Paper No. 7
Presentation Time: 10:30 AM


RYAN, Jeffrey G., Department of Geology, University of South Florida, 4202 East Fowler Avenue, SCA 528, Tampa, FL 33620-5201, THAU, David, Google Geo Team, 1600 Amphitheatre, Mountain View, CA 94043 and DE PAOR, Declan G., Physics Department, Old Dominion University, 235 OceanographyandPhysics Bldg, Norfolk, VA 23529,

[This is Table 4 at the Digital Geology Express session—a blend of workshop and digital poster. Free participant sign-up at Participants get a seat at each table and hands-on interaction.]

“Big data” geoinformatics projects and portals, such as Earthscope, the Integrated Earth Data Applications facility, the varied NASA-sponsored earth and planetary data repositories, and community tools-and-data enterprises such as UNAVCO and IRIS, are changing the way geoscientists do research, and have the potential to transform geoscience teaching. Integrating the interrogation of publicly accessible global datasets into geoscience curricula is of great instructional value, as the mining of these data resources can faclitate authentic undergraduate research experiences, both in the classroom and online (e.g., students model the scientific process and make real, publishable discoveries, as opposed to the canned "apprentice-style" projects where student engagement is limited, and the scientific outcomes are pre-defined).

A challenge for those charged with the oversight of global geo-data facilities is making their data searchable and visualizable to both novices and experts. New software developments point to a solution. Google Maps Engine (GME) and Google Earth Engine (GEE) are turning apps that were designed as browsers—GE and GM—into powerful data management systems. Maps can be made in GME and imported into GE. Of particular interest to solid earth geoscientists is the new ability to change the GE terrain using geoTiffs imported from GME. GEE permits the integration and visualization of data into the web-based GE API. Data examples from include Landsat and MODIS. Academic researchers can run algorithms on these data using Google’s thousands of parallel processors or they can link their own data sets to the GEE. A particularly effective way of visualizing global change is the TimeLapse feature. Both engines will be demonstrated at Table 4.