Abstract Details
Activity Number:
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137
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #311019
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View Presentation
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Title:
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Conjoint Parametrers Directed to Isolating 'Hot Spots' Within Big Data
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Author(s):
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Turkan K. Gardenier*+ and John S. Gardenier
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Companies:
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Pragmatica Corp. and NCHS (Retired)
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Keywords:
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Trinary ;
Conjoint ;
GIS ;
Scanning efficiency ;
Layers ;
Health Service Areas
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Abstract:
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Evolving technological advances in data processing and visualization have made it feasibe to delve into fine units and multiple layers. Geographic Information Science (GIS) provides latitude/longitude based shape-files for analysis, making it possible to isolate locations where observed risk-related events may be related to demographic, environmental, or public health factors. In this paper, a strategy based on Trinary or three-category allocation will be presented. This method introduces efficiency to the process of scanning and evaluation across multiple maps. It is an extension of a step-function based approach recently applied to 20-year data for Carbon Monoxide (CO) and Nitrogen Dioxide (NO2)in efforts to detect trends over time. These relate to U.S. lung cancer mortality rates mapped by gender and race at the Health-Service Area (HSA) level. Features such as statistical distributional characteristics,e.g. Gaussian assumptions, will be addressed relative to allocation of individual records across multiple conjoint parameters.
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Authors who are presenting talks have a * after their name.
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