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Full-Text Articles in Human Geography
Towards Understanding The Geospatial Skills Of Chatgpt: Taking A Geographic Information Systems (Gis) Exam, Peter Mooney, Wencong Cui, Boyuan Guan, Levente Juhasz
Towards Understanding The Geospatial Skills Of Chatgpt: Taking A Geographic Information Systems (Gis) Exam, Peter Mooney, Wencong Cui, Boyuan Guan, Levente Juhasz
GIS Center
This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their capabilities and limitations in specialized subject areas such as GIS. Human learning of spatial concepts significantly differs from LLM training methodologies. Therefore, this study aims to assess ChatGPT's performance and ability to grasp geospatial concepts by challenging it with a real GIS exam. By analyzing ChatGPT's responses and evaluating its understanding of GIS principles, we gain insights into the potential applications and challenges …
Using Self Organizing Maps To Analyze Demographics And Swing State Voting In The 2008 U.S. Presidential Election, Paul T. Pearson, Cameron I. Cooper
Using Self Organizing Maps To Analyze Demographics And Swing State Voting In The 2008 U.S. Presidential Election, Paul T. Pearson, Cameron I. Cooper
Faculty Publications
Emergent self-organizing maps (ESOMs) and k-means clustering are used to cluster counties in each of the states of Florida, Pennsylvania, and Ohio by demographic data from the 2010 United States census. The counties in these clusters are then analyzed for how they voted in the 2008 U.S. Presidential election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and k-means clusterings are compared and found to be dissimilar by the variation of information distance function.