Recent work suggests that shape can encode quantitative data via a mapping between value and spatial frequency (SF). However, the set-size effect when perceiving multiple SF based items remains unclear. While automatic feature extraction has been found to be less affected by set size (number of items in a group), higher-level processes for making perceptual decisions tend to require increased cognitive demand. To investigate the set-size effect on comparing integrated SF based items, we used a risk-based scenario to assess discrimination performance. Participants were asked to discriminate between pairs of maps containing multiple SF glyphs, in which each glyph represents one of four discrete levels (none, low, medium, high), forming an aggregate "risk strength" per map. The set size was also adjusted across conditions, ranging from small (3 items) to large (7 items). Discrimination sensitivity is modeled with a logistic function and response time with a mixed-effect linear model. Results show that smaller set sizes and lower overall strength enable more precise discrimination, with faster response times for larger differences between maps. Incorporating set size and overall strength into the logistic model, we found that these variables both independently and jointly influence discrimination sensitivity. We suggest these results point towards capacity-limited processes rather than purely automatic ensemble coding. Our findings highlight the importance of set size and overall signal strength when presenting multiple SF glyphs in data visualization.

With the acceleration of global ageing and the widespread adoption of electronic devices, modern visualization design must address the varying perceptual abilities across different age groups. Spatial frequency (SF) is commonly used as a stimulus dimension to assess perceptual ability in vision science, but its potential role as a guiding variable in visualization design, particularly for addressing age-related differences across the lifespan, remains largely unexplored. Many vision science studies have examined age-related spatial frequency perception, but with varying results. This review systematically analyses differences in experimental design and the reasons behind selecting experimental methods, which may be key to these differences. This study developed search terms and systematically searched the ACM, IEEE, PubMed, APA PsycINFO, SAGE, ScienceDirect, and Springer Nature databases. After rigorous screening, 33 studies were included and evaluated, with their similarities and differences compared across five dimensions. Cluster analysis further categorizes the literature into five groups, confirming that discrepancies in experimental outcomes largely stem from variations in psychological methods and participant characteristics. By considering the underlying reasons for these choices, we propose guidelines for designing perceptual experiments in which spatial frequency and age are manipulated factors. These recommendations provide theoretical support and practical insights for visualization design research that considers age-related perception of spatial frequency.

An important aspect of decision-making with visual representations of urban data is ensuring that uncertainty is captured and communicated. However, uncertainty and its causes are often overlooked. In this article, we present a space–time clustering algorithm and data visualization for traffic congestion that incorporate a source of model uncertainty related to identified clusters of congestion. We compare our algorithm using both the Euclidean distance and the street network distance and show that the network-based distance measures produce clusters with less uncertainty. In addition, network-based clusters are more likely to last longer and to precede an increased prevalence of nearby congestion. We consider whether vehicle orientations might usefully filter the data prior to clustering but, in contrast to some other studies, we show that within a dense urban street network environment such as London, a wide range of vehicle orientations contribute to congestion and that these cannot so easily be filtered. Our approach is inspired by traffic control room decision-making and we discuss steps towards the development of a near real-time system that could be used in support of operational decision-making. • Clustering and visualization of traffic congestion incorporating cluster uncertainty • Bivariate glyphs are used to visualize cluster duration and cluster uncertainty • Street network distances are preferred over all evaluation metrics considered • Analysing vehicle orientation shows little basis for separating clusters by direction

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor. However, existing visualization methods are not always effective and efficient in representing bivariate graph-based data. This study proposes a novel node-link visual model — visual entropy (Vizent) graph — to effectively represent both primary and secondary values, such as uncertainty, on the edges simultaneously. We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static node-link diagrams. In the first experiment, we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy. Three static visual encodings that use two visual cues were selected from the literature for comparison: Width-Lightness, Saturation-Transparency, and Numerical values. We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks. The participants achieved higher accuracy of their responses using Vizent and Numerical values; however, both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks. Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy. The performance of the Vizent graph was then compared to the Numerical values visualization. The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented, while no significant difference in accuracy was found. The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.

Background—Photo-realistic terapixel visualization is computationally intensive and to date there have been no such visualizations of urban digital twins, the few terapixel visualizations that exist have looked towards space rather than earth. Objective—Our aims are: creating ascalablecloud supercomputer software architecture for visualization; aphoto-realistic terapixel 3D visualizationof urban IoT data supporting daily updates; arigorous evaluationof cloud supercomputing for our application. Method—We migrated the Blender Cycles path tracer to the public cloud within a new software framework designed to scale to petaFLOP performance. Results—We demonstrate that we can compute a terapixel visualization in under one hour, the system scaling at 98 percent efficiency to use 1024 public cloud GPU nodes delivering 14 petaFLOPS. The resulting terapixel image supports interactive browsing of the city and its data at a wide range of sensing scales. Conclusion—The GPU compute resource available in the cloud is greater than anything available on our national supercomputers providing access to the globally competitive resources. The direct financial cost of access, compared to procuring and running these systems, was low. The indirect cost, in overcoming teething issues with cloud software development, should reduce significantly over time.

Multiview auto-stereoscopic displays support both stereopsis and head motion parallax depth cues and could be superior for certain tasks. Previous work suggests that a high viewpoint density (100 views/10cm at the eye) is required to convincingly support motion parallax. However, it remains unclear how viewpoint density affects task performance, and this factor is critical in determining display and system design requirements. Therefore, we present a simulated multiview display apparatus to undertake experiments using a path-searching task in which we control two independent variables: the stereoscopic depth and the viewpoint density. In the first experiment, we varied both cues and found that even small amounts of stereo depth (2cm) reliably improved task accuracy and reduced latency, whereas there was no evidence of dependence on viewpoint density. In the second experiment, we switched off the stereoscopic cue and varied viewpoint density alone. We found that for these monoscopic images increasing viewpoint density resulted in some reduction in response latency (up to eight views/10cm) but had no effect on accuracy. We conclude for cases where occlusion is not an overriding factor that low viewpoint densities may be sufficient to enable effective path-searching task performance.