Sublinear scaling of country attractiveness observed from Flickr dataset

TL;DR

This study analyzes how country attractiveness, measured via Flickr photo-sharing data, scales with population and size, revealing sublinear scaling with population and no correlation with size, contrasting city-level patterns.

Contribution

It provides the first analysis of country-level attractiveness scaling using real-world social media data, highlighting differences from city-level scaling behaviors.

Findings

Country attractiveness scales sublinearly with population.

No significant correlation between attractiveness and country size.

Contrasts city-level superlinear scaling observed in other studies.

Abstract

The number of people who decide to share their photographs publicly increases every day, consequently making available new almost real-time insights of human behavior while traveling. Rather than having this statistic once a month or yearly, urban planners and touristic workers now can make decisions almost simultaneously with the emergence of new events. Moreover, these datasets can be used not only to compare how popular different touristic places are, but also predict how popular they should be taking into an account their characteristics. In this paper we investigate how country attractiveness scales with its population and size using number of foreign users taking photographs, which is observed from Flickr dataset, as a proxy for attractiveness. The results showed two things: to a certain extent country attractiveness scales with population, but does not with its size; and unlike in case of Spanish cities, country attractiveness scales sublinearly with population, and not superlinearly.