| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | March 23, 2020 |
| Latest Amendment Date: | May 17, 2024 |
| Award Number: | 1942659 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Dan Cosley
dcosley@nsf.gov (703)292-8832 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | April 1, 2020 |
| End Date: | March 31, 2026 (Estimated) |
| Total Intended Award Amount: | $531,435.00 |
| Total Awarded Amount to Date: | $547,435.00 |
| Funds Obligated to Date: |
FY 2021 = $103,959.00 FY 2022 = $121,572.00 FY 2023 = $108,800.00 FY 2024 = $112,147.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 (617)253-1000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
77 Massachusetts Ave Cambridge MA US 02139-4307 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | HCC-Human-Centered Computing |
| Primary Program Source: |
01002122DB NSF RESEARCH & RELATED ACTIVIT 01002223DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This research extends the PI's unique and transformative Vega-Lite grammar and language for specifying information visualizations to focus on mechanisms for interaction. New tools will be integrated with popular open source / industry research authoring platforms. Prior research has developed theories of effective visual encoding (i.e., how best to map data values to visual properties such as position, shape, or size). Implementing these theories in software has advanced society's adoption of visualization as a medium for recording, analyzing, and communicating about data. However, there has been little analogous theory-building for interactivity, a critical component for enabling tight feedback between generating and answering hypotheses. For instance, how do different interaction design choices affect dataset coverage, the rate of insights, and people's confidence in their findings? Limits of prior theory impede support for interaction design in visualization systems, and establishing conventions for interaction design. For example, in different tools, dragging may pan a chart, highlight brushed points, or zoom into a selected region.
This research will develop theory by evaluating interaction techniques for information visualization via crowdsourced, laboratory, and field studies. Design choices, data distributions, and analytic tasks will be investigated, vis-à-vis measurable outcomes, such as usability, completion time, accuracy, and higher-level cognition. The impact of resulting new theory on techniques for interactive visualization will be studied, addressing research questions such as: (1) How to present results to augment static visualizations with effective interactivity? (2) How to promote exploration? (3) How to suggest unexplored visualization states?
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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