RegionDefiner: Data, Documents, and Code

A conversational agent authored by Xin Feng and Yuanpei Cao.

Relevant paper: “Enhancing the accessibility of regionalization techniques through large language models: A case study in conversational agent guidance.” International Journal of Geographical Information Science. (Accepted in Oct. 2024).

Abstract:
The concept of regions has long been crucial for understanding and managing Earth's phenomena, leading to regionalization, aggregating smaller areas into larger, contiguous, and homogeneous regions to achieve specific goals. Open-source regionalization is gaining traction because it reduces dependence on commercial software and fosters wider adoption in analysis and decision-making. However, these packages, often designed by experts for specialized tasks, can be challenging to understand and utilize due to domain-specific jargon and functionalities, especially for unfamiliar users. A prevalent disconnect must be addressed: How can we make a complex optimization approach available to a broad audience with various backgrounds? This study introduces RegionDefiner, a Large Language Modeling (LLM)-powered conversational agent, to comprehensively understand the functionality, inputs, outputs, and potential applications of regionalization problems. We selected it as an illustrative example due to its wide-ranging potential for delineating study regions in various applications. RegionDefiner is designed to guide users in framing their problems, collecting necessary data, and implementing solution approaches in a straightforward and user-centric manner. The experiments demonstrate that RegionDefiner interprets and presents the results in an understandable way for all audiences, thus bridging the gap between intricate computations and practical problem-solving needs.