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Improved analyses using function datasets and statistical modeling

Year:

2014

Publication type:

Paper (invited, offered, keynote)

Primary Station(s):

Rocky Mountain Research Station

Source:

In: Proceedings of the 2014 ESRI Users Conference; July 14-18, 2014, San Diego, CA. Redlands, CA: Environmental Systems Research Institute. Online: http://proceedings.esri.com/library/userconf/proc14/papers/166_182.pdf.

Description

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework with .Net numeric libraries. Our new framework streamlines raster modeling and facilitates predictive modeling and statistical inference.

Citation

Hogland, John S.; Anderson, Nathaniel M. 2014. Improved analyses using function datasets and statistical modeling. In: Proceedings of the 2014 ESRI Users Conference; July 14-18, 2014, San Diego, CA. Redlands, CA: Environmental Systems Research Institute. Online: http://proceedings.esri.com/library/userconf/proc14/papers/166_182.pdf.

Publication Notes

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
https://www.fs.usda.gov/treesearch/pubs/46334