Quantitative Data Visualization in Compartmented Force-Directed Graphs using Calibrated Columns

Thumbnail Image

Files

Kaliappan_umbc_0434M_11644.pdf (1.59 MB)
pdf032.pdf (493.57 KB)

Links to Files

Permanent Link

http://hdl.handle.net/11603/15031

Collections

UMBC Theses and Dissertations

Author/Creator

Author/Creator ORCID

Date

2017-01-01

Type of Work

theses
Text

Department

Computer Science and Electrical Engineering

Program

Computer Science

Citation of Original Publication

Rights

This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan through a local library, pending author/copyright holder's permission.

Subjects

Abstract

Networks are commonly used to represent data and relationships. However, when mapping nodes to quantitative data, it is often difficult to accurately identify both the values of individual nodes and the overall relationships among nodes. Simultaneous group detection and precise quantitative data reading are necessary for scientists interpreting critical data. Here, we hypothesize that the calibrated columns method for encoding large-range quantitative values will provide a more accurate reading of data values than the common approach of variable-area circles. We also hypothesize that the addition of subtle halos around nodes will support accurate grouping of spatially distributed nodes in a network. We have conducted a pilot study with seven critical tasks in order to understand quantitative data reading and group inferencing in networks having up to three-levels of complexity. Our results show that (1) network size has a significant effect on confidence levels in grouping tasks; (2) the grouping encodings do not have a significant effect on confidence levels, but do significantly affect accuracy in the overall task; (3) the quantitative data encodings do not have a significant effect on confidence levels, but do significantly affect accuracy when determining exact node values; and (4) the colored halos and calibrated columns encodings are particularly useful in tasks involving both precise and global perception of the network. This work has contributed to understanding effective construction of quantitative networks and their groupings and our results suggest design guidelines broadly applicable to inform visualization design in domains such as biology and the social sciences.