5,350 research outputs found
Leveraging Citation Networks to Visualize Scholarly Influence Over Time
Assessing the influence of a scholar's work is an important task for funding
organizations, academic departments, and researchers. Common methods, such as
measures of citation counts, can ignore much of the nuance and
multidimensionality of scholarly influence. We present an approach for
generating dynamic visualizations of scholars' careers. This approach uses an
animated node-link diagram showing the citation network accumulated around the
researcher over the course of the career in concert with key indicators,
highlighting influence both within and across fields. We developed our design
in collaboration with one funding organization---the Pew Biomedical Scholars
program---but the methods are generalizable to visualizations of scholarly
influence. We applied the design method to the Microsoft Academic Graph, which
includes more than 120 million publications. We validate our abstractions
throughout the process through collaboration with the Pew Biomedical Scholars
program officers and summative evaluations with their scholars
A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes
International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work
User Interfaces and Difference Visualizations for Alternatives
Designers often create multiple iterations to evaluate alternatives. Todays computer-based tools do not support such easy exploration of a design space, despite the fact that such support has been advocated. This dissertation is centered on this.
I begin by investigating the effectiveness of various forms of difference visualizations and support for merging changes within a system targeted at diagrams with node and edge attributes. I evaluated the benefits of the introduced difference visualization techniques in two user studies. I found that the basic side-by-side juxtaposition visualization was not effective and also not well received. For comparing diagrams with matching node positions, participants preferred the side-by-side option with a difference layer. For diagrams with non-matching positions animation was beneficial, but the combination with a difference layer was preferred. Thus, the difference layer technique was useful and a good complement to animation.
I continue by investigating if explicit support for design alternatives better supports exploration and creativity in a generative design system. To investigate the new techniques to better support exploration, I built a new system that supports parallel exploration of alternative designs and generation of new structural combinations. I investigate the usefulness of my prototype in two user studies and interviews. The results and feedback suggest and confirm that supporting design alternatives explicitly enables designers to work more creatively.
Generative models are often represented as DAGs (directed acyclic graphs) in a dataflow programming environment. Existing approaches to compare such DAGs do not generalize to multiple alternatives. Informed by and building on the first part of my dissertation, I introduce a novel user interface that enables visual differencing and editing alternative graphsspecifically more than two alternatives simultaneously, something that has not been presented before. I also explore multi-monitor support to demonstrate that the difference visualization technique scales well to up to 18 alternatives. The novel jamming space feature makes organizing alternatives on a 23 monitor system easier. To investigate the usability of the new difference visualization method I conducted an exploratory interview with three expert designers. The received comments confirmed that it meets their design goals
Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Annual recruitment data of new graduates are manually analyzed by human
resources specialists (HR) in industries, which signifies the need to evaluate
the recruitment strategy of HR specialists. Every year, different applicants
send in job applications to companies. The relationships between applicants'
attributes (e.g., English skill or academic credential) can be used to analyze
the changes in recruitment trends across multiple years' data. However, most
attributes are unnormalized and thus require thorough preprocessing. Such
unnormalized data hinder the effective comparison of the relationship between
applicants in the early stage of data analysis. Thus, a visual exploration
system is highly needed to gain insight from the overview of the relationship
between applicants across multiple years. In this study, we propose the
Polarizing Attributes for Network Analysis of Correlation on Entities
Association (Panacea) visualization system. The proposed system integrates a
time-varying graph model and dynamic graph visualization for heterogeneous
tabular data. Using this system, human resource specialists can interactively
inspect the relationships between two attributes of prospective employees
across multiple years. Further, we demonstrate the usability of Panacea with
representative examples for finding hidden trends in real-world datasets and
then describe HR specialists' feedback obtained throughout Panacea's
development. The proposed Panacea system enables HR specialists to visually
explore the annual recruitment of new graduates
Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing
We discuss Bayesian analysis of multivariate time series with dynamic factor
models that exploit time-adaptive sparsity in model parametrizations via the
latent threshold approach. One central focus is on the transfer responses of
multiple interrelated series to underlying, dynamic latent factor processes.
Structured priors on model hyper-parameters are key to the efficacy of dynamic
latent thresholding, and MCMC-based computation enables model fitting and
analysis. A detailed case study of electroencephalographic (EEG) data from
experimental psychiatry highlights the use of latent threshold extensions of
time-varying vector autoregressive and factor models. This study explores a
class of dynamic transfer response factor models, extending prior Bayesian
modeling of multiple EEG series and highlighting the practical utility of the
latent thresholding concept in multivariate, non-stationary time series
analysis.Comment: 27 pages, 13 figures, link to external web site for supplementary
animated figure
MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering
Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods
Visualisation of Long in Time Dynamic Networks on Large Touch Displays
Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types
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