Archaeological heritage ‘on-the-rise’: detecting emerging tourist attractions in Peru using machine learning and geotagged photographs

Abstract

ABSTRACT Unlike large cities and urban heritage attractions, archaeological heritage sites in peripheral locations cannot readily absorb the impact of over-tourism. Peru’s rapid growth as a tourist destination since the late 1990s has been bolstered by the popularity of its archaeological heritage sites and past civilizations, particularly in the Cuzco region. As mass tourism and issues of overcrowding have confronted visitors and heritage managers in the Cuzco region, tourists have sought alternative archaeological attractions beyond Cuzco’s ticketed sites – many of which are open-access and unmonitored. By integrating large-scale internet photo datasets from Flickr and the power of computer vision and machine learning algorithms, this study identifies ‘on-the-rise’ (i.e. emerging) archaeological heritage attractions accessible from five Peruvian cities. The major contributions of this paper are (1) to provide a novel method capable of being scaled globally to identify emerging archaeological tourist attractions that are unmonitored by ticket sales, (2) to identify ‘on-the-rise’ archaeological attractions in Peru, and (3) to mitigate future risk (e.g. over-tourism, pollution, destruction and damage) at identified sites as tourist itineraries continue to expand. The identification of emerging archaeological heritage attractions is significant for targeted and sustainable heritage planning, archaeological conservation, future destination marketing and tourism development.