{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:54:45Z","timestamp":1780620885900,"version":"3.54.1"},"reference-count":17,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T00:00:00Z","timestamp":1689465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The emergence and rapid advancement of large language models (LLMs), represented by OpenAI\u2019s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. We conducted a pilot study exploring making maps with ChatGPT, a popular artificial intelligence (AI) chatbot. Specifically, we tested designing thematic maps using given or public geospatial data, as well as creating mental maps purely using textual descriptions of geographic space. We conclude that ChatGPT provides a useful alternative solution for mapping given its unique advantages, such as lowering the barrier to producing maps, boosting the efficiency of massive map production, and understanding geographical space with its spatial thinking capability. However, mapping with ChatGPT still has limitations at the current stage, such as its unequal benefits for different users and dependence on user intervention for quality control.<\/jats:p>","DOI":"10.3390\/ijgi12070284","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:41:05Z","timestamp":1689554465000},"page":"284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Mapping with ChatGPT"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4618-1893","authenticated-orcid":false,"given":"Ran","family":"Tao","sequence":"first","affiliation":[{"name":"School of Geosciences, University of South Florida, Tampa, FL 33647, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9663-5737","authenticated-orcid":false,"given":"Jinwen","family":"Xu","sequence":"additional","affiliation":[{"name":"GIS Center, Florida International University, Miami, FL 33199, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,16]]},"reference":[{"key":"ref_1","unstructured":"Meynen, E. 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The Ethics of AI-Generated Maps: A Study of DALLE 2 and Implications for Cartography. arXiv."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/7\/284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:51Z","timestamp":1760127171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/7\/284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,16]]},"references-count":17,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["ijgi12070284"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12070284","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,16]]}}}