The Impact of Bivariate Symbol Design on Task Performance in a Map Setting
Bibliographic Data
| ID | 14701927 |
|---|---|
| Authors | Elisabeth S Nelson (0009-0009-2290-1604, University of North Carolina at Greensboro, corresponding author), Elisabeth Nelson (Department of Geography, University of North Carolina, Greensboro, USA) |
| Year | 2000 |
| Volume | 37 |
| Issue | 4 |
| Pages | 61-78 |
| Publication date | 2000-12-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartographica The International Journal for Geographic Information and Geovisualization (JOURNAL) |
| Journal identifiers | ISSN: 0317-7173 • E-ISSN: 1911-9925 |
| Publisher | University of Toronto Press Inc. (UTPress) (PUBLISHER) |
| DOI | 10.3138/v743-k505-5510-66q5 |
| OpenAlex | W2161468427 |
| Language | EN |
| Citations received | 5 |
| References cited | 22 |
Research conducted on the theory of selective attention suggests that varying the graphic combinations used when designing bivariate symbols affects the functionality of the symbol. Some graphic combinations appear to facilitate the ability to visualize correlation between the data sets represented by the symbol; others appear to be more effective at representing the data sets individually, some even at the expense of extracting correlational information. The purpose of the research described here was to test the strength of these findings in a map use context. Several bivariate symbol designs were tested using map use tasks designed to test participants' abilities to extract either correlational or individual information. Participant reaction times provided an assessment of the types and levels of interactions that occurred with each symbol set. Results corroborate previous research in both cartography and psychology, with several symbol designs falling into each of three interactional categories: separable, integral, and configural. By confirming and expanding previous research, this study provides further evidence of the strength of selective attention theory in aiding the design of bivariate thematic maps
Bivariate analysis · Cognitive psychology · Context (archaeology · Machine learning · Set (abstract data type · Symbol (formal · Task (project management · Categorization, perception, and language · Computer Science · Data Visualization and Analytics · Engineering · Psychology · Spatial Cognition and Navigation · Artificial Intelligence
| Citations per year | 0.26 |
|---|---|
| Citation span | 2007 - 2026 (20) |
| Highly cited | No |