What is Hogland's Spatial Solutions?
What is Hogland's Spatial Solutions?
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Hogland's Spatial Solutions is a website devoted to sharing information, knowledge, science, and solutions to tabular, spatial, statistical, and machine learning problems as they relate to remote sensing, Big Data, and natural resources. Much of the theory, concepts, datasets, workshops, and software presented on this site can be found scattered throughout the primary literature. However, this site provides a centralized location for those resources while also expanding upon what is available at other locations. If you are interested in spatial, statistical, and machine learning research, check out our Research page. If you want to learn various modeling concepts, open the Learning page. If you would like to start using our modeling techniques and software, explore the Software page.
Hogland's Spatial Solutions is a website devoted to sharing information, knowledge, science, and solutions to tabular, spatial, statistical, and machine learning problems as they relate to remote sensing, Big Data, and natural resources. Much of the theory, concepts, datasets, workshops, and software presented on this site can be found scattered throughout the primary literature. However, this site provides a centralized location for those resources while also expanding upon what is available at other locations. If you are interested in spatial, statistical, and machine learning research, check out our Research page. If you want to learn various modeling concepts, open the Learning page. If you would like to start using our modeling techniques and software, explore the Software page.
Happy Modeling!
Happy Modeling!
BIO
BIO
Dr. John Hogland
Dr. John Hogland
Dr. Hogland is a Research Forester working for the Rocky Mountain Research Station. His research interests revolve around quantitative methods within geographic information systems (GIS) and understanding the relationships between landscape patterns and forested ecosystems processes. Current project include: 1) Quantifying forest characteristics at fine spatial scales, 2) designing, developing, and building new procedures that integrate machine learning and statistical modeling with fast raster processing (Function Modeling) to streamline spatial modeling and reduce storage space associated with GIS analyses, and 3) developing sampling strategies focused on reducing the cost of sampling while maintaining the characteristics of a representative sample.
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