城市视觉环境如何影响人类反应：分析框架构建与系统综述

Urban Visual Environment and Human Responses: An Analytical Framework and Systematic Review

摘要

【目的】针对城市视觉景观影响人类反应研究中指标体系分散、作用方向与强度不一致、研究结论难以转化为规划依据等问题，系统梳理既有实证研究证据，识别不同视觉景观指标对人类反应的作用规律及差异。【方法】采用系统性文献综述方法，构建“城市视觉景观-人类反应”的实证分析框架，对既有研究中的视觉要素指标及其影响方向进行了系统梳理与整合。【结果】1）现有研究在视觉变量分布上呈现显著集中性，主要聚焦于可见要素与空间结构特征，其中可见植被比例为最核心指标（N=97），显著高于可见建筑、水体与天空比例等其他视觉要素。2）不同视觉要素对人类反应的作用呈现清晰分化格局：可见植被比例表现出高度稳定的正向效应（净效应约 0.73~0.74），而可见建筑比例则持续呈负向效应（约-0.43）。相比之下，可见天空、水体比例及围合度、开敞度等空间形态指标在不同研究情境与分析框架中结果差异明显，尚未形成一致结论，表现出显著的情境依赖性。3）在进一步机制层面，植被效应不仅来源于整体绿量水平，还受到内部结构特征的显著调节。其中，植被结构多样性对情绪恢复呈显著正向效应（r=0.715），植被物种丰富度与环境质量评价亦表现出较强正向效应（r=0.646）。【结论】本研究基于统一分析框架整合既有实证证据，系统揭示城市视觉环境影响人类反应的结构性规律，为城市环境要素的分层优化提供了量化依据，也为后续跨情境、多尺度研究提供了可复用的分析框架与方法基础。

Abstract

[Objective] To clarify how urban visual environments influence human responses, this study reviews empirical evidence on urban visual landscape indicators and their effects. Urban visual environments play an important role in people-oriented urban development and healthy city planning. Street-view imagery, semantic segmentation, and computer vision now support large-scale measurement of human-scale visual exposure. Common indicators include green view index, visible sky ratio, visible building ratio, visible water ratio, enclosure, and openness. However, existing studies use different definitions, data sources, spatial scales, statistical models, and response variables. Their findings often differ in effect direction and strength. This limits comparison across studies and weakens their value for planning practice. To address this problem, this study constructs an “urban visual landscape-human response” analytical framework. It aims to identify the evidence distribution, effect direction, contextual differences, and vegetation-related mechanisms of urban visual landscape effects. [Methods] This study followed the PRISMA framework. We searched the Web of Science Core Collection, Scopus, and CNKI databases for empirical studies published from January 2016 to January 2025. After screening titles, abstracts, and full texts, we included 119 studies. We standardized and coded visual indicators and human response variables. Urban visual landscape indicators were divided into four groups: color and lighting features, landscape elements, spatial form, and landscape imagery. Human responses were divided into six groups: restoration and stress, emotional and psychological states, safety and risk perception, aesthetic and preference evaluation, perceived comfort and environmental quality, and behavioral responses and use of space. To improve comparability, we unified effect directions. A positive effect means that a visual indicator relates to a more favorable human response. A negative effect means that it relates to a less favorable response. We used vote counting based on effect direction to summarize the overall pattern. We also calculated a net direction index for each visual indicator. Stratified analyses by landscape type and evidence quality tested the robustness and contextual boundaries of the findings. In addition, vegetation-related indicators had the largest evidence base. Therefore, we conducted a random-effects meta-analysis to compare visible vegetation ratio, species richness, structural diversity, and color diversity. [Results] Results show that current research on urban visual environments is uneven. Existing studies mainly focus on landscape elements and spatial form. Color and lighting features and landscape imagery receive less attention. Among all indicators, visible vegetation index is the most frequently studied variable. It far exceeds visible sky ratio, visible building ratio, visible water ratio, enclosure, and openness. This pattern reflects the strong influence of street-view imagery and semantic segmentation methods. Different visual indicators show distinct relationships with human responses. visible vegetation index shows a stable positive association across response categories, landscape types, and evidence quality levels. Its net direction index is 0.73−0.74. Visible building ratio shows a relatively stable negative association. Its net direction index is −0.43. These two indicators act as relatively stable object-based visual cues. Vegetation usually conveys naturalness and restorative qualities. Buildings often convey artificiality, density, and visual pressure. Other indicators show stronger context dependence. Visible sky ratio, visible water ratio, enclosure, and openness do not show a single consistent direction. Their effects vary across landscape types and response categories. This indicates that their influence depends on spatial organization, scene type, and the specific human response being studied. The stratified analysis further shows the contextual boundaries of visual landscape effects. visible vegetation index relates positively to human responses in streets, residential communities, parks, urban green spaces, waterfront areas, and blue-green corridors. Visible building ratio generally relates to less favorable responses across contexts. In contrast, visible sky ratio may show positive effects in residential and waterfront settings, but negative or mixed effects in street spaces. Enclosure and openness also show mixed directions. These findings suggest that stable semantic cues and context-dependent spatial cues coexist in urban visual landscape effects. The vegetation-focused meta-analysis shows that vegetation effects should not be simplified as “more greenery is always better.” visible vegetation index represents the quantity of visual green exposure. It shows broad positive effects across several response dimensions, especially perceived comfort and environmental quality, aesthetic and preference evaluation, behavioral responses and use of space, restoration and stress reduction, and emotional and psychological states. Vegetation quality indicators show more specific effects. Structural diversity relates strongly to emotional restoration, with a pooled correlation coefficient of r = 0.715. Species richness relates closely to perceived environmental quality, with a pooled correlation coefficient of r = 0.646. Color diversity relates more closely to restoration experience and aesthetic evaluation. These results show a hierarchical relationship of “quantity exposure-quality experience-response differentiation”. [Conclusion] This study provides a systematic synthesis of empirical evidence on urban visual landscapes and human responses. The findings indicate that urban visual landscape effects are not fully linear or fully consistent. Stability and context dependence coexist. Visible vegetation and visible buildings are relatively stable visual cues. Sky visibility, water visibility, enclosure, and openness are more context-sensitive indicators. Based on these findings, we suggest that planning practice should avoid simple one-directional optimization of visual indicators. Urban greening strategies should not only increase visible vegetation coverage. They should also improve species richness, structural diversity, and color variation. Context-dependent indicators should be adjusted according to specific spatial settings and response targets. Overall, this study offers a reusable analytical framework for urban visual environment research and evidence-based implications for health-oriented urban planning and landscape design.

关键词

城市生态学 / 城市环境 / 视觉景观 / 人类反应 / 系统综述

Key words

urban ecology / urban environment / visual landscape / human response / systematic review

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基金资助

国家重点研发计划“城市格局演变的生态环境效应与调控技术”(2022YFF1301100)

国家自然科学基金国家杰出青年科学基金“城市景观格局与生态过程”(42225104)