Google Earth as a Decision Support System for Targeting College Admissions Drives

Leonidas Deligiannidis, Michael Werner, John Russo

Wentworth Institute of Technology Department of Computer Science

550 Huntington Avenue Boston, MA 02115, USA

{deligiannidisl,wernerm,russoj}@wit.edu

Abstract - Visualization using Google Earth is an effective way of presenting geographically linked data. The GEW project shows how university administrators, particularly the admissions, alumni and development offices can deploy data they already have to enhance resource allocation in their recruiting and fundraising efforts. GEW provides an administrative interface for inputting spreadsheet data and a viewer interface for visualizing the data using Google Earth. Keywords: User Interface, Google Earth

1. Introduction

Targeting resources for generating college admissions has a geographical component. Limited advertising resources should be concentrated in the most promising markets. Tours by admissions representatives should be routed so as to produce the greatest potential yield. The development and alumni offices face similar problems as they plan remote alumni events and fundraising tours.

Google Earth [7] is a tool and technology that enables us to use dynamic real-time 3D visualization of geospatial information with minimum hardware requirements and computer skills. Using a mouse, the user can zoom in closer to a point of interest or zoom out to view a larger portion of the visualized area. Using streaming technology, low resolution imagery is replaced by high resolution imagery as the user zooms in. The vector component of Google earth allows one to overlay roads, places of interest such as local museums, hospitals, restaurants, etc on the current view. Google earth incorporates a geo-coding mechanism where the user can specify an address, city, state, zip code or just the country name to obtain longitude and latitude of places. A programmer’s interface (KML) is available to annotate the close-up views with overlays of text, images, and even 3-D constructions. This provides a superb way of visualizing geographically keyed data [4]. Google Earth uses the the Keyhole Markup Language (KML) file specification [11][12] as its input, which is XML based with Google Earth’s tagging.

Colleges and universities are data rich. Besides extensive record keeping on their own students, admissions departments also have data on potential students who applied but never enrolled. In addition admissions departments often exchange data or purchase lists of possible candidates from third parties. This data goes back a number of years enabling detection of trends from time series analysis. Similarly, alumni and development personnel keep records on graduates and other contacts.

However, effectively deploying the data resource requires some skill. The quantity of available data is overwhelming. Visualization techniques are required to make the data meaningful to admissions managers. Edward Tufte has made a

career demonstrating the use of effective graphics for visualizing data and statistics. The cover of his book The Visual Display of Quantitative Information [21] shows in dramatic fashion the diminution of Napoleon’s army as he marched from France to Moscow and back.

So how can university administrators effectively deploy data they already possess so as to optimize their recruiting efforts? For this, geographic visualization techniques are an important tool. GEW annotates the 3-D Google Earth maps with the university’s own data. When current enrollment data is used, GEW raises vertical columns at the student’s home towns, whose size is proportional to total enrollment from that town. In addition, the viewer can turn on a Google Earth overlay showing the locations of secondary schools in the town. Clicking on the enrollment markers (and school markers) brings up additional information that may be of use.

2. Related Work

In the last years, geospatial applications have become popular [7][14][15][22] and adopted by many researchers for scientific and information visualization [1][16]. Many people use these tools on a daily basis to plan trips, find restaurants, doctor’s offices, etc. Mashup is the term used to describe the integration of customized data on top of Google Earth (or Google Maps) [18]. One example is the gCensus [5] project for visualizing U.S. census data using Google Earth.

Visual Analytics is the term used to describe the facilitation of analytic reasoning by the use of interactive visual tools and techniques [19]. Geovisual Analytics refers specifically to analysis tools that draw on geospatial information to support decision making. Current projects would deploy Geovisual Analytics, including the use of Google earth for impact visualization and crisis management in the event of catastrophic natural and manmade disasters including floods and power grid failures. [2] [20].

In [10], 2D power grid diagrams are overlaid onto satellite maps to reveal the geographic location of transformer stations and potential points of failure. Other examples of the utilization of Google Earth are shown in [17] and [9], in particular weather forecasting applications viewable on mobile devices. In [8] a series of earthquake charting applications is shown.

3. GEW’s User Functionality

GEW has two types of users as shown in figure 1: 1) An administrative user responsible for preparing spreadsheets containing the data sets to be visualized; 2) An earth browser (such as an admissions counselor) who uses the tool to plan a recruiting drive.

Figure 1. Type of users.

3.1 GEW Use Cases

1) Load Data – The administrator prepares the data to be displayed in a spreadsheet and saves it in a tabdelimited text file. She then runs a script to create the database and upload it to the web server.

Choose Attributes

Browse

Select Data Series

Load Data

Earth Browser

Administrator

2) Select Data Series – The Earth Browser launches GEW from a desktop icon and sees a window as in Figure 2 (Here the original version of GEW is described. Section 6 describes the newer version which our Admissions Department plans to deploy). She selects the data to visualize. GEW has been designed to be used even by novice users. WebStart technology allows us to simply point the users at a URL to launch GEW.

Figure 2. GEW’s Graphical User Interface. 3) Choose Attributes – The browser user selects attributes to customize the browsing experience, i.e. magnitude can be

visualized by markers varying in a) height, b) width, or c) 3D bars, as shown in the figures 3, 4, and 5 respectively. For admissions purposes, height could be proportional to the number of students that come from a particular town. The user selects the type of visualization and the data series of interest. After the selections, the user needs to click on the “RUN” button, shown in figure 2. Then, GEW generates the KML file, which is used as input to Google Earth, and invokes Google Earth. Google Earth loads the file and presents the visualization to the user.

Figure 3. Quantity by height.

4) Browse – The user can employ the usual Google Earth navigation techniques, such as fly to, zoom-in, zoom-out, dragging, etc. The data is visualized on the maps. In all three types of visualization, a place-mark is placed at the top of the visual component representing a point of interest on the map. This way, all place-marks are visible from above and are easily selectable by the user – the place-marks are not hidden from the view by any lines or 3D models. The place– marks are semi-transparent so that they do not occlude the view. When the user hovers the mouse over a place-mark, the place-mark becomes opaque, its color becomes yellow-greenish and becomes enlarged. The line connecting the place-mark to the point on the map also becomes highlighted – by changing its width, as shown in figure 6.

Figure 4. Quantity by width.

Figure 5. Quantity by 3D place-marks.

Figure 6. Highlighted place-mark in foreground. Normal place-mark (semi-transparent) in the background. Upon selecting a place-mark, additional information is provided to the user, via a balloon, such as city, state, country, enrolled students and their current status as shown in figure 7.

Figure 7. Place-marks provide additional information.

4. GEW’s Architecture

GEW is implemented entirely in Java utilizing WebStart technology. A simple Graphical User Interface (GUI), see figure 2, allows a user to specify the data to be visualized and the type of visualization. By default, the data is stored on the web server.

We extracted the data from the admissions server and placed it on the web server. However, it is possible that a user can load data from her local computer or a remote server as long as the file format complies with GEW’s file specification. GEW, running on the client machine where Google Earth is also installed, generates a KML file based on the user’s selections. This KML file is passed to Google Earth which sends it to Google Earth’s server and receives streaming data of the maps as well as the placemarks specified in the KML file, as shown in the figure 8.

Figure 8. GEW’s software Architecture.

5. Discussion

In order to understand better how a group of typical users would utilize GEW, we presented the application to our Admissions Department utilizing data of currently enrolled students provided by the Registrar of our institution. We were able to show the admissions staff how they could drill down both by major and then by geographic location. Presently, many of the admissions counselors utilize maps on walls with pushpins as shown in figure 9.

Figure 9. Physical map with pushpins to visualize where our students come from. While these maps are useful for individual counselors responsible for one particular territory, the information is not easily aggregated for an entire country or for one major. With GEW, one can see an overview as shown in figure 10 and then drill down to individual states, towns, and school districts.

Figure 10. Overview of visualized data-set. The outcome of our meeting with the Admissions staff was quite positive. The staff had several suggestions, including expanding the domain of users to include the President and the Alumni Office. In addition, the staff suggested some additional functionality:

• Ability to group by a set of majors or all majors

• Ability to compare side by side prospects and admitted students

• Ability to compare side by side admitted students with enrolled students in first year

• Functionality to see schools in a particular target area From this feedback, it is clear that users need some sort of a front-end tool which would enable them to make selections to generate the KML file from several different parameters. This presents a challenge, since users may also want interactive drill-down capability while viewing a particular geographic area.

6. Current Status of GEW

After our meeting with the admissions counselors, we modified the initial GEW interface to:

• Perform student quantity visualization by height only (as shown in figure 3).

• Kept the functionality of performing queries and visualizations based on students’ majors.

• Added functionality to visualize all currently enrolled students in our institution.

• Limited the modifiable attributes to line height and provided a slider to control it. A snapshot of the current Graphical Interface of GEW is shown in figure 11.

Figure 11. New Graphical Interface of GEW.

7. Conclusion

This project has demonstrated the feasibility of using Google Earth customization to better target college admissions efforts. Currently, a prototype has attracted substantial interest at our institution and plans are underway to begin using GEM in the near future. Based on suggestions from the admissions office, usage may be expanded to the alumni and development offices.

8. Future Directions

Current plans are to expand the scope of the project to make it more useful to admissions counselors. Recent directions in recruitment have emphasized targeted marketing strategies [13]. The aggregate market for educational services is divided into a number of relatively homogeneous subsets. Demographics, including the geographic dimension are perhaps the most important segmentation basis. Many pertinent demographics are obtainable from U.S. census data, including educational attainment, income, race, etc. In addition:

Various organizations, including the College Board, ACT, and the National Research Center for College and University Admissions, compile vast databases with information on the demographics and credentials of millions of high-school students: their test scores, grade-point averages, intended majors, ZIP codes, extracurricular activities, and colleges in which they have expressed an interest.” [3].

A university may have several target markets, for example – one based on student types which have been successful in the past, one based on the need to achieve diversity, one based on the stated mission, etc. Once a target market has been identified it is possible to integrate the university’s internal data with publicly available census and other data so as to weight geographic subdivisions by their potential for producing the desirable applicants. Once weights have been calculated it is easy to deploy them on Google Earth so as to visually facilitate recruitment targeting.

The project can also be integrated with other admissions software. For example, Applications Quest [6] allows clustering of applications for the purpose of achieving diversity. We feel that enhancements along these lines will allow universities to better exploit their data resources in furtherance of their student recruitment and development goals.

9. References

[1] Clifton Forlines, Alan Esenther, Chia Shen, Daniel Wigdor, Kathy Ryall, “Multi-User, Multi-Display Interaction with a Single-User, Single-Display Geospatial Application”, UIST'06, October 15–18, 2006, Montreux, Switzerland, p273-276

[2] Dennis McGrath, Doug Hill, Amy Hunt, “IC2020: Embedded Simulation for Next Generation Incident Command Software” Proc. of the 2006 Huntsville Simulation Conference, October 18-19, Huntsville, AL, 2006.

[3] Elizabeth F. Farrell, The Power and Peril of Admissions Data, The Chronicle of Higher Education, Volume 53, Issue 8, Page A46

[4] Evan Ratliff, Google Maps Is Changing the Way We See the World, Wired magazine Issue 15.07, Jul 2007.

[5] GCensus: Using Google Earth for Census Analysis By Imran Haque, accessed from

http://www.extremetech.com/article2/0,1697,2102559,00.asp on Feb 23, 2008. See also http://gecensus.stanford.edu/gcensus/index.html.

[6] Gilbert, J.E. (2006) Applications Quest: Computing Diversity. Communications of the ACM, 49,3, ACM, pp. 99-104.

[7] Google Inc. Google Earth . http://earth.google.com/

[8] Home page of Google Earth Science at WPI http://web.mac.com/depaors/Site/KMZs.html retrieved Feb. 25 2008.

[9] Honkamaa P., Siltanen S., Jäppinen J., Woodward C., Korkalo O., “Interactive outdoor mobile augmentation using markerless tracking and GPS”, Proc. Virtual Reality International Conference (VRIC), Laval, France, April 2007, pp. 285-288.

[10] Jinming Ge, Khalil Habash “Simulation of the Impact of Power Grid Failure under Catastrophic Event”. In Proc. of the International Conference on Modeling, Simulation and Visualization Methods, June 2007.

[11] KML Documentation Introduction. Retrieved Mar 5, 2008 from http://code.google.com/apis/kml/documentation/.

[12] KML file specification http://earth.google.com/kml/

[13] Lewison, Dale M.; Hawes, Jon M., Student Target Marketing Strategies for Universities , Journal of College Admission, n196 p14-19 Sum 2007

[14] MapQuest. http://www.mapquest.com/

[15] Microsoft. Virtual Earth. http://local.live.com/

[16] R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky, 2006. Hierarchical Simplification of City Models to Maintain Urban Legibility, SIGGRAPH Sketches Program, 2006.

[17] Smith, T. M. and V. Lakshmanan, “Utilizing Google Earth as a GIS platform for weather applications”. In Proc. of the 22nd Conference on Interactive Information Processing Systems, Atlanta, GA, 2006.

[18] Sumit Bando and Darius Kasad, A Google Maps mash-up, Integrate external data with the Google Maps API,

JavaWorld.com, 01/16/06

[19] Thomas, J. J. and Cook, K. A. (2005) Illuminating the Path: The Research and Development Agenda for Visual Analytics, IEEE, Los Alametos, CA.

[20] Tomaszewski, Brian M. ;Robinson, Anthony C. ;Stryker, Michael ;Maceachren, Alan M; ( in press ) GeoVisual Analytics and Crisis Management, 4th International Information Systems for Crisis Response and Management (ISCRAM) Conference ,B. Van de Walle, P. Burghardt and C. Nieuwenhuis , Delft, Netherlands, 2008.

[21] Tufte, Edward R. “The Visual Display of Quantitative Information”, 2nd Edition, Cheshire, CT: Graphics Press. ISBN 0961392142. [1983] (2001)

[22] Yahoo’s geo-coding API http://developer.yahoo.com/maps/rest/V1/geocode.ht ml