Campus Interior—3D Visualization Model
Cheyne Hadley, Doug Carreiro, Paul Muse, and Scott Prindl
Department of Geography
University of California, Santa Barbara
With ever advancing computer technology, representation of real world objects in a virtual space has become increasingly easier. Using this technology, a true to life 3D representation of Phelps Hall was constructed for GIS applications.
UCSB Campus Outside 3D Model
Benjamin Swardlick, Jeffrey Munowitch, Eric Tomczak, John Manus
Promotion, Organization and Management of the UCSB Greenhouse and Garden Project
Kenneth G Fairbarn Jr., Daniel C Doran, Matthew Dursum, Jodi Woods
UCSB’s Labyrinth Location Suitability Analysis
Shannon Moy, Sarah Horwath, Justin Healy, and Ricardo Kiresich
Creating a Geodatabase of World Oil and Water Data for use in Geography 7
Joey Lecky
Department of Geography
University of California, Santa Barbara
Acknowledgements: Catherine Gautier, Indy Hurt, and Amanda Henley
Course Description: Oil and water are two key strategic resources dominating the international scene. Using ArcGIS, this project provides an overview of global distributions of oil and water resources and analyzes some of the social, economic, and geopolitical ramifications of these distributions.
Phelps Hall Restrooms Lighting Conversion Priorities
Madeleine Crump, Coryl Dolfin, Kevin Le
Acknowledgements: Indy Hurt, Keith Clarke, Perrin Perregrin, Katie Maynard, Jerome Ripley, Paul Bartsch, Flex Your Power Org., Earth Easy Org.
UCSB Earthquake Risk Assessment
Madeleine Crump, Coryl Dolfin, Kevin Le
Acknowledgements: Indy Hurt, Keith Clarke, Perrin Perregrin, Katie Maynard, Jerome Ripley, Paul Bartsch, Flex Your Power Org., Earth Easy Org.
UCSB Virtual History Tour
Daniel Inloes, Michael Hino, Scott Heimerman, Cody Kaufman
Acknowledgements: Indy Hurt, Suzanne Foss
Determining spatial usage of the West Nile virus using surveillance data
Josh Bader
Department of Geography
University of California, Santa Barbara
Knowing the distribution of West Nile Virus (WNV) can help direct mosquito control efforts and alert the public to high-risk areas, and thus has public health significance. What is needed then is a reliable method of using surveillance data to predict the locations of WNV activity. The purpose of this research is to answer three questions: How can the range of WNV be defined in terms of surveillance data and ancillary environmental variables? What methods are best suited for the spatial analysis of WNV surveillance data? How can additional sampling be directed so as to best improve the predicted distribution.