Graduate Student Profiles
Tom Auer (mta138 [at] psu.edu) graduated this summer with his Master's Degree in Geography, having worked under Dr. Alan M. MacEachren. Auer's thesis sought to understand whether explicitly symbolizing time-series change in map animations would help users recognize patterns in those animations, showing that for animated map reading tasks explicitly about change, symbolizations encoding change were most successful. As a corollary, Auer developed a task typology on movement patterns found in aggregated point data. Working as a research assistant, Auer helped develop a web-map, CalFloraViz, which allows the quick and easy spatiotemporal exploration of a large California plant sample collection.
Tom's Thesis Work
Explicitly Representing Geographic Change in Map Animations with Bivariate Symbolization
Animated maps provide an intuitive method for representing univariate time-series data, but often fail in presenting additional relevant information saliently, making recognition of certain patterns difficult. Using a second visual variable in animations to represent the magnitude of change between time states has been suggested as an effective method for enabling users to more easily recognize patterns of change in a geographic time-series. This work seeks to answer the question: Does explicitly representing geographic change in animated maps enable users to answer questions about patterns of change easily? To address this research question, bivariate symbols (with both the value of the data and the magnitude of change between time frames represented) were created and tested. Selective attention theory (SAT) was used in selecting bivariate symbol types (separable and integral). Domain analysis with experts from the Avian Knowledge Network (AKN) was performed to determine appropriate map reading tasks for use in task-based experiments using AKN data. Combined with existing task typologies, material from the domain analysis helped form a new task typology of movement patterns found in aggregated spatiotemporal point data. Formal task-based experiments followed, where participants were placed into one of five experiment groups (each using a different symbol) and asked to perform the same series of statement agreement and certainty ratings while studying map animations. Results show that aside from questions explicitly about change, univariate non-change symbolization may be most appropriate. Future studies should focus on testing different data relationships (independent, interdependent, or unrelated) with symbol variations that may have different attention behaviors as predicted by SAT. The results presented here improve the understanding of whether explicit change symbolization helps elucidate geographic time-series patterns or hinders the overall effectiveness of map animations.
More about Tom's work on CalFloraViz:
CalFloraViz: A web-based client-server interface for mapping California flora observation data
The duties of my research assistantship were to create, from scratch, a web-map that would give users the ability to explore, spatially and temporally, the geo-referenced plant samples in a dataset provided by the Consortium of California Herbaria. However, at the start, I had almost no programming skills, learning ActionScript 3 for Adobe Flash from the ground up. By the end of my first summer and with help from Scott Pezanowski and Craig McCabe, we had created a functional, yet simple, interface for generating graduated circle maps based on species selections and temporal filtering. Having developed considerably from its first form, CalFloraViz has become a strong map tool that allows easy query and display of the spatial history of a large plant sample collection. The underlying database, housed on GeoVISTA servers, contains over 377,000 unique, spatially referenced samples collected in California between 1860 and 2007. Handling so many records with client-server interactions over the internet can be slow and cumbersome (both practically and visually). Our solution was to aggregate sample point locations to relevant regions (as polygons); in this case, Jepson sub-ecoregions. Performing database pre-aggregation on the server-side made responses to client requests for data much faster. Interface design focused on having the user first work through the phylogenetic hierarchy to select a family, genus or species and apply temporal filtering, generating maps featuring Coxcombs (or Polar Area Charts) that bin data by months. This way, users can explore seasonal patterns in the collection. Another key feature provides web links to plant information on Wikipedia or on the California Plant Names Index. Using the Google Map API allows users to zoom in and generate maps with individual plant sample records.