In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb, a service that puts travellers in contact with hosts for the purposes of renting accommodation, either rooms or entire homes/apartments. Although Airbnb may bring benefits to cities in that it increases tourist numbers, its concentration in certain areas of heritage cities can lead to serious conflict with the local population, as a result of rising rents and processes of gentrification. This article analyses the patterns of spatial distribution of Airbnb accommodation in Barcelona, one of Europe's major tourist cities, and compares them with the accommodation offered by hotels and the places most visited by tourists. The study makes use of new sources of geolocated Big Data, such as Airbnb listings and geolocated photographs on Panoramio. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between the accommodation offered by Airbnb and the one offered by hotels, with a marked centre-periphery pattern, although Airbnb predominates over hotels around the city's main hotel axis and hotels predominate over Airbnb in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the city's main tourist attractions than does the hotel sector. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb's recent expansion.

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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.

Access coverage is important in public transit planning, as this is the means by which service is provided to riders. In fact the proximity of demand (population and employment) to stops or stations on the network to a great extent explains its greater or lesser usage by potential users. Coverage of service areas can be delineated by GIS through the creation of buffers around transit facilities based on Euclidean (straight-line) distance. A second method is based on calculations of distances along a street network (network distance). The choice of the distance calculation method affects significantly the final results in terms of population covered. This paper assesses the overestimation of the straight-line-distance method, which is the most widely used in coverage analysis, by comparing it with that of network distances. It investigates systematically the factors influencing this overestimation, such as the density of stops or stations, the coverage distance thresholds and the characteristics of the area analysed (street-network design, barriers, and population distribution in the neighbourhood of the bus stop or station). Finally, it concludes that the network-distance method provides systematically better estimates of transit ridership than the Euclidean distance method.

In the last years, studies on the vulnerability of public transport networks attract a growing attention because of the repercussions that incidents can have on the day-to-day functioning of a city. The aim of this paper is to develop a methodology for measuring public transport network vulnerability taking the Madrid Metro system as an example. The consequences of a disruptions of riding times or the number of missed trips are analysed for each of the network links with a full scan approach implemented in GIS (Geographic Information Systems). Using real trips distribution, each link in the network is measured for criticality, from which the vulnerability of lines and stations can be calculated. The proposed methodology also makes it possible to analyse the role of circular lines in network vulnerability and to obtain a worst-case scenario for the successive disruption of links by simulating a targeted attack on the network. Results show the presence of critical links in the southern part of the network, where line density is low and ridership high. They also highlight the importance of the circular line as an element of network robustness.