Territorial Algorithmic Invisibility: A Diagnostic Framework for Generative Engine Optimization Applied to Local Tourism Destination Entities

The emergence of Large Language Models (LLMs) as primary mediators of travel information retrieval presents a distinct challenge for local tourism destinations: algorithmic invisibility induced not by poor content production, but by geographic proximity to dominant destinations that systematically absorb a smaller entity's algorithmic identity. This paper introduces the concept of Territorial Algorithmic Invisibility (TAI) and proposes the Destination Algorithmic Readiness Score (DARS), a five-dimensional diagnostic framework for measuring and addressing GEO deficiencies in Destination Marketing Organizations (DMOs). The framework is derived from a structured diagnostic audit of Neltur, the official tourism company of Niterói, Rio de Janeiro-a destination of demonstrable cultural and geographic significance that exists in the algorithmic shadow of its neighboring metropolis. Systematic querying of ChatGPT, Google Gemini, and Perplexity AI revealed that Niterói attractions are consistently attributed to Rio de Janeiro or omitted entirely from AI-generated travel recommendations, despite housing internationally recognized architectural landmarks including Oscar Niemeyer's Museu de Arte Contemporânea (MAC). The DARS baseline established for Niterói (38/100) quantifies the gap between the destination's real-world tourism value and its current algorithmic representation. The paper contributes to the nascent GEO literature by extending existing frameworks beyond brand and professional entities to geographic destinations, formalizing the Proximity Shadow Effect as a structural impediment to local DMO visibility, and proposing a reproducible diagnostic methodology applicable to tourism entities globally.