Enhancing Accessibility of Spatial Optimization Techniques through Large Language Models: A Case Study in Autonomous Agent Guidance for Regionalization

Keywords: Large Language Models, Conversational Virtual Agent, Regionalization
Abstract Type: Paper Abstract

Abstract

The notion of regions has historically been essential for explaining and regulating Earth's phenomena, resulting in regionalization, which gathers smaller areas into bigger, contiguous, and homogenous regions to fulfill certain objectives. Open-source regionalization is becoming popular since it diminishes reliance on commercial software and promotes broader use in analysis and decision-making. Nonetheless, these packages might be difficult to comprehend and employ due to specialized terminology and functions, particularly for unfamiliar users. A major gap needs to be resolved: How can we make a difficult optimization methodology accessible to a diverse audience with diverse backgrounds? This paper presents a conversational agent driven by Large Language Modeling (LLM) designed to thoroughly comprehend the functioning, inputs, outputs, and possible applications of regionalization issues. We chose it as an instructive example because of its extensive possibilities for defining research zones across many applications. We intend to assist in articulating their challenges, gathering requisite data, and executing solution strategies in a clear and user-focused manner. The experiments illustrate that the proposed conventional agent understands and conveys findings comprehensibly for all audiences, bridging the gap between complex calculations and practical problem-solving tasks.