Contour Lines Generation in Karstic Plateaus for Topographic Maps

International audienceContour lines are a key features of topographic maps as they make the comprehension of terrain more easy. But they are no longer drawn by cartographers, they are mostly automatically derived from digital terrain models. Despite real progress in this automated derivation, some specific terrain landscapes remain incorrectly depicted with such techniques, and this is the case for karstic plateaus full of sinkholes. This paper proposes a specific automated method to derive better contour lines in plateaus, particularly around sinkholes. The process first detects karstic plateaus with many sinkholes, as well as the individual sinkholes. Then, the DTM is smoothed to better reflect the terrain in the plateau and in its surroundings. As a third step, the contour lines around sinkholes are enhanced to draw legible round features that better reflect the real terrain. The process was implemented in a QGIS plugin and tested on a small area with a karstic plateau in the Jura mountain in France, and the cartographers of IGN, the French national mapping agency assessed the results as a great improvement compared to the generic automated process to derive contour lines

How does your viewing perspective matter for decision-making with flood risk maps?*

The globally increasing frequency of flood events highlights the importance of effective flood risk communication. The influence of the viewing perspective of mapped flood events on human risk perception has not yet been a research focus of the geovisualization community. This empirical study aims to fill this gap by investigating how the viewing perspective of flood risk maps, that is, 2D orthographic vs. 2.5D oblique views, influence human flood risk perception and decision-making. Results on how viewing perspective might influence measured risk perception are in line with prior inconclusive research on the utility and usability of adding a third viewing dimension on static maps. Unlike prior research would have suggested, we find that the individual risk attitude of our participants had no direct influence on their risk ratings in the context of this study. With additional empirical evidence on how static 2D and oblique 2.5D hazard maps might influence the public’s risk perception and decision-making, we hope to further inform policy and decision makers on the critical importance of well-designed cartographic displays for effective and efficient hazard and risk communication. We also provide an open-source code repository for making reproducible experiments with our static maps

Virtual environments as memory training devices in navigational tasks for older adults.

Cognitive training approaches using virtual environments (VEs) might counter age-related visuospatial memory decline and associated difficulties in wayfinding. However, the effects of the visual design of a VE in route learning are not fully understood. Therefore, we created a custom-designed VE optimized for route learning, with adjusted levels of realism and highlighted landmark locations (MixedVE). Herein we tested participants' route recall performance in identifying direction of turn at the intersection with this MixedVE against two baseline alternatives (AbstractVE, RealisticVE). An older vs. a younger group solved the tasks in two stages (immediate vs. delayed recall by one week). Our results demonstrate that the MixedVE facilitates better recall accuracy than the other two VEs for both age groups. Importantly, this pattern persists a week later. Additionally, our older participants were mostly overconfident in their route recall performance, but the MixedVE moderated this potentially detrimental overconfidence. Before the experiment, participants clearly preferred the RealisticVE, whereas after the experiment, most of the younger, and many of the older participants, preferred the MixedVE. Taken together, our findings provide insights into the importance of tailoring visualization design in route learning with VEs. Furthermore, we demonstrate the great potential of the MixedVE and by extension, of similar VEs as memory training devices for route learning, especially for older participants

Virtual environments as memory training devices in navigational tasks for older adults

Cognitive training approaches using virtual environments (VEs) might counter age-related visuospatial memory decline and associated difficulties in wayfinding. However, the effects of the visual design of a VE in route learning are not fully understood. Therefore, we created a custom-designed VE optimized for route learning, with adjusted levels of realism and highlighted landmark locations (MixedVE). Herein we tested participants’ route recall performance in identifying direction of turn at the intersection with this MixedVE against two baseline alternatives (AbstractVE, RealisticVE). An older vs. a younger group solved the tasks in two stages (immediate vs. delayed recall by one week). Our results demonstrate that the MixedVE facilitates better recall accuracy than the other two VEs for both age groups. Importantly, this pattern persists a week later. Additionally, our older participants were mostly overconfident in their route recall performance, but the MixedVE moderated this potentially detrimental overconfidence. Before the experiment, participants clearly preferred the RealisticVE, whereas after the experiment, most of the younger, and many of the older participants, preferred the MixedVE. Taken together, our findings provide insights into the importance of tailoring visualization design in route learning with VEs. Furthermore, we demonstrate the great potential of the MixedVE and by extension, of similar VEs as memory training devices for route learning, especially for older participants

How do decision time and realism affect map-based decision making?

We commonly make decisions based on different kinds of maps, and under varying time constraints. The accuracy of these decisions often can decide even over life and death. In this study, we investigate how varying time constraints and different map types can influence people’s visuo-spatial decision making, specifically for a complex slope detection task involving three spatial dimensions. We find that participants’ response accuracy and response confidence do not decrease linearly, as hypothesized, when given less response time. Assessing collected responses within the signal detection theory framework, we find that different inference error types occur with different map types. Finally, we replicate previous findings suggesting that while people might prefer more realistic looking maps, they do not necessarily perform better with them

The impact of 3D virtual environments with different levels of realism on route learning: a focus on age-based differences

With technological advancements, it has become notably easier to create virtual environments (VEs) depicting the real world with high fidelity and realism. These VEs offer some attractive use cases for navigation studies looking into spatial cognition. However, such photorealistic VEs, while attractive, may complicate the route learning process as they may overwhelm users with the amount of information they contain. Understanding how much and what kind of photorealistic information is relevant to people at which point on their route and while they are learning a route can help define how to design virtual environments that better support spatial learning. Among the users who may be overwhelmed by too much information, older adults represent a special interest group for two key reasons: 1) The number of people over 65 years old is expected to increase to 1.5 billion by 2050 (World Health Organization, 2011); 2) cognitive abilities decline as people age (Park et al., 2002). The ability to independently navigate in the real world is an important aspect of human well-being. This fact has many socio-economic implications, yet age-related cognitive decline creates difficulties for older people in learning their routes in unfamiliar environments, limiting their independence. This thesis takes a user-centered approach to the design of visualizations for assisting all people, and specifically older adults, in learning routes while navigating in a VE. Specifically, the objectives of this thesis are threefold, addressing the basic dimensions of: ❖ Visualization type as expressed by different levels of realism: Evaluate how much and what kind of photorealistic information should be depicted and where it should be represented within a VE in a navigational context. It proposes visualization design guidelines for the design of VEs that assist users in effectively encoding visuospatial information. ❖ Use context as expressed by route recall in short- and long-term: Identify the implications that different information types (visual, spatial, and visuospatial) have over short- and long-term route recall with the use of 3D VE designs varying in levels of realism. ❖ User characteristics as expressed by group differences related to aging, spatial abilities, and memory capacity: Better understand how visuospatial information is encoded and decoded by people in different age groups, and of different spatial and memory abilities, particularly while learning a route in 3D VE designs varying in levels of realism. In this project, the methodology used for investigating the topics outlined above was a set of controlled lab experiments nested within one. Within this experiment, participants’ recall accuracy for various visual, spatial, and visuospatial elements on the route was evaluated using three visualization types that varied in their amount of photorealism. These included an Abstract, a Realistic, and a Mixed VE (see Figure 2), for a number of route recall tasks relevant to navigation. The Mixed VE is termed “mixed” because it includes elements from both the Abstract and the Realistic VEs, balancing the amount of realism in a deliberate manner (elaborated in Section 3.5.2). This feature is developed within this thesis. The tested recall tasks were differentiated based on the type of information being assessed: visual, spatial, and visuospatial (elaborated in Section 3.6.1). These tasks were performed by the participants both immediately after experiencing a drive-through of a route in the three VEs and a week after that; thus, addressing short- and long-term memory, respectively. Participants were counterbalanced for their age, gender, and expertise while their spatial abilities and visuospatial memory capacity were controlled with standardized psychological tests. The results of the experiments highlight the importance of all three investigated dimensions for successful route learning with VEs. More specifically, statistically significant differences in participants’ recall accuracy were observed for: 1) the visualization type, highlighting the value of balancing the amount of photorealistic information presented in VEs while also demonstrating the positive and negative effects of abstraction and realism in VEs on route learning; 2) the recall type, highlighting nuances and peculiarities across the recall of visual, spatial, and visuospatial information in the short- and long-term; and, 3) the user characteristics, as expressed by age differences, but also by spatial abilities and visuospatial memory capacity, highlighting the importance of considering the user type, i.e., for whom the visualization is customized. The original and unique results identified from this work advance the knowledge in GIScience, particularly in geovisualization, from the perspective of the “cognitive design” of visualizations in two distinct ways: (i) understanding the effects that visual realism has—as presented in VEs—on route learning, specifically for people of different age groups and with different spatial abilities and memory capacity, and (ii) proposing empirically validated visualization design guidelines for the use of photorealism in VEs for efficient recall of visuospatial information during route learning, not only for shortterm but also for long-term recall in younger and older adults

Thinking with data visualisations: cognitive processing and spatial inferences when communicating climate change

Data visualisations can be effective for communicating scientific data, but only if they are understood. Such visualisations (i.e. scientific figures) are used within assessment reports produced by the Intergovernmental Panel on Climate Change (IPCC). However, IPCC figures have been criticised for being inaccessible to non-experts. This thesis presents a thematic analysis of interviews with IPCC authors, finding that a requirement to uphold scientific accuracy results in complex figures that are difficult for non-experts to comprehend, and which therefore require expert explanation. Evidence is subsequently presented showing that figures with greater visual complexity are associated with greater perceived comprehension difficulty among non-experts. Comprehension of complex data visualisations may require readers to make spatial inferences. When interpreting a time-series graph of climate data, it was found that non-experts did not always readily identify the long-term trend. Two experiments then show that linguistic information in the form of warnings can support spatial representations for trends in memory by directing visual attention during encoding (measured using eyetracking). This thesis also considers spatial inferences when forming expectations about future data, finding that expectations were sensitive to patterns in past data. Further, features that act on bottom-up perceptual processes were largely ineffective in supporting spatial inferences. Conversely, replacing spatial inferences by explicitly representing information moderated future expectations. However, replacing spatial inferences might not always be desirable in real-world contexts. The evidence indicates that when information is not explicitly represented in a data visualisation, providing top-down knowledge may be more effective in supporting spatial inferences than providing visual cues acting on bottom-up perceptual processes. This thesis further provides evidence-based guidelines drawn from the cognitive and psychological sciences to support climate change researchers in enhancing the ease of comprehension of their data visualisations, and so enable future IPCC outputs to be more accessible

Visualizing and communicating uncertainty for map-based decision-making: The case of uncertainty depiction in debris flow predictions

Any type of data is subject to uncertainty in one way or another. The prediction of natural hazards such as debris flows is no exception to this rule, especially in the face of ongoing climate change. Since maps are a valuable tool to depict scientific results, the visualization of uncertainty has occupied cartographers and visualization experts over the past decades. In this research, a large variety of different uncertainty visualization methods have been developed. However, testing their effectiveness and their impact on the decision-making process has not been on the forefront of research. Therefore, the study at hand aimed at testing two types of uncertainty visualization methods (single-hue and multi-hue colour scheme; within-group variable) as well as two ways of communicating uncertainty in the map legend (numerical and verbal expressions; between-group variable) in debris flow prediction maps. A key aspect investigated in this study are the strategies applied to make decisions based on uncertain information. Additionally, the study makes use of eye tracking technology to infer on cognitive processes. Two research questions investigated the influence of the uncertainty visualization and communication methods on decision outcome, response time and decision-making strategy. The goal of the last research question was to gain insight into the sources of information which guide decision-making with uncertainty. The empirical study showed that decision outcomes slightly varied between the two visualization methods. Additionally, the decision-making process seemed to be more complicated when uncertainty was communicated through verbal expressions, as shown by the significant difference in response time. Lastly, it was found that decisions were strongly guided by heuristics related to the uncertainty information as well as the distance parameter. Furthermore, a boundary effect, already observed in other uncertainty visualization studies, occurred. Most importantly however, the results indicate that the non-expert audience had trouble correctly interpreting the uncertainty information. Consequently, it is argued that map design choices might be of secondary importance as long as profound understanding of the concept of uncertainty is lacking among map readers. The study thus calls for more profound training of the public on the concept of uncertainty, its visualization in maps and ways to incorporate it into spatial decision-making

Représentations cartographiques intermédiaires : comment covisualiser une carte et une orthophotographie pour naviguer entre abstraction et réalisme ?

Two representations of the territory are widely provided simultaneously to the user through interactive tools (such as magnifiers, sliders or swipes): topographic maps and orthoimages. They provide complementary visions of the territory because of abstraction steps used to design maps and the intrisic perceived photorealism power of orthoimages. Aiming at providing efficient covisualizations of these two representations to the user, we advise not to search for an ideal graphic mix, but to produce a cartographic continuum composed of in-between representations mixing topographic data and orthoimagery. Our objective is to provide interactive tools allowing to choose an intermediate step within the continuum by controling the realism and abstraction levels. Our approach is based on three principles: first, the need for local adaptation of vector data symbolisation to preserve their readability, second, the call for graphic transitions to establish a continuity through in-between cartographic representations, and third the required control over realism level in order to ensure a visual consistency of hybrid visualisations. We provide elementary symbolisation methods to be combined in a global design process. The first one aims at interpolating SLD symbolisation parameters such as color, opacity or texturing between two symbolisations. The second one aims at defining a local symbolisation depending on the graphic context of objects to be highlighted. Those symbolisations are combined for each theme and synchronized for all themes. For these design steps, we provide guidelines based on the evaluation of the realism level coming from our user test. Finally we build a prototype software allowing to test our propositions and browse in-between representations from abstraction to realism through an interactive sliderDeux représentations du territoire sont majoritairement proposées pour être covisualisées de multiples façons (loupe, curseurs, vues asservies, etc.) : la carte topographique et l'orthophotographie. Ces deux représentations apportent une vision complémentaire du territoire : la carte topographique est l'archétype même de l'abstraction et l'orthophotographie renvoie une perception réaliste du territoire. Pour permettre à l'utilisateur de covisualiser ces deux types de représentations, nous préconisons de ne pas chercher un mélange graphique idéal mais plutôt de produire un continuum cartographique formé d'un ensemble continu de représentations intermédiaires mixant données topographiques et orthophotographie. Notre objectif est de permettre à l'utilisateur de choisir sa position entre les deux extrémités en contrôlant le degré de réalisme et d'abstraction tout au long du continuum. Notre approche se fonde sur la nécessité d'adaptation locale de la symbolisation des données topographiques pour assurer la lisibilité de chaque représentation intermédiaire, la création de transitions graphiques pour établir une continuité entre ces représentations, et la synchronisation des symbolisations visant à garantir une homogénéité visuelle de ces représentations mixtes. Nous proposons une méthode de conception reposant sur la combinaison de briques de symbolisation élémentaires. Le premier type de brique consiste à interpoler les paramètres de symbolisation de la norme SLD tels que la couleur, la transparence ou la texture (procédurale, naturelle, ou mixée) entre deux symbolisations données. Le second type de brique analyse le contexte graphique des objets à mettre en valeur afin de déterminer localement une symbolisation adaptée et lisible. Ces briques sont combinées pour chaque thème et coordonnées entre les différents thèmes. Nous émettons des préconisations de paramétrage de ces étapes de conception à partir des résultats de notre test utilisateur visant à estimer le degré de réalisme et d'abstraction des symbolisations cartographiques. Enfin, nous mettons en œuvre cette méthode de conception au sein de la plateforme de recherche GeOxygene sous la forme d'un outil permettant de naviguer dans un continuum cartographique entre réalisme et abstractio