Creating Choropleth Maps by Artificial Intelligence—Case Study on ChatGPT-4
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73634076" target="_blank" >RIV/61989592:15310/25:73634076 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2220-9964/14/12/486" target="_blank" >https://www.mdpi.com/2220-9964/14/12/486</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/ijgi14120486" target="_blank" >10.3390/ijgi14120486</a>
Alternative languages
Result language
angličtina
Original language name
Creating Choropleth Maps by Artificial Intelligence—Case Study on ChatGPT-4
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ISPRS International Journal of Geo-Information
ISSN
2220-9964
e-ISSN
—
Volume of the periodical
14
Issue of the periodical within the volume
12
Country of publishing house
CH - SWITZERLAND
Number of pages
30
Pages from-to
"486-1"-"486-30"
UT code for WoS article
001646748100001
EID of the result in the Scopus database
2-s2.0-105025956976