Introduction: The application of Google Geo Tools to geoscience education and research
Published:October 01, 2012
- PDF LinkChapter PDF
J.E. Bailey, S.J. Whitmeyer, D.G. De Paor, 2012. "Introduction: The application of Google Geo Tools to geoscience education and research", Google Earth and Virtual Visualizations in Geoscience Education and Research, Steven J. Whitmeyer, John E. Bailey, Declan G. De Paor, Tina Ornduff
Download citation file:
Until relatively recently, only cosmonauts and astronauts had ever viewed the Earth as a planet suspended in space. These pioneers brought back stunning photographs of the “Blue Marble” and “Earth Rise.” It wasn't quite like being out there in orbit, but nevertheless such images profoundly influenced the global human psyche and promoted awareness of the finite nature of our home in the Cosmos. Now, with the development of virtual globe technology such as Google Earth (GE), everyone who has access to the Internet can visualize the Earth as if we were all astronauts.
GE has emerged as one of the most powerful and easy-to-use tools for viewing, tracking, and analyzing geological features, surface processes, and events. Since the application's release in 2005, GE's use in the geosciences has evolved from simple fly-bys of landforms to dynamic models displaying geologic processes. The diversity of applications of GE in geoscience education and research has been highlighted at the annual meetings of the American Geophysical Union (AGU) and Geological Society of America (GSA) in dedicated and popular sessions (Bailey, 2009).
Discussions at those meetings indicated the need for a specialized forum where development of virtual globe-based educational resources and visualizations could be coordinated among the greater geoscience community. The result was a GSA Penrose Conference, which brought together educators, researchers, publishers and software developers (from both and academia and Google Inc.) to discuss recent advances in the development of educational modules and research visualizations that use the Google mapping services and related tools.
The conference was held onsite at the Google Inc. headquarters in Mountain View, California (https://sites.google.com/site/gepenrose/) in January 2011. The primary goal was to pool ideas and resources from the broader community, with the hope of stimulating new initiatives and directions in the use of GE in the geosciences, as well as encouraging the active participation of Google Inc. in the future development of geoscience education tools. The papers in this special volume highlight cutting-edge educational and research uses that were demonstrated in Mountain View, along with examples of projects that developed from collaborations established at this meeting.
The volume is organized into four sections: (i) Data Visualization; (ii) Digital Mapping; (iii) Virtual Field Experiences; and (iv) Educational Models, Learning Methods, and Assessment. The foci of each section and their importance to geoscience education and research are described in the rest of this paper.
Google Earth is a computer program that integrates a global digital elevation model (DEM) with base surface imagery to create a 3D, mirror-world representation of the Earth (Bailey, 2010). Technically speaking, GE is only 2.5D as the model is projected onto a 2D computer screen with the appearance of being 3D. The combination of terrain and land coverage data, literally, offers a whole world for students, teachers, researchers, or anyone to explore.
Google's large investment in acquiring global high-resolution data, and efforts to make important imagery available in a timely manner (Google Inc., 2010; Bradley et al., 2011; Hennessey-Fiske, 2011), has created an archive of imagery that individual researchers would not have the resources to compile. For geoscientists, this has provided imagery good enough to perform surveys that were traditionally only achievable through field-based methods. The utility of this archive is emphasized in Tewksbury et al. (this volume, chapter 2) and Fisher et al. (this volume, chapter 1). The authors in both papers use GE imagery to map surface morphologies related to regional geology. Tewksbury et al. (chapter 2) demonstrate this for the mapping of folds, faults, and lithological units in Egypt's Western Desert. Fisher et al. (chapter 1) have used GE to define channels widths in the Himalayan Mountains, landslide properties in Haiti, and fault characteristics in California.
For those who have their own data, GE also offers a framework through which to easily view and share that data. For example, Crosby (this volume, chapter 3) describes how a growing archive of high-resolution Lidar (light and detection ranging) derived data has been made easily accessible through GE. Williams et al. (this volume, chapter 4) use GE to view other imagery, in their case derived from ground penetrating radar (GPR) and magnetic gradiometry, and describe how GE can be used to annotate these data.
GE provides a canvas to which users can add their own geospatial data to create dynamic visualizations using Keyhole Markup Language (KML). This code is a type of eXtensible Markup Language (XML) and thus is similar to Hypertext Markup Language (HTML) in form and structure (Wernecke, 2009). Like HTML, the function of many KML elements is self-evident. For example, <color>value</color> defines the color of an object, making KML a user-friendly and easy-to-learn coding language.
The idea of directly editing code is not a natural concept for many educators or even scientific researchers, as it lies outside their comfort zone or might involve learning time they do not have available. Fortunately, GE offers a way for non-developers to create visualizations as many (though not all) KML elements can be created directly in GE through the built-in graphical user interfaces (GUIs). Users do not need to understand the underlying code as features such as color are manipulated directly through on screen widgets (Fig. 1).
The combination of GE and KML has opened up possibilities that previously only existed for those with knowledge of, and access to, proprietary applications (e.g., ArcGIS). Stewart and Baldwin (this volume, chapter 5) describe a case where geolocated photographs and videos were shared using KML, rather than as GIS shapefiles, to address the sustainability of marine resources.
One of the much-debated questions about GE (and Google Maps) is whether they can or should be considered a type of GIS (Turner, 2008). Arguments against it being identified as such generally emphasize the lack of built-in analytical capabilities similar to those found in ArcGIS or other image analysis programs. In GE the application is only concerned with the spatial location, size and other <Style> properties of the geometry (vector data) and imagery (raster data). It does not consider the inter-relation between different KML objects or relative “values” of pixels in an image, which are core functions in ArcGIS. However, by linking GE to other Google services some of these limitations can be overcome. For vector data, Google Fusion Tables (Gonzalez et al., 2010) can provide the functionality of geospatial relational database in a user-friendly interface. For imagery, Google Earth Engine (Google Inc., 2011), while still early in its development, has exciting potential for manipulating raster images directly within the GE API.
De Paor et al. (this volume, chapter 6) briefly describe how to extend the functionality of GE by linking to Fusion Tables. Zular et al. (this volume, chapter 8) have implemented an example that combines geomorphologic observations made in GE with laboratory analyses data collated in a Fusion Table. Nunn and Bentley (this volume, chapter 9) employ further capabilities of Fusion Tables to create charts to display spatial and temporal trends in Louisiana's water usage. All of these implementations of Fusion Tables contain data with location components, and so have the option to be displayed on a map. The Google Maps API is integrated into Fusion Tables, but Fusion Tables also generate KML links and <iframe> code (the latter allows that instance of Google Maps to be embedded on a web page). The KML can be downloaded as a “current view” static file or as a network link. The advantage of the latter is that any changes made to the Fusion Table, and hence the KML, are pushed to the user when the link is refreshed, without the user having to download a new file.
Online links also allow interaction between GE and other “cloud” services, such as Google Docs and Really Simple Syndication (RSS) blog feeds. Innovative research has taken advantage of these links to generate dynamic KML that can track events in near-real time. For example, Potapov and Hronusov (this volume, chapter 10) use these services to create KML that maps the movements of radio-tagged deer and birds.
DIGITAL GEOLOGIC MAPPING
While KML allows users to augment the GE landscape, the base globe itself has also developed into an important tool for education and research. GE provides a universal interface to imagery and derived products. It enables users to easily view spatial and temporal changes across images and maps, and to then mark or annotate features using KML. These functions are also possible in traditional GIS technologies, but the methods can be cumbersome and for many purposes the advantages of GE (accessible, free, simple user interface) outweigh the advantages of traditional GIS (more exact mapping, control of map projections, sophisticated analytical tools).
Shufeldt et al. (this volume, chapter 11) highlight benefits of using GE rather than printed materials for displaying geological maps. In particular, they emphasize the ability of GE to drill down into different scales of data by displaying higher resolution maps as the user's viewpoint gets closer to the ground. This tile pyramid method, which is also used by the base imagery, is the core of what makes GE special. Although this was previously possibly in desktop GIS applications, what stands GE apart is how tile pyramids (Fig. 2) have been optimized to load rapidly, transition smoothly, and work on all sizes of computing devices (smartphones, pads, laptops). This accessibility has encouraged many researchers to migrate data from GIS environments (Guth, this volume, chapter 12) and take the time to create visualizations of pre-digital maps. Simpson et al. (this volume, chapter 13) describe a case study of the latter for mapping of Vredefort Dome, South Africa.
For most geologists, remote mapping will never totally eliminate the need for fieldwork (Hasbargen, this volume, chapter 14). However, the scaling ability of GE can be combined with field-based knowledge to create maps across spatial scales that would be logistically impractical or complex on the ground (Lageson et al., this volume, chapter 15). As the archive of high-resolution imagery increases, there is also meaningful data to be found across temporal scales, e.g., landscapes shaped by active tectonic processes or surface processes. At a minimum, GE enables the identification of sites on which to focus ground-based research (Tewksbury et al., this volume, chapter 2), eliminating a need a for reconnaissance field trips.
Another advantage of using GE for remote geologic mapping is the 3D perspective and the ability to view 3D models of geologic structures. KML uses <Model> to display COLLAborative Design Activity (COLLADA) models read from Digital Asset Exchange (DAE) format files. These are easily created using SketchUp, a 3D modeling computer program that combines a tool-set with an intelligent drawing system (Fig. 3). SketchUp models can be imported into GE to show both above ground outcrops (De Donatis et al., this volume, chapter 16) and block diagrams of geological cross-sections (Karabinos, this volume, chapter17). Hill and Harrison (this volume, chapter 18) demonstrate that it is also possible to use SketchUp to fix limitations in GE's topographic model by adding terrain in areas where the resolution the data used is not high enough to show the real-life shape of the landscape.
Innovative technology is also augmenting the modern field geologist's view with a 3D perspective. Wang et al. (this volume, chapter 19) describe a systematic workflow to use an Apple iPad to manually trace geological features on 2D photos and then use them to construct 3D models of an outcrop. Tools such as these and applications such as SketchUp are changing geologists' mindsets from both an educational and a field-based research perspective.
VIRTUAL FIELD EXPERIENCES
The impact of GE on geologic mapping techniques has been described, but its use in educational settings also has helped modernize both the methods and mindsets of students toward geology. Geology is an exciting and dynamic science but it can also be limited in the classroom by the fact that traditional information sources (lectures, laboratory exercises, textbooks, and even multimedia resources) cannot always convey the scope and setting of landscapes and geologic features. The use of virtual field experiences (VFEs), especially those based in GE, can remove that limitation and provide a proxy for field trips that are not logistically feasible.
There are problems with VFEs, especially when considering their practical and pedagogical design, which are discussed by Granshaw and Duggan-Haas (this volume, chapter 20), but there are also major advantages aside from making “trips” logistically possible. It has been suggested that the ability of GE to scale from a whole-earth global view to a detailed outcrop image gives geologists a perspective and insight that was previously hard to gain (Whitmeyer et al., 2008). The same is true for GigaPans, high-resolution photographic panoramas that employ the same method of image tile pyramids that make GE so efficient (Fig. 4). Although one GigaPan is a single photomosaic, it is possible to build a series of GigaPans at different scales for the same target, e.g., outcrop to thin section. Using a common topic or theme, Piatek et al. (this volume, chapter 21) demonstrate how these GigaPan series act as VFEs that are useful for explaining geologic concepts.
A limitation of VFEs is the lack of student-to-student interactions within the virtual environment—something that is an important component during real field trips. Many students will ask their peers questions before seeking help from the instructor, especially when new concepts are being encountered. To overcome this limitation Dordevic and Wild (this volume, chapter 22) have developed a way for avatars—virtual representation of users—to communicate and cooperate while exploring field locations with the GE API. The movements and actions of these avatars can be logged allowing VFEs to be refined and to scaffold student learning.
GE offers an ideal virtual field environment as “trips” across the real-world mirror landscape can be augmented with KML to provide extra information. This information compensates for the lack of a field trip instructor. Most commonly, a VFE in GE displays information using different types of the <Placemark> geometry (points, lines or polygons) and associated description balloons containing text and images. The user navigates to each placemark and clicks on the object to display the balloon. For example, Lang et al. (this volume, chapter 23) describe a numbered sequence of point-placemarks, which represent “stops” on a trip around the volcanoes of Tenerife, Spain. This work was based on a real-life field trip that followed the same route.
Using a similar approach, Muller (this volume, chapter 24) created placemarks and description balloons to archive 55 years of road logs from field trips offered by the New York State Geological Association (NYSGA). Muller makes use of Fusion Tables to store, and in some cases merge, the KML created. Muller's work takes advantage of the detailed, though not always correct, geologic and route descriptions contained in the NYGSA guidebooks. By comparison, Rueger and Beck (this volume, chapter 25) created KML based on information gleaned by them and others (Clark, 2003) from historical documents and journals on an eighteenth century military march into Canada. Rueger and Beck's goal was not just to map the march's route, but to use GE to interpret how the landscape influenced the path taken. This contrasts with Muller's NYSGA guidebook KML, which describes the geology along a specific pre-planned route. The concept that VFEs can add to the understanding of observations made in the real-world was tested by Eusden et al. (this volume, chapter 26) in an introductory geology class. They used GPS devices and cameras to collect multimedia data during a field trip and displayed that data in GE using KML created by the students. Assessment of the results was qualitative in nature, but suggested that this approach is beneficial to learning.
EDUCATIONAL MODELS, LEARNING METHODS, AND ASSESSMENT
A potential flaw of VFEs is that sometimes there is too much freedom for users, especially if there is a large environment to explore and the exploration is left open ended. While GE can contribute to this flaw, it also includes functionality that can be used to guide users though the virtual environment; Google Earth Tours. These tours are KML scripts that record movement around the 3D globe and interaction with other KML objects. A tour can be replayed to replicate that same movement and interaction. Tours can be automatically generated from a folder of placemarks (Fig. 6) or a path, or they can be freeform and used to record the user's independent navigation. Since GE Tours are composed of KML code, they can be shared as easily as any KML files, so once created they offer a distributable, “guided” VFE.
GE Tours are easy to create, but the author of a tour also needs to consider the user experience, the subject matter being illustrated, and the learning objectives. Sometimes it might not be appropriate to use a GE Tour, but in many cases they are a very useful approach if designed appropriately. Treves and Bailey (this volume, chapter 28) describe a series of design-based best practices that are recommended for developers creating GE Tours for educational purposes.
Design is an important consideration for all classroom uses of GE. An educator needs to consider the scale of their goals, whether the goal is to broadly encourage geo-education (Lee and Guertin, this volume, chapter 29) or to specifically integrate GE into a curriculum (Almquist, this volume, chapter 30). GE and KML also provide a common medium for collaborations between students around the world. An example of this is the Northern Environmental Education Development project (NEED), an initiative involving schools in Ireland, Norway, Iceland, and Finland that seeks to share geo-visualizations (Hennessy et al., this volume, chapter 31).
Currently, GE's most common role in the classroom is to provide additional illustrations for specific geoscience topics. There exist many “good” sources of geoscience KML. Well-known examples include: The Smithsonian's Volcanoes of the World (Venzke et al., 2006); National Snow and Ice Data Center's ice and glacier mapping (Ballagh et al., 2011); the U.S. Geological Survey's real-time earthquake locations (Blair and Ticci, 2006); the National Oceanic and Atmospheric Administration's severe weather tracking (Smith and Lakshmanan, 2011); and NASA's atmosphere profiling (Chen et al., 2009). Using these resources, or by developing new ones, educators are finding a role for GE and other Google Geo Tools within their subject areas. Examples include geomorphology (Dolliver, this volume, chapter 32), hydrology (Habib et al., this volume, chapter 33) and oceanography (Hochstaedter and Sullivan, this volume, chapter 34).
The question remains though, “How is Google Earth influencing learning?” Answering this question is key, as it will determine the direction towards which educational developers need to head (Gobert et al., this volume, chapter 35). Some studies, especially in the area of GE-based virtual field experiences and tours, have included assessments of the impact on students (Treves and Engelbrecht, 2011; Johnson et al., 2011; Eusden et al., chapter 26), and early signs are encouraging. However, these assessments were qualitative or semi-quantitative. More studies targeting in-depth testing of the impact of Google Geo Tools are needed as new educational strategies are developed.
Google Earth is now a much-used and relied upon application for geoscience educators and researchers. It is in their interests to see GE improve and better fit their needs, as well as encouraging the development of associated applications (e.g., SketchUp, GigaPan), which add to the utility of GE. Continued communication among all parties—educators, researchers, developers, publishers, and others with an interest in that process—are important. The 2011 GSA Penrose Conference provided these parties with an opportunity to gather in one location and develop new collaborations. This special volume highlights collaborations and projects founded or shared at that gathering, and is a documentation of the importance of GE and related tools to geoscience education and research.
Engaging scientific visualizations are important. They have the potential to bring data to life, increase understanding of natural processes, and promote awareness of global change. We believe that GE-based visualizations are already transforming geoscience education and research and that this is only the beginning.
The editors would like to express our appreciation to Google Inc. for hosting the 2011 GSA Penrose Conference that led to the publication of this special volume. We sincerely thank both the conference attendees and authors of papers in this special volume for their participation and enthusiasm. We would also especially like to acknowledge the reviewers whose time and efforts made the publication of this special volume possible:
Carlos Aiken, Steve Allard, Heather Almquist, Chuck Bailey, Greg Baker, Alan Benimoff, Callan Bentley, Andy Bobyarchick, Katherine Boggs, Andre Breton, Anna Courtier, Rónadh Cox, Chris Crosby, Holly Dolliver, Mauro De Donatis, Natalia Deligne, Don Duggan-Haas, Tyler Erickson, Dyk Eusden, Martin Feely, Ioannis Georgiou, Tod Greene, Laura Guertin, Peter Guth, Michael Harrison, Les Hasbargen, Matt Heavner, Ronán Hennessy, Otto Hermelin, Jesse Hill, Victoria Hill, Fred Hochstaedter, Eric Horsman, Micah Jessup, Paul Karabinos, Eric de Kemp, Tom Kurkowski, Dave Lageson, Wes Lauer, Nick Lang, Jack Loveless, Karen McNeal, Riley Milner, Alexandra Moore, Otto Muller, Dan Murray, Rick Murray, Jeff Nunn, Carol Ormond, Terry Pavlis, Lyman Persico, Jen Piatek, Arancha Pinan-Llamas, Phillip Prince, Eric Pyle, Bruce Rueger, Uwe Schindler, Peter Selkin, Colin Shaw, Owen Shufeldt, Jill Singer, Gary Solar, Meg Stewart, Barb Tewksbury, Dave Tewksbury, Ryan Thigpen, Peter Thompson, Sarah Titus, Rich Treves, Rich Whittecar, Crystal Wilson, Mike Winiski, Christine Witkowski, Michael Wizevich, and Andre Zular.
Special thanks are due to Chris Condit, who served as special editor in cases where the regular editors had a conflict of interests.
The Google Penrose Conference at Mountain View and this GSA Special Paper were funded in part by NSF TUES 1022755. Any opinions, findings, and conclusions or recommendations expressed in this volume are those of the authors and do not necessarily reflect the views of the National Science Foundation or Google Inc.