Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencephalography (EEG)-based machine learning (ML) models can be used to this end, their high computational cost hinders embedded real-time applications. Hardware implementations of spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing. This study compares hardware-compatible SNN models with various traditional ML ones, using an open-source multimodal dataset. Our results show that multimodal integration improves accuracy, with SNN performance comparable to the ML one, demonstrating their potential for real-time implementations of cognitive load detection. These findings position event-based processing as a promising solution for low-latency, energy-efficient workload monitoring in adaptive closed-loop embedded devices that dynamically regulate cognitive load.

Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.

With the development of modern geovisual analytics tools, several researchers have emphasized the importance of understanding users' cognitive, perceptual, and affective tendencies for supporting spatial decisions with geographic information displays (GIDs). However, most recent technological developments have focused on support for navigation in terms of efficiency and effectiveness while neglecting the importance of spatial learning. In the present paper, we will envision the future of GIDs that also support spatial learning in the context of large-scale navigation. Specifically, we will illustrate the manner in which GIDs have been (in the past) and might be (in the future) designed to be context-responsive, personalized, and supportive for active spatial learning from three different perspectives (i.e., GIScience, cartography, and cognitive science). We will also explain why this approach is essential for preventing the technological infantilizing of society (i.e., the reduction of our capacity to make decisions without technological assistance). Although these issues are common to nearly all emerging digital technologies, we argue that these issues become especially relevant in consideration of a person’s current and future locations.

Gaze-Contingent Displays (GCDs) can improve visual search performance on large displays. GCDs, a Level Of Detail (LOD) management technique, discards redundant peripheral detail using various human visual perception models. Models of depth and contrast perception (e.g., depth-of-field and foveation) have often been studied to address the trade-off between the computational and perceptual benefits of GCDs. However, color perception models and combinations of multiple models have not received as much attention. In this paper, we present GeoGCD which uses individual contrast, color, and depth-perception models, and their combination to render scenes without perceptible latency. As proof-of-concept, we present a three-stage user evaluation built upon geographic image interpretation tasks. GeoGCD does not impair users' visual search performance or affect their display preferences. On the contrary, in some cases, it can significantly improve users' performance.

This study empirically investigates the potential of auditory displays for spatial data exploration, as an additional means to broaden the accessibility and dissemination of geographic information for a diverse body of users. In a mixed factorial experiment, three parameter mapping sonification methods are empirically evaluated to interactively explore discrete and continuous digital elevation models by auditory means. Contrasting prior sonification research, this study’s unique empirical evidence suggests that participants can indeed successfully interpret sonified displays containing continuous spatial data. Specifically, the auditory variable pitch leads to significantly better response accuracy, compared to the sound variable duration. Background and training has a weak effect on data interpretation performance with the auditory display. The more immersive the experienced soundscape, the better participants can interpret the sonified terrain. These encouraging empirical results indeed suggest that interactive auditory displays might offer additional means to disseminate spatial information, and to increase the accessibility to spatial data, beyond the currently dominant visual paradigm.

The selection and retrieval of relevant information from the information universe on the web is becoming increasingly important in addressing information overload. It has also been recognized that geography is an important criterion of relevance, leading to the research area of geographic information retrieval. As users increasingly retrieve information in mobile situations, relevance is often related to geographic features in the real world as well as their representation in web documents. We present 2 methods for assessing geographic relevance (GR) of geographic entities in a mobile use context that include the 5 criteria topicality, spatiotemporal proximity, directionality, cluster, and colocation. To determine the effectiveness and validity of these methods, we evaluate them through a user study conducted on the Amazon Mechanical Turk crowdsourcing platform. An analysis of relevance ranks for geographic entities in 3 scenarios produced by two GR methods, 2 baseline methods, and human judgments collected in the experiment reveal that one of the GR methods produces similar ranks as human assessors.

This issue of IJGIS showcases research activities related to how map displays can support users in visuospatial decision making for solving complex spatiotemporal problems. It represents a selectio...

It has been over 50 years since one of the first Geographic Information System (GIS) publications introduced the use of computers for spatial data management, analysis, and mapping (Tomlinson 1962)...

Depicting change captured by dynamic graphs and temporal paths, or trails, is hard. We present two techniques for simplified visualization of such data sets using edge bundles. The first technique uses an efficient image-based bundling method to create smoothly changing bundles from streaming graphs. The second technique adds edge-correspondence data atop of any static bundling algorithm, and is best suited for graph sequences. We show how these techniques can produce simplified visualizations of streaming and sequence graphs. Next, we show how several temporal attributes can be added atop of our dynamic graphs. We illustrate our techniques with data sets from aircraft monitoring, software engineering, and eye-tracking of static and dynamic scenes.

Visual analytics aims to combine the strengths of human and electronic data processing. Visualisation, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Seamless and sophisticated synergies are required for analysing spatio-temporal data and solving spatio-temporal problems. In modern society, spatio-temporal analysis is not solely the business of professional analysts. Many citizens need or would be interested in undertaking analysis of information in time and space. Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.

Visual analytics is often based on the intuition that highly interactive and dynamic depictions of complex and multivariate databases amplify human capabilities for inference and decision-making, as they facilitate cognitive tasks such as pattern recognition, association, and analytical reasoning (Thomas and Cook 2005 Thomas, J.J. and Cook, K.A. 2005. Illuminating the path: the research and development agenda for visual analytics, Los Alamitos, CA: IEE Computer Society Press. National Visualization and Analytics Ctr [Google Scholar]). But how do we know whether visual analytics really works? This article offers a generic evaluation approach combining theory- and data-driven methods based on sequence similarity analysis. The approach systematically studies users' visual interaction strategies when using highly interactive interfaces. We specifically ask whether the efficiency (i.e., speed) of users can be characterized by specific display interaction event sequences, and whether studying user strategies could be employed to improve the (interaction) design of the dynamic displays. We showcase our approach using a very large, fine-grained spatiotemporal dataset of eye movement recordings collected during a controlled human subject experiment with dynamic visual analytics displays. With this methodological approach based on empirical evidence, we hope to contribute to a deeper understanding of how people make inferences and decisions with highly interactive visualization tools and complex displays.

Abstract The landscape metaphor was one of the first methods used by the information visualization community to reorganize and depict document archives that are not inherently spatial. The motivation for the use of the landscape metaphor is that everyone intuitively understands landscapes. We critically examine the information visualization designer's ontologies for implementing spatialized landscapes with ontologies of the geographic domain held by lay people. In the second half of the article, we report on a qualitative study where we empirically assessed whether the landscape metaphor has explanatory power for users trying to make sense of spatialized views, and if so, in what ways. Specifically, we are interested in uncovering how lay people interpret hills and valleys in an information landscape, and whether their interpretation is congruent with the current scientific understanding of geomorphologic processes. Our empirical results suggest that neither developers' nor lay users' understanding of terrain visualizations is based on universal understanding of the true process that has shaped a natural landscape into hills and valleys, mountains, and canyons. Our findings also suggest that the information landscape metaphor for sense making of a document collection is not self‐evident to lay users, as claimed by information landscape designers. While a deep understanding of geomorphology will probably not be required to successfully use an information landscape, we do suggest that a coherent theory on how people use space will be necessary to produce cognitively useful information visualizations.

We investigate the relationship of perceived distances to judged similarities between document points in various types of spatialized displays. Our findings suggest that the distance–similarity relationship is not as self‐evident to viewers as is commonly assumed in the information visualization literature. We further investigate how participants interpret instructions to judge distances when those instructions do or do not specify the type of distance. We find that in all types of spatialization displays, there is no significant difference between default and direct judgements of distance; people clearly interpret default distance instructions to refer to direct (straight‐line) distance. These findings provide direct evidence on the conditions under which people employ distance when assessing similarity between data objects in various types of spatialized views and, when they do, which type of distance. They also give insight into how people explore the similarity of geographic features depicted in cartographic maps or GIS displays.

Geovisualization (GeoViz) is an intrinsically complex process. The analyst needs to look at data from various perspectives and at various scales, from "seeing the whole" to "attending to particulars " (Andrienko and Andrienko 2006). The analyst is also supposed to "see in relation", i.e. make numerous comparisons. This inherent complexity is multiplied by the complexity of the data that is explored and analyzed. The complex, multivariate data structure and heterogeneous components of most contemporary datasets necessitate a combined use of multiple techniques and approaches. There is no single visualization method capable to show "the whole". The analyst has to decompose this whole into views, examine these views and then try to synthesize the whole picture from the partial views. Also, because of large data volumes, we must use methods capable of simultaneously providing an overall view and exposing various "particulars". Looking for "particulars" requires therefore different techniques than "seeing the whole". Some existing visualization tools such as GeoVista and CommonGIS have successfully demonstrated the advantage of multiple-linked views and the use of information visualization (InfoViz) methods such as Parallel Coordinates and Heat maps to explore spatial multivariate data. GeoViz tools support interactive visual representation and analysis of spatio-temporal data, enabling analysts to explore geospatial and multivariate data from multiple perspectives. GeoViz is differentiated from GIS because it focuses on exploratory visual analysis rather than the pre-defined mapping. GeoViz research focuses particular attention on integrating cartographic approaches with interactive visual representations from information visualization, analytical data dissemination and visual analytics.