Assessing the Variation of Visual Complexity in Multi-Scale Maps with Clutter Measures

workshopInternational audienceMapping applications, where a multi-scale navigation is available, display multi-scale maps, i.e. a set of maps at different scales. Across scales, the map levels (Fig. 1) can present large differences in terms of representation, due to cartographic generalisation. In our research project [2], we assume that adding intermediate representations between existing map levels could be one way to reduce these differences and to enable smooth transitions while zooming. Inspired by the literature (§2.b), we believe that smooth zooming requires regular and small variations of map complexity across scales. In this paper, we present our experiments assessing clutter variation in existing multi-scale maps. This study seeks to identify if clutter variations may predict the perceived variation of visual complexity in multi-scale maps. After presenting some research works connected to our experiments (§2), the following sections address our research issues and study procedure (§3), then results analysis (§4). Lastly, we discuss the relevance of clutter measures for our project and presents our future work (§5)

Progressive Block Graying and Landmarks Enhancing as Intermediate Representations between Buildings and Urban Areas

workshopInternational audienceGeovisualization applications that allow the navigation between maps at different scales while zooming in and out often provide no smooth transition between the individual building level of abstraction and the representation of whole urban areas as polygons. In order to reduce the cognitive load of the user, we seek to add intermediate zoom levels with intermediate and progressive abstractions between buildings and urban areas. This paper proposes a method based on progressive block graying while enhancing building landmarks, to derive these intermediate representations from the individual buildings. Block graying is based on an automatic building classification, and a multiple criteria decision technique to infer inner city blocks. The landmarks identification relies on machine learning and several criteria based on geometry and spatial relations. The method is tested with real cartographic data between the 1:25k (with individual buildings) and the 1:100k scale (with urban areas): transitions with one, two, or three intermediate representations are derived and tested

Scale Stain: Multi-Resolution Feature Enhancement in Pathology Visualization

Digital whole-slide images of pathological tissue samples have recently become feasible for use within routine diagnostic practice. These gigapixel sized images enable pathologists to perform reviews using computer workstations instead of microscopes. Existing workstations visualize scanned images by providing a zoomable image space that reproduces the capabilities of the microscope. This paper presents a novel visualization approach that enables filtering of the scale-space according to color preference. The visualization method reveals diagnostically important patterns that are otherwise not visible. The paper demonstrates how this approach has been implemented into a fully functional prototype that lets the user navigate the visualization parameter space in real time. The prototype was evaluated for two common clinical tasks with eight pathologists in a within-subjects study. The data reveal that task efficiency increased by 15% using the prototype, with maintained accuracy. By analyzing behavioral strategies, it was possible to conclude that efficiency gain was caused by a reduction of the panning needed to perform systematic search of the images. The prototype system was well received by the pathologists who did not detect any risks that would hinder use in clinical routine

Designing Multi-Scale Maps: Lessons Learned from Existing Practices

International audienceMapping applications display multi-scale maps where zooming in and out triggers the display of different maps at different scales. Multi-scale maps strongly augmented the potential uses of maps, compared to the traditional single-scaled paper maps. But the exploration of the multi-scale maps can be cognitively difficult for users because the content of the maps can be very different at different scales. This paper seeks to identify the factors in the design of map content and style that increase or decrease the exploration cognitive load, in order to improve multi-scales map design. We studied sixteen existing examples of multi-scale maps to identify these factors that influence a fluid zooming interaction. Several different analyses were conducted on these sixteen multiscale maps. We first conducted a guided visual exploration of the maps, and a detailed study of the scales of the maps, to identify general trends of good practices (e.g. the WMTS standard that defines zoom levels is widely used) and potential ways of improvement (e.g. a same map is often used at multiple successive zoom levels). Then, we focused on the visual complexity of the multi-scale maps by analyzing how it varies, continuously or not, across scales, using clutter measures, which showed a peak of complexity at zoom level 12 of the WMTS standard. Finally, we studied how buildings and roads are subject to abstraction changes across scales (e.g. at what zoom level individual buildings turn into built-up areas), which can be one of the causes of exploration difficulties. We identified some good practices to reduce the impact of abstraction changes, for instance by mixing different levels of abstraction in the same map.Les applications cartographiques actuelles affichent des cartes multi-échelles, dans lesquelles une interaction de zoom avant ou arrière déclenche l'affichage d'une nouvelle carte à plus grande ou plus petite échelle. Ces cartes multi-échelles permettent des utilisations beaucoup plus vastes et diverses que les traditionnelles cartes topographiques imprimées sur papier. Mais l'exploration interactive de ces cartes peut entrainer une charge cognitive assez lourde car le contenu des cartes peut varier très fortement entre les différentes échelles, et il devient difficile de se repérer. Cet article cherche à identifier les facteurs du design cartographique qui influent sur cette charge cognitive lors d'un changement d'échelle, avec pour objectif à long terme d'améliorer les pratiques de conception de cartes multi-échelles. Nous avons ainsi étudié seize exemples de cartes multi-échelles pour identifier les facteurs permettant d'influer sur la fluidité du zoom. Plusieurs analyses différentes ont été menées sur ces seize cartes. Nous avons d'abord réalisé une analyse visuelle de ces cartes selon divers critères, et une étude détaillée des différentes échelles utilisées, afin d'identifier des tendances (comme l'utilisation massive du standard WMTS), ou des pistes d'amélioration (par exemple, l'utilisation d'une même carte à plusieurs échelles parait sous-optimale). Nous avons ensuite mesuré la variation de complexité visuelle des cartes quand les échelles varient à l'aide de mesures de l'effet de ≪ clutter ≫ ce qui a notamment montré un pic de complexité pour les cartes présentées au niveau de zoom n∘12 du standard WMTS. Enfin, nous avons étudié les changements de niveau d'abstraction spécifiquement sur les thèmes ≪ bâti ≫ et ≪ routes ≫ (par exemple à quelle échelle la représentation des bâtiments individuels est remplacée par une représentation de l'aire urbaine), ce qui a permis de mettre en valeur une cause possible de ces difficultés d'exploration. Des bonnes pratiques ont été identifiées pour une meilleure transition entre les niveaux d'abstraction, notamment en les combinant dans une même carte à une échelle de transition

Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing

International audienceTo enable smooth zooming, we propose a method to continuously generalize buildings from a given start map to a smaller-scale goal map, where there are only built-up area polygons instead of individual building polygons. We name the buildings on the start map original buildings. For an intermediate scale, we aggregate the original buildings that will become too close by adding bridges. We grow (bridged) original buildings based on buffering, and simplify the grown buildings. We take into account the shapes of the buildings both at the previous map and goal map to make sure that the buildings are always growing. The running time of our method is in O(n 3), where n is the number of edges of all the original buildings. The advantages of our method are as follows. First, the buildings grow continuously and, at the same time, are simplified. Second, right angles of buildings are preserved during growing: the merged buildings still look like buildings. Third, the distances between buildings are always larger than a specified threshold. We do a case study to show the performances of our method

Constrained tGAP for generalisation between scales: the case of Dutch topographic data

This article presents the results of integrating large- and medium-scale data into a unified data structure. This structure can be used as a single non-redundant representation for the input data, which can be queried at any arbitrary scale between the source scales. The solution is based on the constrained topological Generalized Area Partition (tGAP), which stores the results of a generalization process applied to the large-scale dataset, and is controlled by the objects of the medium-scale dataset, which act as constraints on the large-scale objects. The result contains the accurate geometry of the large-scale objects enriched with the generalization knowledge of the medium-scale data, stored as references in the constraint tGAP structure. The advantage of this constrained approach over the original tGAP is the higher quality of the aggregated maps. The idea was implemented with real topographic datasets from The Netherlands for the large- (1:1000) and medium-scale (1:10,000) data. The approach is expected to be equally valid for any categorical map and for other scales as well

ScaleTrotter: Illustrative Visual Travels Across Negative Scales

We present ScaleTrotter, a conceptual framework for an interactive, multi-scale visualization of biological mesoscale data and, specifically, genome data. ScaleTrotter allows viewers to smoothly transition from the nucleus of a cell to the atomistic composition of the DNA, while bridging several orders of magnitude in scale. The challenges in creating an interactive visualization of genome data are fundamentally different in several ways from those in other domains like astronomy that require a multi-scale representation as well. First, genome data has intertwined scale levels---the DNA is an extremely long, connected molecule that manifests itself at all scale levels. Second, elements of the DNA do not disappear as one zooms out---instead the scale levels at which they are observed group these elements differently. Third, we have detailed information and thus geometry for the entire dataset and for all scale levels, posing a challenge for interactive visual exploration. Finally, the conceptual scale levels for genome data are close in scale space, requiring us to find ways to visually embed a smaller scale into a coarser one. We address these challenges by creating a new multi-scale visualization concept. We use a scale-dependent camera model that controls the visual embedding of the scales into their respective parents, the rendering of a subset of the scale hierarchy, and the location, size, and scope of the view. In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual representations that are depicted in integrated visuals. We discuss, specifically, how this form of multi-scale visualization follows from the specific characteristics of the genome data and describe its implementation. Finally, we discuss the implications of our work to the general illustrative depiction of multi-scale data

Making Sense of Document Collections with Map-Based Visualizations

As map-based visualizations of documents become more ubiquitous, there is a greater need for them to support intellectual and creative high-level cognitive activities with collections of non-cartographic materials -- documents. This dissertation concerns the conceptualization of map-based visualizations as tools for sensemaking and collection understanding. As such, map-based visualizations would help people use georeferenced documents to develop understanding, gain insight, discover knowledge, and construct meaning. This dissertation explores the role of graphical representations (such as maps, Kohonen maps, pie charts, and other) and interactions with them for developing map-based visualizations capable of facilitating sensemaking activities such as collection understanding. While graphical representations make document collections more perceptually and cognitively accessible, interactions allow users to adapt representations to users’ contextual needs. By interacting with representations of documents or collections and being able to construct representations of their own, people are better able to make sense of information, comprehend complex structures, and integrate new information into their existing mental models. In sum, representations and interactions may reduce cognitive load and consequently expedite the overall time necessary for completion of sensemaking activities, which typically take much time to accomplish. The dissertation proceeds in three phases. The first phase develops a conceptual framework for translating ontological properties of collections to representations and for supporting visual tasks by means of graphical representations. The second phase concerns the cognitive benefits of interaction. It conceptualizes how interactions can help people during complex sensemaking activities. Although the interactions are explained on the example of a prototype built with Google Maps, they are independent iv of Google Maps and can be applicable to various other technologies. The third phase evaluates the utility, analytical capabilities and usability of the additional representations when users interact with a visualization prototype – VIsual COLlection EXplorer. The findings suggest that additional representations can enhance understanding of map-based visualizations of library collections: specifically, they can allow users to see trends, gaps, and patterns in ontological properties of collections

User Interfaces for Personal Knowledge Management with Semantic Technologies

This thesis describes iMapping and QuiKey, two novel user interface concepts for dealing with structured information. iMapping is a visual knowledge mapping technique based on zooming, which combines the advantages of several existing approaches and scales up to very large maps. QuiKey is a text-based tool to interact with graph-structured knowledge bases with very high interaction efficiency. Both tools have been implemented and positively evaluated in user studies