128 research outputs found
Natural charge spatial separation and quantum confinement of ZnO/GaN core/shell nanowires
We performed density-functional calculations to investigate the electronic
structure of ZnO/GaN core/shell heterostructured nanowires (NWs) orientating
along direction. The build-in electric filed arising from the charge
redistribution at the {1-100} interfaces and the band offsets were revealed.
ZnO-core/GaN-shell NWs rather than GaN-core/ZnO-shell ones were predicted to
exhibit natural charge spatial separation behaviors, which are understandable
in terms of an effective mass model. The effects of quantum confinement on the
band gaps and band offsets were also discussed.Comment: 3 pages, 3 figure
Exploring the Design Space of Immersive Urban Analytics
Recent years have witnessed the rapid development and wide adoption of
immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft
HoloLens. These immersive devices have the potential to significantly extend
the methodology of urban visual analytics by providing critical 3D context
information and creating a sense of presence. In this paper, we propose an
theoretical model to characterize the visualizations in immersive urban
analytics. Further more, based on our comprehensive and concise model, we
contribute a typology of combination methods of 2D and 3D visualizations that
distinguish between linked views, embedded views, and mixed views. We also
propose a supporting guideline to assist users in selecting a proper view under
certain circumstances by considering visual geometry and spatial distribution
of the 2D and 3D visualizations. Finally, based on existing works, possible
future research opportunities are explored and discussed.Comment: 23 pages,11 figure
KB4VA: A Knowledge Base of Visualization Designs for Visual Analytics
Visual analytics (VA) systems have been widely used to facilitate
decision-making and analytical reasoning in various application domains. VA
involves visual designs, interaction designs, and data mining, which is a
systematic and complex paradigm. In this work, we focus on the design of
effective visualizations for complex data and analytical tasks, which is a
critical step in designing a VA system. This step is challenging because it
requires extensive knowledge about domain problems and visualization to design
effective encodings. Existing visualization designs published in top venues are
valuable resources to inspire designs for problems with similar data structures
and tasks. However, those designs are hard to understand, parse, and retrieve
due to the lack of specifications. To address this problem, we build KB4VA, a
knowledge base of visualization designs in VA systems with comprehensive labels
about their analytical tasks and visual encodings. Our labeling scheme is
inspired by a workshop study with 12 VA researchers to learn user requirements
in understanding and retrieving professional visualization designs in VA
systems. The theme extends Vega-Lite specifications for describing advanced and
composited visualization designs in a declarative manner, thus facilitating
human understanding and automatic indexing. To demonstrate the usefulness of
our knowledge base, we present a user study about design inspirations for VA
tasks. In summary, our work opens new perspectives for enhancing the
accessibility and reusability of professional visualization designs
NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial Data
Understanding how local environments influence individual behaviors, such as
voting patterns or suicidal tendencies, is crucial in social science to reveal
and reduce spatial disparities and promote social well-being. With the
increasing availability of large-scale individual-level census data, new
analytical opportunities arise for social scientists to explore human behaviors
(e.g., political engagement) among social groups at a fine-grained level.
However, traditional statistical methods mostly focus on global, aggregated
spatial correlations, which are limited to understanding and comparing the
impact of local environments (e.g., neighborhoods) on human behaviors among
social groups. In this study, we introduce a new analytical framework for
analyzing multi-variate neighborhood effects between social groups. We then
propose NeighVi, an interactive visual analytics system that helps social
scientists explore, understand, and verify the influence of neighborhood
effects on human behaviors. Finally, we use a case study to illustrate the
effectiveness and usability of our system.Comment: Symposium on Visualization in Data Science (VDS) at IEEE VIS 202
Reviving Static Charts into Live Charts
Data charts are prevalent across various fields due to their efficacy in
conveying complex data relationships. However, static charts may sometimes
struggle to engage readers and efficiently present intricate information,
potentially resulting in limited understanding. We introduce "Live Charts," a
new format of presentation that decomposes complex information within a chart
and explains the information pieces sequentially through rich animations and
accompanying audio narration. We propose an automated approach to revive static
charts into Live Charts. Our method integrates GNN-based techniques to analyze
the chart components and extract data from charts. Then we adopt large natural
language models to generate appropriate animated visuals along with a
voice-over to produce Live Charts from static ones. We conducted a thorough
evaluation of our approach, which involved the model performance, use cases, a
crowd-sourced user study, and expert interviews. The results demonstrate Live
Charts offer a multi-sensory experience where readers can follow the
information and understand the data insights better. We analyze the benefits
and drawbacks of Live Charts over static charts as a new information
consumption experience
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