GeoLinter: A Linting Framework for Choropleth Maps

Fan Lei, Arlen Fan, Alan M. Maceachren, Ross Maciejewski

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1592-1607
Number of pages16
JournalIEEE Transactions on Visualization and Computer Graphics
Volume30
Issue number2
DOIs
StatePublished - Feb 1 2024

Keywords

  • Choropleth maps
  • automated visualization design
  • visualization linting
  • visualization recommendation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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