EXPLICITLY REPRESENTING GEOGRAPHIC CHANGE IN MAP ANIMATIONS WITH BIVARIATE SYMBOLIZATION
Open Access
- Author:
- Auer, M. Thomas A.
- Graduate Program:
- Geography
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- None
- Committee Members:
- Alan Maceachren, Thesis Advisor/Co-Advisor
Alan Maceachren, Thesis Advisor/Co-Advisor - Keywords:
- task typology
selective attention theory
animated maps
bivariate symbolization
representing change
domain analysis
cartographic experiment - Abstract:
- 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 animation.