Learning Patterns of Tourist Movement and Photography from Geotagged Photos at Archaeological Heritage Sites in Cuzco, Peru

TL;DR

This study uses geotagged tourist photos and machine learning to analyze travel patterns and visual culture at archaeological sites in Cuzco, Peru, revealing how social media influences heritage tourism and aesthetic preferences.

Contribution

It introduces a novel approach combining geotagged photos and AI to quantify tourist movement and visual experiences at heritage sites, expanding heritage tourism analysis.

Findings

Identified distinct travel patterns across Cuzco's archaeological circuit.

Quantified the influence of social media on tourist behavior and aesthetic choices.

Demonstrated the potential of open-source geotagged data for heritage studies.

Abstract

The popularity of media sharing platforms in recent decades has provided an abundance of open source data that remains underutilized by heritage scholars. By pairing geotagged internet photographs with machine learning and computer vision algorithms, we build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit, and quantify visual culture and experiences in Cuzco, Peru. Leveraging large-scale in-the-wild tourist photos, our goals are to (1) understand how the intensification of tourism intersects with heritage regulations and social media, aiding in the articulation of travel patterns across Cuzco's heritage landscape; and to (2) assess how aesthetic preferences and visuality become entangled with the rapidly evolving expectations of tourists, whose travel narratives are curated on social media and grounded in historic site representations.