Creating Choropleth Maps by Artificial Intelligence—Case Study on ChatGPT-4
Bibliographic Data
| ID | 22032678 |
|---|---|
| Authors | Parinda Pannoon (Palacký University Olomouc), Rostislav Nétek (0000-0002-3923-7676, Palacký University Olomouc) |
| Year | 2025 |
| Volume | 14 |
| Issue | 12 |
| Pages | 486 |
| Publication date | 2025-12-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14120486 |
| OpenAlex | W7110967384 |
| Language | EN |
| References cited | 15 |
This study explores the potential of ChatGPT-4, an AI-powered large language model, to generate thematic maps and compare its outputs to the traditional method in which maps are produced manually by humans using GIS software. Prompt engineering is a crucial methodology of large language models that can enhance output quality. The main objective of this study is to assess the capability of AI-generated maps and to compare the quality with a traditional method. The study evaluates two prompt patterns: basic (zero-shot prompts) and advanced (Cognitive Verifier and Question Refinement). The performance of AI-generated maps is assessed based on attempts, errors, incorrect results, and map completeness. The final stage involved evaluating AI-generated maps against cartographic rules to assess their suitability. ChatGPT-4 performs well in generating suitable choropleth maps but faced challenges in understanding the prompts and potential errors in the generated code. Advanced prompts reduced errors and improved the quality of outputs, particularly for complex map elements. This paper enhances the understanding of AI’s role in cartography and further research in automated cartography. The study assesses cartographic aspects, offering insights into the strengths and limitations of AI in cartography, illustrating how large language models can process geospatial data and adhere to cartographic principles. The study also paves the way for future innovations in automated geovisualization
Data quality · Geospatial analysis · Thematic map · Visualization · Geographic Information Systems Studies · Multimodal Machine Learning Applications · Spatial Cognition and Navigation
| Highly cited | No |
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