MapColorAI: designing contextually relevant choropleth map color schemes using a large language model

Choropleth maps are fundamental tools for geographic data analysis, primarily relying on color to convey information. Consequently, the design of their color schemes is important in choropleth map production. However, the traditional coloring methods offered by GIS tools such as ArcGIS and QGIS are not user-friendly enough for nonprofessionals. These tools provide numerous color schemes, making selection difficult, and cannot also easily fulfill personalized coloring needs, such as requests for “summer-like” map colors. To address these shortcomings, we develop a novel system that leverages a large language model and map color design principles to generate contextually relevant and user-aligned choropleth map color schemes. The system follows a three-stage process: Data processing, which provides an overview and classification of the data; Color Concept Design, where color theme and mode are conceptualized based on data characteristics and user intentions; and Color Scheme Design, where specific colors are assigned to classes. Our system incorporates an interactive interface for choropleth map color design and allows users to customize color choices flexibly. Through user studies and evaluations, the system demonstrates acceptable usability, accuracy, and flexibility, with users highlighting its efficiency and ease of use.