Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2403.08260
Cited By

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

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

International Conference on Human Factors in Computing Systems (CHI), 2024

13 March 2024

Ross Maciejewski

ArXiv (abs)PDF HTML Github

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

5 / 5 papers shown

MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering

MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering

Varun Srivastava

Srija Mukhopadhyay

Ross Maciejewski

245

1

0

15 Jul 2025

A Survey on Annotations in Information Visualization: Empirical
Insights, Applications, and Challenges

A Survey on Annotations in Information Visualization: Empirical Insights, Applications, and Challenges

Md Dilshadur Rahman

Bhavana Doppalapudi

Ghulam Jilani Quadri

348

6

0

08 Oct 2024

MAPWise: Evaluating Vision-Language Models for Advanced Map Queries

MAPWise: Evaluating Vision-Language Models for Advanced Map Queries

Srija Mukhopadhyay

Abhishek Rajgaria

Prerana Khatiwada

Vivek Gupta

Dan Roth

201

0

0

30 Aug 2024

DASH: A Bimodal Data Exploration Tool for Interactive Text and
Visualizations

DASH: A Bimodal Data Exploration Tool for Interactive Text and VisualizationsVisual .. (VISUAL), 2024

251

6

0

02 Aug 2024

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy
and Design Space

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design SpaceIEEE Transactions on Visualization and Computer Graphics (TVCG), 2023

Md Dilshadur Rahman

Ghulam Jilani Quadri

Bhavana Doppalapudi

236

21

0

09 Jun 2023

Page 1 of 1