IEEE VIS 2024 Content: GeoLinter: A Linting Framework for Choropleth Maps

GeoLinter: A Linting Framework for Choropleth Maps

Fan Lei -

Arlen Fan -

Alan M. MacEachren -

Ross Maciejewski -

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DOI: 10.1109/TVCG.2023.3322372

Room: Bayshore II

2024-10-16T17:45:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T17:45:00Z
Exemplar figure, described by caption below
The GeoLinter Interface: (A) the VegaLite code editor; (B) the original map; (C) the map after applying soft fixes; (D) classification recommendations; (E) detected violations with guides on map improvements, and; (F) the status panel. A choropleth map showing the value per capita of freight shipments in the U.S. by state 2002. In the original choropleth map design (B), the data classification accuracy is lower than the average value; the colors between bins are nearly indistinguishable; the map data has not been normalized and the data units are missing. After applying the suggested fixes from GeoLinter, the designer produces (C).
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Keywords

Data visualization , Image color analysis , Geology , Recommender systems , Guidelines , Bars , Visualization Author Keywords: Automated visualization design , choropleth maps , visualization linting , visualization recommendation

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.