Identifying Tourist Dispersion in Austria by Digital Footprints

Koerbitz, Wolfgang; Önder, Irem; Hubmann-Haidvogel, Alexander

doi:10.1007/978-3-642-36309-2_42

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…Photo-sharing services have been used for several purposes in the field of tourism, including identifying social events such as festivals, demonstrations, sporting events and so on (Sun and Fan, 2014), estimating tourist numbers (Koerbitz et al, 2013), identifying the presence of tourists (Girardin et al, 2008a, Kisilevich et al, 2013Straumann et al, 2014) and the most common trajectories followed by tourists (Girardin et al, 2008b), proposing or assessing tourist routes (Kurashima et al, 2013), suggesting tourist trips (Lu et al, 2010) and planning trips lasting several days and places to visit (Li, 2013). These data sources make it possible to identify areas with a concentration of tourists in cities (sightseeing spots) through spatial statistical techniques (García-Palomares et al, 2015).…”

Section: Sightseeing: Photo-sharing Servicesmentioning

confidence: 99%

Tourists' digital footprint in cities: Comparing Big Data sources

et al. 2018

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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.

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Section: Sightseeing: Photo-sharing Servicesmentioning

confidence: 99%

Tourists' digital footprint in cities: Comparing Big Data sources

et al. 2018

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“…Since the ICT and social media revolution, tourists leave more and more digital footprints on different UGC platforms (Litvin et al, 2008;Munar & Jacobsen, 2014). Next to the fast-growing field of studies analysing topics and trends in UGC (O'Connor, 2008;Wang, Kirillova, & Lehto, 2017), analysing the digital footprint geographically can also give an indication on how tourists perceive, experience and use the destination (Ganzaroli et al, 2017;García-Palomares, Gutiérrez, & Mínguez, 2015;Girardin, Calabrese, Dal Fiore, Ratti, & Blat, 2008;Kádár, 2014;Koerbitz, Önder, & Hubmann-Haidvogel, 2013) and which experiences they communicate to their peers (Leung, Vu, Rong, & Miao, 2016).…”

Section: Theoretical Framework: Tourist Behaviour In Historic Cities mentioning

confidence: 99%

“…However, there is at least some indication that UGC provides reliable data for tourism research. For instance, Koerbitz et al (2013) found that by analysing UGC, in this case photos uploaded on the platform Flickr, the length of stay of tourists could be extracted correctly. Tilly, Fuschbach, and Schoder (2015) showed that tourist reviews on an undisclosed virtual tourism community correlated strongly with United Nations World Tourism Organization statistics on tourism per country, with the strongest relationship being found between overall tourism, both domestic and international, and the number of reviews on the UGC platform.…”

Section: Studying Tourists' Spatial Behaviour Through Ugcmentioning

confidence: 99%

“…Active users of UGC, being the providers of peer-to-peer information, are important when studying tourist behaviour through UGC (Kádár, 2014;Koerbitz et al, 2013). These tourists can be tracked through the information they leave behind deliberately, forming their explicit digital footprints (Fuchs et al, 2014;Girardin et al, 2008).…”

Section: Studying Tourists' Spatial Behaviour Through Ugcmentioning

confidence: 99%

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Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management

Zee

Bertocchi

Vanneste

2018

Current Issues in Tourism

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The emergence of social media and Web 2.0 has a notable impact upon the tasks of destination managers as these platforms have developed into influential mechanisms affecting tourist behaviour. This paper shows how Destination Management Organizations (DMOs) can reap the benefits of the Web 2.0 revolution as it serves as an important source of user-generated information, bringing novel opportunities for data-driven destination management. To test the applicability of user-generated content for destination management, this paper analyses restaurant reviews from five Flemish art cities which were retrieved from the Web 2.0 platform TripAdvisor. Getis-Ord hot spot analysis revealed spatial clusters of frequently ('hot spots') and rarely ('cold spots') reviewed restaurants in four out of the five art cities. By comparing these spatial patterns, the digital footprints of tourists were uncovered and discussed with DMO directors. Found patterns appeared to reflect local policies aimed either at concentrating tourism, as in Bruges, the city with the most prominent hot spot, or spreading tourism over time and space as seen in Antwerp and Ghent where less prominent hot spots were present. The visualization proved to be a valuable input when discussing tourism management and fuelled the sharing of knowledge between the destinations.

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“…Other traditional methods include data from accommodation providers and guest surveys, which are time consuming and expensive. Data can also be taken from official guides, but these fail to adapt to the rapidly-changing tastes of tourists and their actual preferences [29]. Today, everyone leaves digital footprints on the internet, which can be used as data what is essential of smart tourism.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Data on Bike-Sharing System User Stopovers in Smart Tourism: A Case Study

Banet¹

2021

Komunikácie

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Bike-sharing systems are an important element in development of the smart cities and datasets from these systems are one of the ways to obtain large amount of information on bicycle traffic. These usually contain data on the origin and destination of each trip, as well as its time and duration. Alongside the basic data, some operators also provide information on the exact route picked by each user. This allows researchers to study stopovers, which may serve as a source of interesting information on human behaviour in public spaces and, as a consequence, help improve its analysis and design. However, using the raw data may lead to important errors because most stops occur in the vicinity of bike stations or are related to traffic problems, as evidenced by the case study of Cracow. The data filtering method proposed below opens up the possibility for using such datasets for further research on bike user behaviour and public spaces.

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Identifying Tourist Dispersion in Austria by Digital Footprints

Cited by 13 publications

References 8 publications

Tourists' digital footprint in cities: Comparing Big Data sources

Tourists' digital footprint in cities: Comparing Big Data sources

Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management

Using Data on Bike-Sharing System User Stopovers in Smart Tourism: A Case Study

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