Abstract Visualization is routinely used in control rooms to maintain situational awareness of complex sociotechnical systems. While control room design often incorporates human factors engineering to identify the information presented, it is not unusual for control room visual encodings to rely closely on operator experience and historical encodings, which may be contrary to recommended guidelines from the visualization literature. Such approaches may contribute to the development of sub‐optimal operator mental models of the systems they monitor. In this article, we describe a design study in which we assessed the control room context from a visualization perspective and identified a set of 15 visualization guidelines, matching each of these to the domain requirements of the control room. Inspired by these guidelines, we design a visual encoding for alerts occurring on a power system schematic single line diagram, which is commonly used to identify and respond to system alerts arising from faults and conditions that require operator attention in power system control rooms. We report on the results of an experiment conducted with both domain experts and non‐experts. Our results show support for designs that follow our visualization guidelines via faster response times. Accuracy is improved in cases where information is directly encoded by our designs. We also find increased accuracy for domain experts, but expert response times were biased towards more familiar visual encodings in comparison to non‐experts. This emphasizes the importance of established mental maps for control room operators and the need for careful change management in updating control room visualizations.

Abstract Visualization studies that examine age effects on task performance often attribute the performance gap between young adults (YA) and people in late adulthood (PLA) to declines in cognitive abilities, especially reduced visual working memory (VWM) capacity. However, the tasks and visualizations used in prior work typically involve many additional perceptual and cognitive processes, making it difficult to isolate the contribution of VWM decline to age differences in visualization performance. Here, we used a glyph‐based discrimination paradigm in which set size (number of glyphs) was manipulated to vary VWM demand systematically. Young adults (YA), mid‐age adults (MA), and PLA completed a two‐alternative forced‐choice task in which they judged which of two glyph‐based maps presented a higher overall risk level encoded by the glyphs. For each participant, we fitted psychometric functions to obtain accuracy and precision, and analyzed the response time. The results show that set size impaired performance similarly across age groups, and the marked accuracy drop at the largest set size indicated a practical upper limit on meaningful set size for this task. Contrary to expectations based on cognitive aging, PLA did not perform worse overall or show disproportionate costs at higher VWM demands. Instead, the MA group showed higher precision and was more strongly influenced by the distribution of individual glyph values across the visualization, raising the possibility that strategic or effortful processing may partly compensate for capacity limits. These findings indicate that, for this glyph‐based estimation task, age‐related VWM decline alone is unlikely to fully explain age differences in performance. Therefore, our findings offer little evidence for treating reduced VWM demand as the primary design goal for visualizations targeting PLA, in the absence of evidence that age‐related VWM limitations substantially constrain task performance.

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