西部人居环境学刊2025，Vol.40Issue(5)：126-134,9.DOI:10.13791/j.cnki.hsfwest.20250403002
街道空间质量与城市活力的匹配关系及影响因素研究
Research on the matching relationship between street space quality and urban vitality and influencing factors:Taking Wuhan City center as an example
摘要
Abstract
In face of the process of rapid urbanization and global pursuit of sustainable development,renewal and quality enhancement of urban street space becomes increasingly critical.Internationally,studies by scholars such as Jane Jacobs and Jan Gehl have long emphasized the role of streets as vital public realms that foster social interaction,economic vitality,and cultural expression.In China,aligned with the national strategic vision for urban regeneration and high-quality development,street space transformation has become an essential component of modern urban governance.However,a noticeable paradox persists across global cities:substantial investments in physical upgrades often fail to translate into sustained urban vitality.This disjunction between spatial supply and user demand reflects deeper structural and contextual imbalances that necessitate systematic analysis and context-sensitive policy responses.Taking the central urban area of Wuhan as a case study,this research constructs a technical framework entitled"quantitative evaluation-dynamic matching-multidimensional decoupling".A comprehensive set of multi-source big data was employed.Baidu Street View imagery was used to quantify street space quality,while Sina Weibo check-in data served to measure urban vitality levels.These datasets were integrated into a geospatial modeling platform to support fine-grained analysis at the street segment level.To extract the multidimensional perceptual indicators from street view images,Deep Learning(DL)was employed with a pre-trained DeepLabV3+model trained on the Cityscapes data set.Perceptual indicators within the view included 5 main dimensions including walkability(sidewalk width,crossing facilities),visual appeal(green view index,)sky openness,spatial comfort(visual enclosure,building interface continuity),traffic inclusiveness(pedestrian-vehicle separation),and interface activities(street wall continuity,ground-floor transparency)were used as indexes of street space quality.Furthermore,urban vitality was measured in terms of the density and distribution of geotagged social media check-ins.Such data proxies represent human activities and street-level behavior to varying extents.To examine the correspondence between spatial supply and behavioral demand,a matching index was developed based on normalized rankings of street space quality and urban vitality.This index enabled the classification of street segments into four distinct matching types:high-quality-high-vitality(H-H),low-quality-high-vitality(L-H),high-quality-low-vitality(H-L),and low-quality-low-vitality(L-L).Furthermore,a multinomial logistic regression model was utilized to identify key factors influencing these matching types from a multidimensional perspective that incorporated built environment,economic development,and social context variables.Specific metrics included building density,intersection density,regional housing price,nighttime light intensity,and density of convenience facilities.The results reveal a significant proportion of mismatch(72.1％)between street space quality and urban vitality in Wuhan's city center,indicating widespread disconnection between spatial supply and user demand.H-H streets(27.9％)were predominantly located in mixed-use central areas such as Jianghan Road Pedestrian Street and Zhongnan Road-Wuluo Road commercial districts,where high functional diversity and accessibility supported synergistic quality-vitality integration.L-H types(22.26％)clustered in historic inner-city neighborhoods like Wuchang Ancient City and old concession areas,as well as urban villages such as Hua'anli,where cultural heritage,social networks,and economic informality compensated for physical deficiencies.H-L streets were frequently found in newly developed waterfront zones and high-green-coverage suburbs,where superior physical conditions were undermined by monofunctional land use,inadequate daily facilities,and poor connectivity.L-L segments typically appeared in peripheral industrial zones and urban-rural fringes characterized by poor accessibility,resource deprivation,and functional isolation.The regression analysis identified several factors that effectively inhibit undesirable matching outcomes.Higher intersection density,average housing price,nighttime light intensity,POI mix,and density of convenience facilities.These factors collectively enhance street connectivity,economic vitality,functional diversity,and service accessibility,thereby promoting better alignment between space quality and usage intensity.Conversely,and somewhat counterintuitively,greater accessibility to public green spaces was found to promote mismatch.This suggests that large,poorly integrated green patches may disrupt street connectivity,reduce commercial visibility,and hinder vibrancy,highlighting the importance of strategic landscape integration rather than mere proximity.This paper contributes to literature through 3 innovative parts:1)introducing street view image segmentation(via SVI and DL)to extract 3D visual indicators to evaluate quality of the street space;2)A ranking-difference matching index to quantify space-vitality relationships,identify mismatches,and break linear"high vitality-high quality"reliance;3)Integrating built environment/economic/social factors,using multinomial logistic regression to reveal mismatch drivers and inform multidimensional indicator coordination in stock renewal.关键词
街道空间质量/城市活力/街景图像/影响因素/深度学习Key words
street space quality/urban vitality/street view images/influencing factors/deep learning分类
土木建筑引用本文复制引用
单卓然,张馨月,袁满,陈玥迪..街道空间质量与城市活力的匹配关系及影响因素研究[J].西部人居环境学刊,2025,40(5):126-134,9.基金项目
国家自然科学基金面上项目(52278062) （52278062）
国家自然科学基金青年科学基金项目(C类)(51708233) （C类）
