Geospatial Experience-Oriented Notation (GEON): A Semantic Format for LLM-Native Spatial Intelligence

Abstract

Existing geospatial data formats such as GeoJSON, Well-Known Text (WKT), and CityGML, are optimised for geometric computation and rendering. While effective for Geographic Information Systems (GIS), these approaches present limitations when used with Large Language Models (LLMs). For example, coordinate arrays carry no inherent semantic meaning, spatial relationships require computational geometry to extract, and the human experience of place is usually absent. This manuscript introduces foundational work on Geospatial Experience-Oriented Notation (GEON), a text-based format that bridges machine-optimised geospatial data and human-readable spatial descriptors. GEON encodes identity, geometry, purpose, experiential qualities, spatial relationships, temporal patterns, and data provenance in a readable and structured syntax designed for human comprehension and LLM reasoning. This manuscript presents the initial specification, reference implementations in Python, Rust, and JavaScript, and an empirical evaluation demonstrating how GEON achieves 20\% fewer tokens than equivalent GeoJSON files, while encoding 31\% more semantic facts per token. This manuscript explores the implementation and how LLMs reason about place-making, urban design interventions, and spatial intelligence tasks that existing formats struggle to support.