Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

Technical Report · Fri Jan 01 04:00:00 EST 2016

DOI:https://doi.org/10.2172/1235087· OSTI ID:1235087

Stone, Daithi ^[1]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE
• OSTI ID: 1235087
• Report Number(s): LBNL--1003958; ir:1003958
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
information interfaces and presentation
multi-resolution projection
multi-variate
nonparmetric statistics
simulation and modeling
simulation output analysis
visualizing data variation