Mauro Diaz | Searcharxiv

arXiv2026

UrbanGazeVis: A Visualization System for Analyzing Eye-Tracking Data on Urban Safety Perception

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.

cs.HC↗

arXiv2025

STRive: An association rule-based system for the exploration of spatiotemporal categorical data

Effectively analyzing spatiotemporal data plays a central role in understanding real-world phenomena and informing decision-making. Capturing the interaction between spatial and temporal dimensions also helps explain the underlying structure of the data. However, most datasets do not reveal attribute relationships, requiring additional algorithms to extract meaningful patterns. Existing visualization tools often focus either on attribute relationships or spatiotemporal analysis, but rarely support both simultaneously. In this paper, we present STRive (SpatioTemporal Rule Interactive Visual Explorer), a visual analytics system that enables users to uncover and explore spatial and temporal patterns in data. At the core of STRive lies Association Rule Mining (ARM), which we apply to spatiotemporal datasets to generate interpretable and actionable insights. We combine ARM with multiple interactive mechanisms to analyze the extracted relationships. Association rules serve as interpretable guidance mechanisms for visual analytics by highlighting the meaningful aspects of the data that users should investigate. Our methodology includes three key steps: rule generation, rule clustering, and interactive visualization. STRive offers two modes of analysis. The first operates at the rule cluster level and includes four coordinated views, each showing a different facet of a cluster, including its temporal and spatial behavior. The second mode mirrors the first but focuses on individual rules within a selected cluster. We evaluate the effectiveness of STRive through two case studies involving real-world datasets -- fatal vehicle accidents and urban crime. Results demonstrate the system's ability to support the discovery and analysis of interpretable patterns in complex spatiotemporal contexts.

cs.HC↗