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The Art and Power of Data Imaging

[+] Author Affiliations
Jeffrey W. Lively

AMEC, Grand Junction, CO

Paper No. ICEM2013-96256, pp. V002T04A019; 9 pages
  • ASME 2013 15th International Conference on Environmental Remediation and Radioactive Waste Management
  • Volume 2: Facility Decontamination and Decommissioning; Environmental Remediation; Environmental Management/Public Involvement/Crosscutting Issues/Global Partnering
  • Brussels, Belgium, September 8–12, 2013
  • Conference Sponsors: Nuclear Engineering Division, Environmental Engineering Division
  • ISBN: 978-0-7918-5602-4
  • Copyright © 2013 by ASME


Data imaging and visual data assessment are veritable gold mines in the scientist’s quest to understand and accurately interpret numerical data. Graphical displays of various aspects of a dataset offer the analyst insight to the data that no mathematical computation or statistic can provide. It is difficult, at best, for even a skilled and observant statistician to understand the underlying structure of a dataset. Often, there is either too little data to get a good “picture” of the structure that might be present or there is so much data that one cannot readily assimilate it. Of course, the latter problem (too much data) is, in reality, no problem at all given the abilities of modern computers and software systems to manage large amounts of data.

Advances in computer technology and the advent of the global positioning satellite system have enabled scientists from many fields of endeavor to collect and view data in its spatial context. Visual images constructed from spatially referenced data reveal the inherent richness and structure in the data and lead to more informed conclusions. So powerful is data collected with spatial context that a relatively new branch of mathematical statistics, geospatial statistics, has emerged. Geospatial statistics seek to exploit this context rich data form to better understand the spatial and co-relationships that might exist, but would be otherwise hidden in tabular data or obscured with classic statistical approaches.

This paper (and accompanying presentation) will show the power that visual data assessment possesses to understand radiological scanning data and to make confident and accurate decisions based on the data images. It will challenge the traditional mathematical concept of detection limits for scanning. It will demonstrate that more data, even if the individual datum comprising the dataset is of “poorer quality” (i.e., has a larger uncertainty and, thus, a larger calculated minimum detection value), is significantly more powerful than a smaller dataset comprised of higher quality measurements. This presentation will cause the open-minded health physicist to rethink how they prescribe, collect, evaluate, and make decisions based upon radiological scan data.

Copyright © 2013 by ASME
Topics: Imaging



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