C) when it is compared to models with bigger building areas (the PET is 13.9 °C). In summer, when the wind is parallel to the direction of the street canyon, the wind speed in the street canyon is high and the model has relatively good thermal comfort (the PET is 35.5 °C) compared with another model which has different wind direction and lower wind speed at the street canyon (the PET is 36.2 °C). In winter, when the wind is perpendicular to the direction of the street canyon, buildings and trees have a strong windproof effect and this is beneficial to the improvement of thermal comfort (the PET is 15.3 °C for this model and 13.9 °C for another comparable model). This research lays a solid foundation and encourages people to think about the impact of building and tree composition and configuration on the thermal comfort of street canyons during hot and cold days simultaneously.

Enhancing Walking Accessibility in Urban Transportation: A Comprehensive Analysis of Influencing Factors and Mechanisms

The rise in “urban diseases” like population density, traffic congestion, and environmental pollution has renewed attention to urban livability. Walkability, a critical measure of pedestrian friendliness, has gained prominence in urban and transportation planning. This research delves into a comprehensive analysis of walking accessibility, examining both subjective and objective aspects. This study aims to identify the influencing factors and explore the underlying mechanisms driving walkability within a specific area. Through a questionnaire survey, residents’ subjective perceptions were gathered concerning various factors such as traffic operations, walking facilities, and the living environment. Structural equation modeling was employed to analyze the collected data, revealing that travel experience significantly impacts perceived accessibility, followed by facility condition, traffic condition, and safety perception. In the objective analysis, various types of POI data served as explanatory variables, dividing the study area into grids using ArcGIS, with the Walk Score® as the dependent variable. Comparisons of OLS, GWR and MGWR demonstrated that MGWR yielded the most accurate fitting results. Mixed land use, shopping, hotels, residential, government, financial, and medical public services exhibited positive correlations with local walkability, while corporate enterprises and street greening showed negative correlations. These findings were attributed to the level of development, regional functions, population distribution, and supporting facility deployment, collectively influencing the walking accessibility of the area. In conclusion, this research presents crucial insights into enhancing walkability, with implications for urban planning and management, thereby enriching residents’ walking travel experience and promoting sustainable transportation practices. Finally, the limitations of the thesis are discussed.

How Does the Living Street Environment in the Old Urban Districts Affect Walking Behavior? A General Multi-Factor Framework

To strike a trade-off between walking behavior and street resource constraint, extensive research tends to focus on how the urban environment affects walking behavior. However, most of the existing impact measurements focus on the cities in low-latitude temperate environments, which may not truly reflect the situation when assessing high-latitude cities. To address this drawback, in this paper, a general multi-factor framework is introduced to quantify the influence of street-level environmental factors on walking behavior. Specifically, a framework is constructed by comprehensively considering the subjective data and the objective data of Harbin, China, which is mainly composed of multivariate measurement indicators, a multi-source data analysis library, and four-dimensional evaluation paradigm. The results indicate that two main measures can promote the current situation of human-oriented living street environment planning, namely, increasing the distribution of green facilities and life service facilities in the old urban districts living street, and paying attention to the diversity of street greening and street landscape. The proposed framework is conducive to improve the planning status of human-centered street environments and guide the construction of pedestrian-friendly life and healthy streets.

Urban Renewal Path, Streetscape Enhancement, take green as a Strategy: a case study in Mong Kok

Rapid urbanization and high-density development will continue to reduce people's access to nature, which has aroused people's concern about the quality of urban green spaces. Urban green spaces have been recognized as a vital element in enhancing the overall livability and aesthetics of cities. As an integral component of urban green space, street greening is a commonly employed strategy within streetscape enhancement, with the aim of enhancing the overall appearance and functionality of urban streets. However, strategies for green spaces as a common enhancement method along street environments should be reevaluated. This paper aims to investigate how open spaces can play a crucial role in enhancing street environments and address the challenges of limited public space in high-density cities like Hong Kong. The study is conducted in Mong Kok, in Hong Kong which serves as a unique example of high-density urban streets. In the context of the urban framework from 1950s, Mong Kok's spatial constraints coexists with its continuous and vibrant street facades, giving rise to its unique Mong Kok culture and becoming a distinctive emblem of Hong Kong’s street character. Based on the present situation and challenges of public open space in Hong Kong and the review of international cases, theories and policies, this paper discusses the methods to strategize the shortage of urban public space while improving its quality. As a result, more opportunities will be provided for leisure and social interaction and ultimately enriching the quality and sustainability of public realm as a whole.