UrbanGazeVis：用于分析城市安全感知眼动数据的可视化系统
UrbanGazeVis: A Visualization System for Analyzing Eye-Tracking Data on Urban Safety Perception
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中文总结 AI 辅助
本研究开发UrbanGazeVis可视化系统，通过30名参与者使用HoloLens 2分析150张里约街景的眼动数据，揭示注视与城市安全感知的关联，为城市设计提供见解。
中文摘要 AI 辅助
街景的感知安全度取决于人们的注视位置，但注视行为与城市混乱视觉线索之间的关联仍未被充分理解。现有研究将安全视为图像级标签，几乎无法揭示对特定元素（如建筑、绿植、人群、衰败迹象）的关注如何影响安全判断。我们开展了一项头戴式眼动仪研究，30名参与者使用HoloLens 2头显观看并评估里约热内卢150张街景图像的安全度，将注视轨迹映射到语义分割区域和混乱线索（如破损墙壁、涂鸦、架空线缆），得到关联注视动态、场景语义与安全评分的多模态数据集。为分析该数据集，我们提出UrbanGazeVis，一款交互式视觉分析系统，包含以图像和参与者为中心的视图，将注视的空间、时间、语义维度与感知安全度关联，支持安全与不安全场景的对比、相似图像不同评分的检查，以及基于字形摘要的感兴趣区域分析。统计模型显示，对物理混乱的持续关注与较低的感知安全度相关，而视觉分析揭示了常被全局聚合掩盖的特定情境效应。这些分析为城市设计与规划提供了可操作的见解。
英文摘要
Perceived safety in streetscapes depends on where people look, yet how gaze relates to visual cues of urban disorder remains poorly understood. Prior work treats safety as an image-level label, offering little insight into how attention to specific elements (e.g, buildings, greenery, people, signs of decay) shapes these judgments. We present a head-mounted eye-tracking study in which 30 participants viewed and rated the safety of 150 street-view images from Rio de Janeiro using a HoloLens 2 headset. Gaze traces were mapped onto semantic segments and disorder cues (e.g., damaged walls, graffiti, overhead cables), yielding a multimodal dataset linking gaze dynamics, scene semantics, and safety scores. To analyze it, we introduce UrbanGazeVis, an interactive visual analytics system with image- and participant-centric views that connects the spatial, temporal, and semantic dimensions of gaze to perceived safety, supporting comparisons between safe and unsafe scenes, inspection of divergent ratings for similar images, and region-of-interest analysis via glyph-based summaries. Statistical models show that sustained attention to physical disorder is associated with lower perceived safety, while the visual analysis reveals context-specific effects often masked by global aggregation. Together, these analyses offer actionable insights for urban design and planning.
