  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.
