TY - GEN
T1 - Text-Color Hybrid Labeling for Multiclass Map Visualization
T2 - 18th International Symposium on Visual Information Communication and Interaction, VINCI 2025
AU - Chen, Xinyao
AU - Zhang, Xinyuan
AU - Ma, Teng
AU - Yu, Lingyun
AU - Liu, Yu
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/12/18
Y1 - 2025/12/18
N2 - Prior work has identified the shortcomings of color-only encodings for maps with many categories, yet systematic comparisons of hybrid text-color strategies remain scarce. We therefore ran an 80-participant crowdsourced study on choropleth maps with 8-13 categories - approaching the 10-hue perceptual limit - to compare four annotation designs (Legend-Aside, Label-Fill, Label-Fit, Colored Label-Fill) across Count, Identify, Compare, and Rank tasks. Results show that the Label-Fit Map - with a single, large in-situ label - yields the highest accuracy and speed and ranks first in readability; Legend-Aside excels in simple counting and side-by-side comparisons. These findings deliver clear, task-specific guidelines for enhancing multiclass map readability and efficiency, informing the design of more effective map visualizations. All supplementary materials are available at our GitHub repository.
AB - Prior work has identified the shortcomings of color-only encodings for maps with many categories, yet systematic comparisons of hybrid text-color strategies remain scarce. We therefore ran an 80-participant crowdsourced study on choropleth maps with 8-13 categories - approaching the 10-hue perceptual limit - to compare four annotation designs (Legend-Aside, Label-Fill, Label-Fit, Colored Label-Fill) across Count, Identify, Compare, and Rank tasks. Results show that the Label-Fit Map - with a single, large in-situ label - yields the highest accuracy and speed and ranks first in readability; Legend-Aside excels in simple counting and side-by-side comparisons. These findings deliver clear, task-specific guidelines for enhancing multiclass map readability and efficiency, informing the design of more effective map visualizations. All supplementary materials are available at our GitHub repository.
KW - Crowd-source Experiment
KW - Geographical Data
KW - Map Visualization
UR - https://www.scopus.com/pages/publications/105026263865
U2 - 10.1145/3769534.3769610
DO - 10.1145/3769534.3769610
M3 - Conference Proceeding
AN - SCOPUS:105026263865
T3 - 18th International Symposium on Visual Information Communication and Interaction, VINCI 2025
BT - 18th International Symposium on Visual Information Communication and Interaction, VINCI 2025
A2 - Wallner, Gunter
A2 - She, James
A2 - Burch, Michael
A2 - Liang, Hai-Ning
PB - Association for Computing Machinery, Inc
Y2 - 1 December 2025 through 3 December 2025
ER -