Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity

TL;DR

This study uses big data from bank transactions, geotagged photos, and tweets to analyze how city attractiveness for foreign visitors scales superlinearly with city population in Spain, revealing consistent patterns across data sources and seasons.

Contribution

It demonstrates a robust superlinear scaling law of city attractiveness with population using diverse digital data sources, highlighting the potential of big data in urban analysis.

Findings

City attractiveness scales superlinearly with population.

Scaling exponent remains consistent across data sources.

Seasonal variations in attractiveness are observed.

Abstract

Scientific studies investigating laws and regularities of human behavior are nowadays increasingly relying on the wealth of widely available digital information produced by human social activity. In this paper we leverage big data created by three different aspects of human activity (i.e., bank card transactions, geotagged photographs and tweets) in Spain for quantifying city attractiveness for the foreign visitors. An important finding of this papers is a strong superlinear scaling of city attractiveness with its population size. The observed scaling exponent stays nearly the same for different ways of defining cities and for different data sources, emphasizing the robustness of our finding. Temporal variation of the scaling exponent is also considered in order to reveal seasonal patterns in the attractiveness