An Automated Framework for Natural Language-Based Spatial Query Using Large Language Models

Abstract

Automating spatial analysis remains challenging due to its dependence on predefined workflows, static schemas, and expert-driven configurations, which limit adaptability across diverse datasets and analytical contexts. Recent advances in large language models (LLMs) provide new opportunities to address these challenges by enabling systems to interpret human intent, reason over structured data, and generate executable analytical workflows. This study presents an automated Spatial Query and Analysis (SQA) framework that leverages LLMs to translate natural language queries into validated spatial operations. The framework interprets user prompts and classifies them as either general or spatial q..