Towards AI-Assisted Mapmaking: Assessing the Capabilities of GPT-4o in Cartographic Design

Cartographic design is fundamental to effective mapmaking, requiring adherence to principles such as visual hierarchy, symbolization, and color theory to convey spatial information accurately and intuitively, while Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed various fields, their application in cartographic design remains underexplored. This study assesses the capabilities of a multimodal advanced LLM, GPT-4o, in understanding and suggesting cartographic design elements, focusing on adherence to established cartographic principles. Two assessments were conducted: a text-to-text evaluation and an image-to-text evaluation. In the text-to-text assessment, GPT-4o was presented with 15 queries derived from key concepts in cartography, covering classification, symbolization, visual hierarchy, color theory, and typography. Each query was posed multiple times under different temperature settings to evaluate consistency and variability. In the image-to-text evaluation, GPT-4o analyzed maps containing deliberate cartographic errors to assess its ability to identify issues and suggest improvements. The results indicate that GPT-4o demonstrates general reliability in text-based tasks, with variability influenced by temperature settings. The model showed proficiency in classification and symbolization tasks but occasionally deviated from theoretical expectations. In visual hierarchy and layout, the model performed consistently, suggesting appropriate design choices. In the image-to-text assessment, GPT-4o effectively identified critical design flaws such as inappropriate color schemes, poor contrast and misuse of shape and size variables, offering actionable suggestions for improvement. However, limitations include dependency on input quality and challenges in interpreting nuanced spatial relationships. The study concludes that LLMs like GPT-4o have significant potential in cartographic design, particularly for tasks involving creative exploration and routine design support. Their ability to critique and generate cartographic elements positions them as valuable tools for enhancing human expertise. Further research is recommended to enhance their spatial reasoning capabilities and expand their use of visual variables beyond color, thereby improving their applicability in professional cartographic workflows.