人视街景图像和机器学习结合的城市街道适老性水平空间效应研究

Spatial Distribution Characteristics and Influencing Factors of Age-friendly Urban Streets Based on Human-centered Streetview Images

城市街道与老年人的日常活动密切相关,探讨街道环境适老性水平的空间分布特征及其关键影响因素,对老年友好型城市公共空间建设有重要指导作用。然而,既有研究难以贴近真实的人本视角、快速、大规模且精准地评估街道适老性水平的地理空间效应。因此,本研究从人行视角采集街景图像,结合语义分割和目标检测技术提取环境要素,利用人机对抗众包评价与残差神经网络50(ResNet50)技术测度街道环境适老性水平,采用莫兰指数(Moran's I)、普通最小二乘回归模型(OLS)、空间滞后模型(SLM)和空间误差模型(SEM)综合分析街道适老性水平的地理空间异质性及其影响因素;最后,选取了老龄化程度明显、街道环境多样的广州天河核心区为例进行实证研究。研究发现:①本研究结合了人视街景图像、机器学习和空间统计学方法,能够快速、有效地开展街道适老性水平评价,揭示其空间效应特征和关键影响因素;②街道适老性水平指标在研究区存在中等偏高程度的空间聚集性,商业型街道和靠近住宅区的街道、滨水街道差异大。水平较高的是商业型街道和靠近低密度住宅区的街道,较低的是靠近高密度住宅区的街道。老年人在滨水街道的活动性和安全感高,但愉悦感低;对靠近住宅区街道的归属感低;③不同街道环境要素对适老性水平的影响存在差异。绿视率、开敞度和围合度对街道适老性水平的影响较强,拥挤度、人行道占比与场景多样性几乎无影响。研究有助于为精细化、具身性的城市街道适老化空间研究与实践提供参考和理论依据。

With the acceleration of population aging,the urban built environment for the elderly faces severe challenges.Urban street environments,one of the most frequently used places by the elderly,require highquality construction,which is vital for realizing an age-friendly society.However,few studies have focused on the spatial effects and influencing factors of urban street environment quality for the elderly from a large-scale and human perspective,resulting in difficult practical applications.Therefore,this study took Tianhe district,Guangzhou as a study area,using machine learning and deep learning technology to evaluate the urban street environment quality for the elderly and analyze its spatial distribution and influencing mechanisms.Based on 14916 human-centric street view images taken by panoramic cameras,semantic segmentation and object detection techniques were used to extract environmental elements.Greenness,openness,crowdedness,enclosure,sidewalk ratio,and scene diversity were obtained finally as explanatory variables in this study.A human-machine adversarial scoring system was constructed for the age-friendly street environment quality assessment.Twenty-two elderly volunteers were invited to rate their sense of walkability,vitality,security,belonging,and pleasure from 1000 randomly selected images.A residual neural network 50(ResNet50)was used to predict the urban street environment quality in the Tianhe district based on street view images and crowd-sourced data.The spatial autocorrelation was measured by global and Local Moran's I.Ordinary Least Square regression model(OLS),Spatial Lag Model(SLM),and Spatial Error Model(SEM)were established to analyze the influence mechanisms.Results show that:(1)Using human-centric street view images,machine learning,and spatial statistics methods,this study conducted a fast,large-scale,and precise age-friendly street environment quality assessment and accounted for spatial heterogeneity to identify its key influencing factors;(2)There was a moderate degree of spatial aggregation of different street environment qualities for the elderly in the Tianhe district.For older people,commercial streets and streets near low-density residential areas were associated with higher levels of walkability,activity,sense of safety,and pleasure.Although waterfront streets had higher levels of activity and security,the level of pleasure was low.Streets near high-density residential areas were found to have lower levels of activity level,sense of safety,and pleasure.The sense of belonging was higher in commercial streets and lower in streets close to residential areas;(3)The effects of environmental factors on different street environment quality indexes for the elderly were different.Greenness,openness,and enclosure were important factors while visual crowdedness,sidewalks,and scene diversity played a weak role.Greenness had a positive effect on activity level and sense of safety,but a negative effect on pleasure and sense of belonging.Openness was positively correlated with walkability,pleasure,and sense of belonging,and negatively correlated with activity levels.Enclosure had negative effects on all indicators,especially the sense of belonging.These results reveal the spatial association,heterogeneity,and influencing mechanisms of the street environment quality for the elderly based on human-centric street view images,machine learning,and deep learning techniques on a large urban scale.It shows a feasible paradigm to analyze the street environment for the elderly,providing practical implications to build resilient streets more conducive to an age-friendly society.It's of great value for policy-making,urban planning,and management.