Tourists' digital footprint in cities: comparing big data sources
Maria Henar Salas-Olmedo, Juan Carlos Garcia-Palomares, Javier, Gutierrez

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…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Urban Transport and Accessibility · Diverse Aspects of Tourism Research
