Search Results (63)

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. Full article

Green open space in high-density cities has positive significance in terms of improving the quality of the living environment and solving problems such as “urban diseases”. Taking the high-density urban districts of the Macau Peninsula and Monaco as examples, this study divides the planning index of open space green quantity into two dimensions: the blue-green spaces occupancy rate (BGOR) within urban land areas and the blue-green spaces visibility rate (BGVR) of the main streetscape. Using satellite remote-sensing maps, GIS databases, and street-view images, this study evaluates the current green quantity in both regions and compares them to identify best practices. This study aims to assess and enhance the green quantity found in the open spaces of high-density cities, using the Macau Peninsula and Monaco as case studies. The primary research questions are as follows: (1) How can the green quantity in open spaces be effectively measured in high-density urban environments? (2) What planning strategies can be implemented to increase the green quantity and improve the urban living environment in such areas? Therefore, this study proposes planning strategies such as three-dimensional greening, converting grey spaces to green spaces, and implementing policies to encourage public participation in greening efforts. These strategies aim to enhance the green quantity in open spaces, thereby improving the urban living environment in high-density cities like Macau and providing a reference for similar urban areas in the world. Full article

As the predominant component of public space in urban areas, streets serve as a fundamental framework of a city’s spatial form. The renewal and enhancement of urban street space are integral to the broader processes of urban regeneration and the sustainable development of both urban and rural areas. This study focuses on the Mingcheng area of Xi’an, employing semantic segmentation technology to extract data and analyze the spatial characteristics of factors influencing street quality. The results of spatial network accessibility analysis are then superimposed, creating a “quality–accessibility” evaluation matrix to provide a comprehensive assessment of the streets within the study area. The findings indicate the following: (1) The spatial quality of streets in Mingcheng District ranges from 1.89 to 5.61, based on the scores ranked from highest to lowest; the streets are categorized into five quality levels: very high, high, medium, low, and very low. (2) Using a radius of 0.8 km for calculation, streets with a centrality value of 600 or above are classified as having high accessibility, whereas those below this threshold are considered to have low accessibility. (3) By constructing a “quality–accessibility” evaluation matrix, the following distribution is obtained: 21.4% of streets are classified as high-quality and high-accessibility, 27.1% as high-quality but low-accessibility, 35.3% as low-quality but high-accessibility, and 16.3% as both low-quality and low-accessibility. (4) A significant correlation exists between street quality, accessibility, and the classification of streets in Mingcheng District. Grounded in the community renewal strategy of the study area, this study investigates the practical integration of urban public space quality improvements and streetscape big data analytics. The methodology employed systematically evaluates the spatial quality of streets in Mingcheng District, offering foundational data and technical support for urban planning and renewal initiatives while contributing valuable insights to urban renewal scholarship. Full article

Urban commercial streets are a crucial component of urban life, serving as the central hubs of commercial activity and providing vital spaces for both residents and visitors to engage in various activities. Walkability is commonly used as a key indicator of environmental quality, playing a significant role in improving residents’ health, community interaction, and environmental quality of life. Therefore, promoting the development of a high-quality walking environment in commercial districts is crucial for fostering urban economic growth and the creation of livable cities. However, existing studies predominantly focus on the impact of the built environment on walkability at the urban scale, with limited attention given to commercial streets, particularly the influence of their physical features on walking-need perceptions. In this study, we utilized Google Street-View Panorama (GSVP) images of the Tenjin commercial district and applied the Semantic Differential (SD) method to assess four walking-need perceptions of visual walkability perception, including usefulness, comfort, safety, and attractiveness. Additionally, deep-learning-based semantic segmentation was employed to extract and calculate the physical features of the Tenjin commercial district. Correlation and regression analysis were used to investigate the impact of these physical features on the four walking-need perceptions. The results showed that the different walking-need perceptions in the Tenjin commercial district are attractiveness > safety > comfort > usefulness. Furthermore, the results show that there are significant spatial distribution differences in walking-need perceptions in the Tenjin commercial district. Safety perception is more prominent on primary roads, all four walking-need perceptions in the secondary roads at a high level, and the tertiary roads have generally lower scores for all walking-need perceptions. The regression analysis indicates that walkable space and the landmark visibility index have a significant impact on usefulness, street cleanliness emerges as the most influential factor affecting safety, greenness is identified as the primary determinant of comfort, while the landmark visibility index exerts the greatest influence on attractiveness. This study expands the existing perspectives on urban street walkability by focusing on street-level analysis and proposes strategies to enhance the visual walkability perception of commercial streets. These findings aim to better meet pedestrian needs and provide valuable insights for future urban planning efforts. Full article

In the context of historical districts becoming a key to the urban transformation and high-quality development of Chinese cities, this paper investigates the regeneration of historic environments in China, emphasizing the creation of public value through urban morphology. By analyzing five distinct case studies—Chengdu KuanZhai Alley, Shanghai TianZiFang Alley, Guangzhou EnNing Road, Taiyuan ZhongLou Street, and Beijing NanLuoGu Alley—this study explores the relationship between urban form and public value creation from 2000 to 2020. The research posits that the spatial attribute of “public nature” is central to the regeneration process, highlighting the importance of understanding how urban spaces can foster community engagement and social interaction. An evaluation system is constructed to assess the regeneration of historic areas based on spatial “publicness” and people’s perceptions, addressing the need for a more nuanced approach to urban planning. The findings reveal that effective urban regeneration not only preserves historical significance but also enhances the quality of public spaces, thereby contributing to social equity and cultural integrity. This study aims to provide valuable insights for urban planners and policymakers, advocating for a public value-oriented approach to the renewal of historic districts that balances economic development with the preservation of cultural heritage. The integration of public value concepts into heritage management is crucial for creating vibrant urban environments that resonate with community needs and aspirations. Full article

Urban greenways are pivotal in enriching urban quality and fostering socio-ecological sustainability. Previous studies on urban greenway networks have often overlooked user-based experience efficiency, leading to the underutilization and insufficient translation of cultural services into human well-being. In this study, we introduce a user behavior-driven assessment framework for planning multifunctional urban greenways that connect parks with high green space exposure and maximize recreational mobility. Beijing’s built-up urban areas (BBUA) were selected as the case study area. Firstly, we evaluated the green space exposure of 331 parks in BBUA using an integrated “Availability–Accessibility–Adaptability” assessment framework as potential carriers. Then, through spatially explicit workflows and the least-cost path methodology, we leveraged a vast dataset of 70 million public transportation swipe records to optimize the alignment of multifunctional greenways, prioritizing the criterion of maximizing recreational footfalls. The results showed that the potential greenways network spans 1566.36 km in BBUA, encompassing 93.88% of parks and offering six diverse functions. It can serve 34.39–35.92% of bus recreation passengers, with this ratio tending to be higher on weekends. However, we identified obstacle points (non-greenway sections) in the networks based on residents’ view perceptions and panoramic street images, primarily located in densely built-up central areas and along southern trunk roads. By addressing these disconnections, the integrity and connectivity of urban greenway networks in BBUA will be improved. Overall, the framework we present can be used to construct greenway networks that maximize the perceived accessibility for bus-based visitors, with valuable implications for sustainable urban planning and regeneration initiatives. Full article

Urban green spaces play a crucial role in providing social services and enhancing residents’ mental health. It is essential for sustainable urban planning to explore the relationship between urban green spaces and human perceptions, particularly their visual comfort. However, most current research has analyzed green spaces using two-dimensional indicators (remote sensing), which often overlook human visual perceptions. This study combined two-dimensional and three-dimensional methods to evaluate urban green spaces. Additionally, the study employed machine learning to quantify residents’ visual comfort in green-space environments and explored the relationship between green spaces and human visual perceptions. The results indicated that Kitakyushu exhibited a moderate FCV and an extremely low Green View Index (GVI). Yahatanishi-ku was characterized as having the highest visual comfort. Tobata-ku demonstrated the lowest visual comfort. Natural, GVI, openness, enclosure, vegetation diversity, landscape diversity, and NDBI were positively correlated with visual comfort. FCV and ENVI were negatively correlated with visual comfort. Vegetation diversity had the most impact on improving visual comfort. By integrating remote sensing and street-view data, this study introduces a methodology to ensure a more holistic assessment of green spaces. Urban planners could use it to better identify areas with insufficient green space or areas that require improvement in terms of green-space quality. Meanwhile, it could be helpful in providing valuable input for formulating more effective green-space policies and improving overall urban environmental quality. The study provides a scientific foundation for urban planners to improve the planning and construction of healthy and sustainable cities. Full article

William H. Whyte took on the challenge of assessing the amount of public space in a city based on its carrying capacity, pointing out that popular public spaces offer more room for social activities. However, the absence of qualitative characteristics makes this assessment even more challenging to implement. This study aims to find a method to gauge the carrying capacity of urban public spaces by calculating the social space ratio for pedestrian-only streets in Karlstad, Sweden, and quantifying this relationship. The social space ratio represents the proportion of public spaces that foster social interaction throughout their entire area. The method began by selecting the most relevant conceptual framework for social public spaces and then sought theory-based characteristics to assign to seven social activities on Karlstad’s pedestrian-only streets. The authors performed a comprehensive search of the literature utilizing the PRISMA approach, gathering information from credible references, placemaking toolkits, transportation toolkits, and academic sources. This was performed to determine the weighting factors and effective social areas by evaluating these activities in terms of nine categories of the chosen framework: accessibility, traffic, social infrastructure, security, places to meet, senses and experience, architecture and aesthetics, development and maintenance, and control and programming. We devised a method to calculate the carrying capacity and social space ratio of Karlstad’s pedestrian-only streets, resulting in a ratio of 0.38. The research led to the development of eight quality-control tools to analyze the seven social activities in public places. This innovative approach helps researchers and municipal planners evaluate the benefits and drawbacks of these spaces, contributing significantly to Swedish urban planning and enabling future studies to create a social area factor. Full article

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. Full article

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage. Full article

Urban space constitutes a complex system, the quality of which directly impacts the quality of life for residents. In high-density cities, factors such as the green coverage in street spaces, color richness, and accessibility of services are crucial elements affecting daily life. Moreover, the application of advanced technologies, such as deep learning combined with street view image analysis, has certain limitations, especially in the context of high-density urban streets. This study focuses on the street space quality within the urban fabric of the Macau Peninsula, exploring the characteristics of the street space quality within the context of high-density urban environments. By leveraging street view imagery and multi-source urban data, this research employs principal component analysis (PCA) and deep-learning techniques to conduct a comprehensive analysis and evaluation of the key indicators of street space quality. Utilizing semantic segmentation and ArcGIS technology, the study quantifies 16 street space quality indicators. The findings reveal significant variations in service-related indicators such as the DLS, ALS, DCE, and MFD, reflecting the uneven distribution of service facilities. The green coverage index and color richness index, along with other service-related indicators, are notably influenced by tourism and commercial activities. Correlation analysis indicates the presence of land-use conflicts between green spaces and service facilities in high-density urban settings. Principal component analysis uncovers the diversity and complexity of the indicators, with cluster analysis categorizing them into four distinct groups, representing different combinations of spatial quality characteristics. This study innovatively provides a quantitative assessment of street space quality, emphasizing the importance of considering multiple key factors to achieve coordinated urban development and enhance spatial quality. The results offer new perspectives and methodologies for the study of street space quality in high-density urban environments. Full article

Urbanization and industrialization have escalated air pollution into a critical global issue, particularly in urban areas. Urban green infrastructures (GIs), such as parks and street trees, play a vital role in mitigating air pollution through dry deposition, the process by which air pollutants are removed by deposition onto plant surfaces or through plant uptake. However, existing studies on the dry-deposition capacity of urban green spaces are limited in their ability to reflect variations at the tree-species level, hindering comprehensive evaluations and effective management strategies. This study aims to quantitatively assess the dry-deposition capacity of the urban green spaces of Beijing and Shanghai for six major air pollutants in using an improved dry-deposition model and tree-species-specific data. Results showed that Shanghai’s urban green spaces had a monthly average dry-deposition rate of 5.5 × 10⁻⁶ s m⁻¹, slightly higher than Beijing’s rate of 5.3 × 10⁻⁶ s m⁻¹. Significant seasonal variations were observed, with summer showing the highest deposition rates due to favorable meteorological conditions. Broad-leaved species such as Zelkova serrata in Beijing and Photinia serratifolia in Shanghai exhibited superior dry-deposition capacities compared to coniferous species. Temperature significantly influenced dry-deposition rates for gaseous pollutants, while particulate-matter deposition was primarily affected by pollutant concentrations. This study provides critical insights into the air = purification functions of urban green spaces and underscores the importance of species selection and strategic green-space planning in urban air-quality management, informing the development of optimized urban-greening strategies for improved air quality and public health. Full article

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. Full article

Street space plays a crucial role in human activity and social life, forming an essential component of a livable and sustainable built environment. Consequently, its quality has garnered significant attention from researchers, designers, and policymakers who aim to achieve precise assessments of street infrastructure and conditions. This study presents a multi-dimensional framework for evaluating street space, considering factors such as access frequency, environmental quality, and amenity richness. By utilizing city-level path planning data, street view imagery, point of interest data, and social media check-in data, this framework assesses each street and assigns scores across these dimensions. These scores facilitate a human-centered analysis of the disparities in street usage and quality. The aggregation of results by administrative regions supports effective policy formulation and implementation. Application of this framework in Xi’an, China, reveals that only 6.95% of frequently visited streets exhibit high environmental quality and functional richness. This study underscores the potential of leveraging public data for detailed street space assessments to inform urban renewal policies. Full article

Street quality plays a crucial role in promoting urban development. There is still no consensus on how to quantify human street quality perception on a large scale or explore the relationship between street quality and street composition elements. This study investigates a new approach for evaluating and comparing street quality perception and accessibility in Shanghai and Chengdu, two megacities with distinct geographic characteristics, using street-view images, deep learning, and space syntax. The result indicates significant differences in street quality perception between Shanghai and Chengdu. In Chengdu, there is a curvilinear distribution of the highest positive perceptions along the riverfront space and a radioactive spatial distribution of the highest negative perceptions along the ring road and main roads. Shanghai displays a fragmented cross-aggregation and polycentric distribution of the streets with the highest positive and negative perceptions. Thus, it is reasonable to hypothesize that street quality perception closely correlates with the urban planning and construction process of streets. Moreover, we used multiple linear regression to explain the relationship between street quality perception and street elements. The results show that buildings in Shanghai and trees, pavement, and grass in Chengdu were positively associated with positive perceptions. Walls in both Shanghai and Chengdu show a consistent positive correlation with negative perceptions and a consistent negative correlation with other positive perceptions, and are most likely to contribute to the perception of low street quality. Ceilings were positively associated with negative perceptions in Shanghai but are not the major street elements in Chengdu, while the grass is the opposite of the above results. Our research can provide a cost-effective and rapid solution for large-scale, highly detailed urban street quality perception assessments to inform human-scale urban planning. Full article