Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources

Silva, Filipe Batista e; Herrera, Mario A. Marin; Rosina, Konštantín; Barranco, Ricardo; Freire, Sérgio; Schiavina, Marcello

doi:10.1016/j.tourman.2018.02.020

Cited by 147 publications

(102 citation statements)

References 17 publications

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“…Chua et al (2016) use information from the geotags of the Twitter social network to describe the flows of tourism in Cilento, with the result being that photos geo-tagged by tourists have a greater tendency to concentrate than those shared by residents of the area. The photos shared by the tourists themselves through different social media have also served as the basis for various studies that have contributed to generating information on the distribution of tourism demand at different destinations at a European level (García-Palomares et al 2015;Gutiérrez et al 2016;Batista e Silva et al 2018).…”

Section: The Importance Of Space In Accommodation Distributionmentioning

confidence: 99%

“…However, if there is a field in which efforts have been focused on creating knowledge on tourist activities and their distribution in a space, there is no doubt that this is the distribution of the tourist offering in the territory. To do this, some studies have focused on analyzing how accommodation companies are distributed in space, using the entity itself or the places offered by each of the existing establishments as a reference base (Martín et al 2018;Yang andWong 2012, 2013;Majewska 2015Majewska , 2017Li et al 2015;Xing-Zhu and Qun 2014;Almeida-García et al 2018;Balaguer and Pernías 2013;Sánchez Martín et al 2013;Sánchez-Rivero 2019, 2020;Batista e Silva et al 2018;Sánchez-Martín et al 2019;Sánchez-Rivero 2008;Sarrión-Gavilán et al 2015). The ultimate purpose of these studies is to identify groupings of accommodation establishments in a space, i.e., the identification of spatial clusters.…”

Section: The Importance Of Space In Accommodation Distributionmentioning

confidence: 99%

“… Sánchez-Rivero 2019, 2020;Batista e Silva et al 2018;Sánchez-Martín et al 2019;Sánchez-Rivero 2008;Sarrión-Gavilán et al 2015).…”

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A Spatial Analysis of Intensity in Tourism Accommodation: An Application for Extremadura (Spain)

2020

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There is a general belief that the distribution of tourist activity in space does not respond to a random pattern, so having a thorough knowledge of said activity will require analyzing and understanding its distribution pattern. At the same time, the adequate planning of this sector requires exhaustive knowledge, on the one hand to be able to enhance the positive effects of concentrations in the space, and on the other hand to avoid those possible adverse effects. Therefore, various studies focus on providing information on the random pattern of these activities, especially in developing destinations with a great interest in generating a model of sustainable tourism development. This paper aims to contrast the results achieved by previous studies at the destination while describing the pattern identified through the use of alternative statistical techniques rather than those commonly used. In particular, an intensity function is estimated using three different methods: quadrant counting, K-function, and kernel smoothing. The results achieved allow the identification of the areas with the greatest tourist intensity while describing the practical implications of the results achieved.

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Section: The Importance Of Space In Accommodation Distributionmentioning

confidence: 99%

Section: The Importance Of Space In Accommodation Distributionmentioning

confidence: 99%

See 1 more Smart Citation

A Spatial Analysis of Intensity in Tourism Accommodation: An Application for Extremadura (Spain)

2020

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“…Silva et al, through ad-hoc routines for web data extraction over aggregations of point-based grids, used Booking and TripAdvisor big data in complement to Eurostat data to identify spatiotemporal patterns in hospitality in the whole of the European Union [2]. Martín et al used the Python-based Scrapy framework to extract user feedback (comments) from Booking and TripAdvisor and implemented the cleaned data within their sentiment estimators based on conventional neural networks and long short-term memory networks [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The selected data resources have been in the scope of recent open research focusing on tourism and destination management, but, except a few, the focus has mainly been on gaining and examining user generated content (UGC) for management and business intelligence purposes. Among the most relevant efforts in the domain may be included Silva's et al ad-hoc routines for Booking and TripAdvisor web data extraction and individual case studies on Airbnb's impact on local housing markets carried out by, e.g., Brauckman, Lima, and Cambell et al [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Can Location-Based Social Media and Online Reservation Services Tell More about Local Accommodation Industries than Open Governmental Data?

et al. 2019

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The paper follows-up ongoing research focusing on the potential of machine-readable data as additional knowledge in the governance of local tourism and destination management organizations (DMOs) in Slovakia. The current focus is on one classic social media (Facebook), one location-based social media (Foursquare), two hybrid travel-related platforms with partial attributes of reservation services (Google Places, TripAdvisor), and two online reservation services (Booking, Airbnb). The global aim is the usage of extracted data for the identification of additional entities with the obligation of local occupancy taxation, which is the financial backbone of Slovak (DMOs). A set of simple and globally reusable scripts constructed in Python and PostgreSQL were used to extract data on lodging providers from the Google Places application programming interface (API), the Facebook Place Search API and the Foursquare Venue API over grid overlays of districts' spatial representation. For pure scientific purposes in the case of Tripadvisor, Booking, and Airbnb, with no suitable access to open APIs, web scraping methods were used for data extraction. The pilot case was applied in the boundaries of Kosice city (Slovakia), and the aggregations of processed data were compared with official open statistics. Results indicate that the automated continuous monitoring of online platforms could help local public administrations in decreasing occupancy tax evasions and even widen knowledge about online audiences and visitors' satisfaction.

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Spatial Analytics and Data Visualization

Yang

2021

Handbook of E-Tourism

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Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources

Cited by 147 publications

References 17 publications

A Spatial Analysis of Intensity in Tourism Accommodation: An Application for Extremadura (Spain)

A Spatial Analysis of Intensity in Tourism Accommodation: An Application for Extremadura (Spain)

Can Location-Based Social Media and Online Reservation Services Tell More about Local Accommodation Industries than Open Governmental Data?

Spatial Analytics and Data Visualization

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