GeoLinter: A Linting Framework for Choropleth Maps
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Abstract (arXiv)
Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2023.
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?cited by 27
- 2025MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Modelcited by 5
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inferencecited by 3
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference21 Dec 2025
- 2025MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model22 Jan 2025
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?24 Jul 2024