Understanding Reader Takeaways in Thematic Maps Under Varying Text, Detail, and Spatial Autocorrelation

Abstract

Maps are crucial in conveying geospatial data in diverse contexts such as news and scientific reports. This research, utilizing thematic maps, probes deeper into the underexplored intersection of text framing and map types in influencing map interpretation. In this work, we conducted experiments to evaluate how textual detail and semantic content variations affect the quality of insights derived from map examination. We also explored the influence of explanatory annotations across different map types (e.g., choropleth, hexbin, isarithmic), base map details, and changing levels of spatial autocorrelation in the data. From two online experiments with $N=103$ participants, we found that annotations, their specific attributes, and map type used to present the data significantly shape the quality of takeaways. Notably, we found that the effectiveness of annotations hinges on their contextual integration. These findings offer valuable guidance to the visualization community for crafting impactful thematic geospatial representations.

Authors
Arlen Fan
Arizona State University, Tempe, Arizona, United States
Fan Lei
Arizona State University, Tempe, Arizona, United States
Michelle Mancenido
Arizona State Unversity, Tempe, Arizona, United States
Alan MacEachren
Pennsylvania State University, University Park, Pennsylvania, United States
Ross Maciejewski
Arizona State University, Tempe, Arizona, United States
Paper URL

https://doi.org/10.1145/3613904.3642132

Video

Conference: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

Session: Data Visualization: Geospatial and Multimodal

324
5 items in this session
2024-05-15 09:00:00
2024-05-15 10:20:00