Visualizing Sensor Network Coverage with Location Uncertainty

We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks

A framework to maximise the communicative power of knowledge visualisations

Knowledge visualisation, in the field of information systems, is both a process and a product, informed by the closely aligned fields of information visualisation and knowledg management. Knowledge visualisation has untapped potential within the purview of knowledge communication. Even so, knowledge visualisations are infrequently deployed due to a lack of evidence-based guidance. To improve this situation, we carried out a systematic literature review to derive a number of “lenses” that can be used to reveal the essential perspectives to feed into the visualisation production process.We propose a conceptual framework which incorporates these lenses to guide producers of knowledge visualisations. This framework uses the different lenses to reveal critical perspectives that need to be considered during the design process. We conclude by demonstrating how this framework could be used to produce an effective knowledge visualisation

Adjacent versus coincident representations of geospatial uncertainty: Which promote better decisions?

International audience3D geological models commonly built to manage natural resources are much affected by uncertainty because most of the subsurface is inaccessible to direct observation. Appropriate ways to intuitively visualize uncertainties are therefore critical to draw appropriate decisions. However, empirical assessments of uncertainty visualization for decision making are currently limited to two-dimensional map data, while most geological entities are either surfaces embedded in a 3D space or volumes. This paper first reviews a typical example of decision making under uncertainty, where uncertainty visualization methods can actually make a difference. This issue is illustrated on a real Middle East oil and gas reservoir, looking for the optimal location of a new appraisal well. In a second step, we propose a user study that goes beyond traditional 2D map data, using 2.5D pressure data for the purposes of well design. Our experiments study the quality of adjacent versus coincident representations of spatial uncertainty as compared to the presentation of data without uncertainty; the representations quality is assessed in terms of decision accuracy. Our study was conducted within a group of 123 graduate students specialized in geology

Communicating model uncertainty for natural hazards:A qualitative systematic thematic review

Natural hazard models are vital for all phases of risk assessment and disaster management. However, the high number of uncertainties inherent to these models is highly challenging for crisis communication. The non-communication of these is problematic as interdependencies between them, especially for multi-model approaches and cascading hazards, can result in much larger deep uncertainties. The recent upsurge in research into uncertainty communication makes it important to identify key lessons, areas for future development, and areas for future research. We present a systematic thematic literature review to identify methods for effective communication of model uncertainty. Themes identified include a) the need for clear uncertainty typologies, b) the need for effective engagement with users to identify which uncertainties to focus on, c) managing ensembles, confidence, bias, consensus and dissensus, d) methods for communicating specific uncertainties (e.g., maps, graphs, and time), and e) the lack of evaluation of many approaches currently in use. Finally, we identify lessons and areas for future investigation, and propose a framework to manage the communication of model related uncertainty with decision-makers, by integrating typology components that help identify and prioritise uncertainties. We conclude that scientists must first understand decision-maker needs, and then concentrate efforts on evaluating and communicating the decision-relevant uncertainties. Developing a shared uncertainty management scheme with users facilitates the management of different epistemological perspectives, accommodates the different values that underpin model assumptions and the judgements they prompt, and increases uncertainty tolerance. This is vital, as uncertainties will only increase as our model (and event) complexities increase.</p

Seismic Vulnerability of the Italian Roadway Bridge Stock

This study focuses on the seismic vulnerability evaluation of the Italian roadway bridge stock, within the framework of a Civil Protection sponsored project. A comprehensive database of existing bridges (17,000 bridges with different level of knowledge) was implemented. At the core of the study stands a procedure for automatically carrying out state-of-the-art analytical evaluation of fragility curves for two performance levels – damage and collapse – on an individual bridge basis. A webGIS was developed to handle data and results. The main outputs are maps of bridge seismic risk (from the fragilities and the hazard maps) at the national level and real-time scenario damage-probability maps (from the fragilities and the scenario shake maps). In the latter case the webGIS also performs network analysis to identify routes to be followed by rescue teams. Consistency of the fragility derivation over the entire bridge stock is regarded as a major advantage of the adopted approach

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making