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    Author(s): John S. HoglandNathaniel M. Anderson
    Date: 2014
    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.
    Publication Series: Paper (invited, offered, keynote)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (718.0 KB)

    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.

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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.

    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.

    Keywords

    modeling, spatial analysis, geographic information system (GIS)

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https://www.fs.usda.gov/treesearch/pubs/46334