Tourists' digital footprint in cities: comparing big data sources

TL;DR

This study compares three different Big Data sources to analyze urban tourists' spatial behavior, revealing that combining multiple data types provides a more comprehensive understanding of tourist activity patterns in cities.

Contribution

It introduces a multi-source approach to analyze tourists' spatial footprints, demonstrating the complementary nature of different Big Data sources for urban tourism analysis.

Findings

Tourist activities are partly redundant and partly complementary across data sources.

Tourist density is highest in city centers and shows increasing specialization outward.

Multiple data sources are necessary for comprehensive urban tourism analysis.

Abstract

There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data (Big Data) when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected). Tourist density in the three data sources is compared via maps, correlation analysis (OLS) and spatial self-correlation analysis (Global Moran's I statistic and LISA). Finally the data are integrated using cluster analysis and combining the spatial clusters identified in the LISA analysis in the different data sources. The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces (with several activities) and spaces specialising in one or various activities (for example, sightseeing and consumption). The case study analysed (Madrid) reveals a significant presence of tourists in the city centre, and increasing specialisation from the centre outwards towards the periphery. The main conclusion of the paper is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.