Comparing the Spatial Querying Capacity of Large Language Models: OpenAI’s ChatGPT and Google’s Gemini Pro

Since the launch of ChatGPT in 2022 and Gemini in 2023, there has been growing interest in the potential application of generative artificial intelligence (AI) in geography and GIScience. As the need for geospatially capable generative AI tools increases, an empirical investigation of generative AI tools' performance in spatial querying is urgently needed. To fill this gap, we conducted experiments to assess ChatGPT and Gemini regarding their ability to generate accurate answers to spatial queries. The results reveal that ChatGPT and Gemini answered spatial queries to identify neighboring counties as defined by two methods for defining the neighboring relationship between geographical methods (queen contiguity and K-5 nearest neighbors) with accuracies ranging between 49 percent (K-5 with Gemini Pro) and 79 percent (queen with GPT-4). Specifically, GPT-4 outperforms GPT-3.5 and Gemini Pro, and queen contiguity queries yield more accurate answers than K-5 queries. Furthermore, our results show the potential sociodemographic and geographic biases in responses from both ChatGPT and Gemini. In general, the AI models retrieved more accurate answers for counties with larger proportions of urbanized areas and inland counties than their counterparts. Based on these findings, we discuss potential implications for geographers, GIScience researchers, and AI developers.