Information
- Publication Type: PhD-Thesis
- Workgroup(s)/Project(s):
- Date: August 2019
- 1st Reviewer: Helwig Hauser
- 2nd Reviewer: Ingrid Hotz
- Rigorosum: 19. August 2019
- First Supervisor: Ivan Viola
Abstract
In this cumulative thesis, I describe geometric abstraction as a strategy to create an integrated visualization system for spatial scientific data. The proposed approach creates a multitude of representations of spatial data in two dominant ways. Along the spatiality axis, it gradually removes spatial details and along the visual detail axis, the features are increasingly aggregated and represented by different visual objects. These representations are then integrated into a conceptual abstraction space that enables users to efficiently change the representation to adjust the abstraction level to a task in mind. To enable the expert to perceive correspondence between these representations, controllable animated transitions are provided. Finally, the abstraction space can record user interactions and provides visual indications to guide the expert towards interesting representations for a particular task and data set. Mental models of the experts play a crucial role in the understanding of the abstract representations and are considered in the design of the visualization system to keep the cognitive load low on the user’s side. This approach is demonstrated in two distinct fields of placenta research and in silico design of DNA nanostructures. For both fields geometric abstraction facilitates effective visual inspection and modeling. The Adenita toolkit, a software for the design of novel DNA nanostructures, implements the proposed visualization concepts. This toolkit, together with the proposed visualization concepts, is currently deployed to several research groups to help them in nanotechnology research.Additional Files and Images
Weblinks
BibTeX
@phdthesis{miao_thesis_2019,
title = "Geometric Abstraction for Effective Visualization and
Modeling",
author = "Haichao Miao",
year = "2019",
abstract = "In this cumulative thesis, I describe geometric abstraction
as a strategy to create an integrated visualization system
for spatial scientific data. The proposed approach creates a
multitude of representations of spatial data in two dominant
ways. Along the spatiality axis, it gradually removes
spatial details and along the visual detail axis, the
features are increasingly aggregated and represented by
different visual objects. These representations are then
integrated into a conceptual abstraction space that enables
users to efficiently change the representation to adjust the
abstraction level to a task in mind. To enable the expert to
perceive correspondence between these representations,
controllable animated transitions are provided. Finally, the
abstraction space can record user interactions and provides
visual indications to guide the expert towards interesting
representations for a particular task and data set. Mental
models of the experts play a crucial role in the
understanding of the abstract representations and are
considered in the design of the visualization system to keep
the cognitive load low on the user’s side. This approach
is demonstrated in two distinct fields of placenta research
and in silico design of DNA nanostructures. For both fields
geometric abstraction facilitates effective visual
inspection and modeling. The Adenita toolkit, a software for
the design of novel DNA nanostructures, implements the
proposed visualization concepts. This toolkit, together with
the proposed visualization concepts, is currently deployed
to several research groups to help them in nanotechnology
research.",
month = aug,
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Research Unit of Computer Graphics, Institute of Visual
Computing and Human-Centered Technology, Faculty of
Informatics, TU Wien ",
URL = "https://www.cg.tuwien.ac.at/research/publications/2019/miao_thesis_2019/",
}