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

Abstract

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.

The spatial distribution of sustainable gastronomy: a case study of tourism in Prague

“…The limited area is conditioned by the highly uneven spatiotemporal patterns of tourist behaviour and associated localization of food service establishments. Tourists tend to stay in the destination's centre that may include iconic sights, shopping areas and places of historical significance (Ashworth & Page, 2011;Domenech et al, 2020;Hayllar & Griffin, 2009;Khan et al, 2020), especially in the close vicinity of their hotel (Lin et al, 2009;Shoval et al, 2011) without a significant dispersion out of the most popular area (Zee et al, 2020;Zee & Bertocchi, 2018). Even though this has been affected by Airbnb (Ki & Lee, 2019), the highest concentration of Airbnb listings still prevails in the vicinity of the most popular attractions (Boros et al, 2018;Gutiérrez et al, 2017;Roelofsen, 2018).…”

“…Tourists opt for fine dining more frequently than when they are at home (Wang et al, 2020). To cater to their needs, food service establishments cluster around the central area where the most popular attractions are located (Zee et al, 2020). Their location affects the food service establishments' popularity.…”

Comparing spatial and content analysis of residents and tourists using Geotagged Social Media Data. The Historic Neighbourhood of Alfama (Lisbon), a case study

“…On the one hand, the lifestyle of a resident tend to resemble that of a tourist, since a key element of urban development is the provision of cultural amenities and opportunities for consumption and leisure (Florida, 2002), understanding the city as an "entertainment machine" (Lloyd & Clark, 2001). On the other hand, according to post-tourism or third-generation tourism theories (Ashworth & Page, 2011;Hiernaux & González, 2014;Urry, 1990), the expectations and practices of a tourist tends to resemble that of a resident, since the tourist is a "resident on holiday". Hence, the practices of residents and visitors are alike and they are becoming increasingly interchangeable, to correspond to those of a new cosmopolitan, urban class with a medium-high economic level.…”

Integrated Approach for Optimizing the Touristic Space Plan for Sustainable Urban Development: A Case Study of Dalian City, China

“…[9]. Tourism space and hot spot distribution were also analysed by using tourist registration information, TripAdvisor data via social media [10], global positioning (GPS) technology [11], and the areas' function and tourist density.…”