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Designing music for therapeutic contexts requires navigating complex relationships between musical structure and listeners' sensory responses, yet composers often lack structured representations of these interactions to inform compositional decisions, relying instead on intuition. We present ResonaVis, an interactive visualization system to help composers analyze interaction and audio data from prior sessions with children with Autism Spectrum Disorder (ASD), informing future compositions. ResonaVis integrates audio features and interaction logs to capture how children engage with layered musical compositions, representing this engagement through coordinated visualizations of temporal transitions, layer co-occurrence, rhythmic activity, and spectral characteristics. Rather than prescribing strategies or directly supporting therapy sessions, the system surfaces patterns in past session data, supporting data-informed reflection during iterative composition. We evaluated ResonaVis through a mixed-methods study with eight music students and a follow-up case study with two experienced composers. Results demonstrate good usability (SUS = 72.23), exceeding benchmarks for early prototypes, and indicate that participants were able to identify interaction patterns, reason about layer relationships, and make informed compositional decisions. Despite moderate cognitive demands, participants reported high perceived performance and low frustration, suggesting productive engagement. Participants' confidence in interpreting interaction and acoustic data when composing for individuals with ASD also increased significantly across multiple dimensions—rhythm and dynamics, pitch and timbre, and spectral characteristics (all p < 0.05). Finally, qualitative findings suggest the potential to shift composers from intuition-driven toward more adaptive, data-informed composition. This work contributes a visualization design space for therapeutic music interaction data, an integrated system for compositional reflection, and empirical evidence that visualization tools can support analytical reasoning and confidence in data-informed creative practices.

Exploring associations between paintings and photographs has become a vital component of art history research, as it helps reveal the evolution of visual patterns and drive interdisciplinary discovery. However, establishing these cross-domain associations remains difficult due to the stylistic gap between artistic expression and photographic realism, coupled with the multifaceted nature of visual attributes. Existing automated approaches often lack accuracy and interpretability, while their rigid outputs fail to accommodate the flexible, multi-dimensional exploration required by domain experts. Addressing these limitations poses three primary challenges: (a) the mismatch between unified representations and diverse analytical needs, (b) the difficulty of identifying relevant species from large cross-domain candidates, and (c) the synthesis of large-scale artistic insights from fragmented matches. To address these challenges, we first introduce a cross-domain visual association model that disentangles multifaceted attributes, enabling intent-driven feature steering. Building on this, we develop PaintEcho, a visual analytics system that enables multi-level exploration to facilitate the systematic discovery and interpretive analysis of cross-domain associations. Specifically, it employs a query-centric radial layout for local taxonomic organization and a "mountain-reflection" metaphor for global pattern discovery. We validate the effectiveness and usability of our approach through an algorithmic performance analysis, two use cases, and a task-based user study. Notably, PaintEcho enables experts to identify elusive species and uncover systematic stylistic shifts across historical masterpieces.

We introduce a visualization that interweaves spatial and temporal structures of photo collections into explorable pathways. Existing photo browsing interfaces typically use space and time separately for sorting, filtering, or mapping photos. What remains missing is an integrated visual form that represents the photographic journey itself. This is particularly relevant for the individual photographer's archive, where their unique lifeline is written into the collection, and it is this spatiotemporal trajectory that gives necessary context and meaning to the work. Inspired by the winding nature of rivers, fluid interaction, and un/foldable visualizations, Meandering Photo Lines visualizes adjustable pathways through a photo collection across space, time, and topics. Two complementary views enable visual exploration at different scales: A grid-based arrangement of photo clusters offers a more analytical overview, while meandering spatiotemporal curves facilitate more serendipitous and reflective exploration. Animated transformations connect these two views seamlessly for multiscale navigation from spatiotemporal cluster signatures down to chronological photo series and individual photographs. We demonstrate our approach through two case studies: an archive of approximately 100,000 photographs documenting the Jewish diaspora life worldwide from 1978 to 2021 and a personal smartphone collection of around 20,000 pictures. Qualitative feedback from a think-aloud study indicates that our approach supports both analytical examination and curiosity-driven engagement.

We introduce a temporal arrangement framework for embedding visualizations in sports videos with a focus on swimming. Our work is inspired by strategies used in current TV broadcasts, where visualizations are selectively displayed to provide meaningful and engaging information to audiences. We began with a systematic review of TV broadcast practices, through which we identified recurring temporal combinations of visualizations and competition statuses, which we define as patterns of temporal arrangement for embedded visualizations. To move beyond the constraints of existing broadcast practices, we then conducted a formative study with a general population. Based on this broader perspective, we designed a configuration framework that allows us to formally specify when and for how long, related to swimming context metadata, visualizations appear in a video. We instantiate the framework in a technology probe, SwimChrono, for applications with real-world swimming context videos. Through audience-customized configurations, SwimChrono supports novel arrangements beyond those used in existing professional settings, is adaptable to various swimming contexts, including different lengths and swimming styles, and key events. Furthermore, we conduct user studies and contribute use cases to illustrate how our framework can be well applied for diverse needs.

The escalating frequency and intensity of heat-related climate events, particularly heatwaves, emphasize the pressing need for advanced heat risk management strategies. Current approaches, primarily relying on numerical models, face challenges in spatial-temporal resolution and in capturing the dynamic interplay of environmental, social, and behavioral factors affecting heat risks. This has led to difficulties in translating risk assessments into effective mitigation actions. Recognizing these problems, we introduce a novel approach leveraging the burgeoning capabilities of Large Language Models (LLMs) to extract rich and contextual insights from news reports. We hence propose an LLM-empowered visual analytics system, Savior, that integrates the precise, data-driven insights of numerical models with nuanced news report information. This hybrid approach enables a more comprehensive assessment of heat risks and better identification, assessment, and mitigation of heat-related threats. The system incorporates novel visualization designs, such as ``thermoglyph'' and news glyph, enhancing intuitive understanding and analysis of heat risks. The integration of LLM-based techniques also enables advanced information retrieval and semantic knowledge extraction that can be guided by experts' analytics needs. We conducted an experiment on information extraction, a case study on the 2022 China Heatwave, and an expert survey & interview collaborated with six domain experts, demonstrating the usefulness of our system in providing in-depth and actionable insights for heat risk management.

XR sports viewing enables spectators to follow play from immersive, spatially anchored perspectives while accessing contextual analytics directly within the scene. In such settings, speech offers a practical interaction modality because text entry and menu navigation can interrupt attention during fast-paced gameplay. However, spoken queries are often underspecified: viewers may omit which player, time period, field location, or metric they intend. When systems resolve these ambiguities implicitly, their assumptions remain hidden, making misinterpretations difficult to notice and correct (repair). We investigate how externalizing a system's interpretation of spoken queries can support inspection and correction of such misunderstandings in XR sports viewing. Through a formative study, we identified four recurring ambiguity types (referential, spatial, temporal, and metric) that characterize ambiguous spoken queries in this context. We develop a design space that organizes externalization along three dimensions (ambiguity type, interpretation state, externalization strategy) and instantiate it in an interactive XR soccer viewing system that combines situated visual cues with supporting analytic views. A within-subjects user study (N=16) comparing externalized interpretation against a voice-only baseline reveals that externalization is associated with higher inspectability on most measured dimensions and increased explicit repair language overall. However, repair occurred in only 38% of misaligned externalization trials, and this visibility-action gap varied by ambiguity type, indicating that transparency and correction affordance are orthogonal design axes.

Athletic coaching increasingly relies on video analysis, yet raw footage lacks tools to quantify motion or simulate valid technique corrections. Drawing on formative interviews with eleven cricket experts (coaches, performance analysts, captains, and players), we introduce PoseForge, a visual analytics system that extracts 3D skeletal poses from single-camera sports videos for interactive movement analysis. In a cricket batting case study, PoseForge computes interpretable kinematic metrics such as feet gap and elbow angle, compares them against scientifically derived norms, and uses an AI coach to suggest targeted adjustments, presented visually and through natural-language feedback (e.g., "increase feet gap by 10 cm"). Users can directly modify poses via mouse interaction or natural-language instructions, with inverse kinematics maintaining anatomical plausibility and real-time updates of metrics and comparisons. An evaluation with the same eleven cricket experts found PoseForge effective for diagnosing movement issues and exploring corrective alternatives, highlighting its applicability in low-resource, academy, and grassroots coaching settings, while identifying opportunities for enhanced sport-specific metrics and longitudinal tracking. PoseForge is available as open-source software at https://github.com/DataVisards/PoseForge.