Overtourism problems, anti-tourist movements and negative externalities of tourism are popular research approaches and are key concepts to better understand the sustainable development of tourism destinations. In many of the overtourism narratives, Venice is considered to be one of the most relevant cases of overtourism and therefore has become a laboratory for studying the different conflicts that emerge when tourism numbers continue to grow and the quality of the tourism flow continues to decline. This article is therefore focusing on Venice and on one of the possible solutions to mitigate the negative impacts of tourism represented by the concept of a tourist carrying capacity (TCC) in an urban destination. The aim of this paper is to discuss alternative methodologies regarding the calculation of the TCC, and to apply a fuzzy instead of a ‘crisp’ linear programming model to determine the scenarios of a sustainable number of tourists in the cultural destination of Venice, looking for the optimal compromise between, on the one hand, the wish of maximizing the monetary gain by the local tourism sectors and, on the other, the desire to control the undesirable effects that tourism exerts on a destination by the local population. To solve the problems related to tourism statistics and data availability, some uncertainty in the parameters has been included using fuzzy numbers. The fuzziness in the model was introduced on the basis of questionnaires distributed among both tourists and residents. By applying the fuzzy linear programming model to the emblematic case of Venice, it was shown that this approach can indeed help destinations to understand the challenges of sustainable tourism development better, to evaluate the impact of alternative policies of overtourism on the sustainability of tourism, and hence, to help design a strategy to manage tourist flows more adequately

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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In just a couple of years, the sharing econom y grew out to becom e a significant segm ent of the holiday accom m odation m arket. O nline peer-topeer m arketplaces allow people to offer room s or entire houses to tourists, w ith A irbnb being the biggest and m ost fam ous exam ple. T his paper aim s to give an insight into explaining w hich factors and attributes influence the success of A irbnb accom m odations in the V eneto R egion, using occupancy as a proxy. W e analysed characteristics of 1962 4 A irbnb accom m odations. T he logistic regression m odel identifies a num ber of influential attributes w hich can be divided betw een locational characteristics, being located in attractive tourism destinations, and accom m odation characteristics, for exam ple the price, rating, num ber of previous bookings and the status of the host. T he quantitative analysis allow s to create an attractiveness scale, w hich is analysed for geographic patterns.

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Venice is one of the most famous iconic destinations and one of the most emblematic cases of overtourism affecting a historic city. Here, social movements against tourism have emerged as a reaction to vastly unsustainable tourist flows that have had dramatic and transformational impacts on Venetians’ lives. The aim of this paper is to investigate how tourism transforms the social, cultural, and everyday geographies of the city. The effects of tourism on the historic city are conceived as a process of continuous transformation and repositioning. Taking into consideration the most tangible daily practices of tourists (eating, sleeping, and buying) and the finer dynamics of Venice’s tourism problem, we translate data on these practices into a temporal and spatial analysis to better understand how dynamic the texture of the city is in relation to the tourism subsystem. A comparison between 2008 and 2019 is conducted to evaluate the impact of tourism on residential uses of the city and measure the sustainability of growth of the tourism facilities. The investigation highlighted an impressive accommodation’s growth, from 8.249 in 2008 to 49.260 in 2019 of bed places (497% growth) in the entire historical city, a similar expansion is also evident in the total number of restaurants that has increased by 160% in all districts and a variations of 4% in shops instead of a population decline of −13% in the same period. In addition, a residents’ survey in spring 2019 was conducted to better understand the intensity of these impacts and the motives for depopulation and the anti-tourism movements. We focus on how tourism, if not managed and planned, radically changes the social and urban structures of the city and the lives of local residents. We conclude by presenting some local theoretical and practical insights into the touristic pressure, provided by citizens’ associations on one side and policymakers on the other.

This chapter examines the contemporary touristification of Venice, first through a 'panoramic' overview, and then through a deeper analysis of the spatial impacts of tourism development over the last decade. The analysis demonstrates how the impacts of overtourism stretch far beyond dimensions of local quality of life, affecting and irreversibly changing the balance of the city's economic landscape and leading to transformation of its fundamental geography. This analysis illustrates an alarming picture of Venice where, for example, the number of residents in the city is now equivalent to the number of beds for tourists. Finally, the chapter proposes a list of possible overtourism indicators that could further contribute to the overtourism debates, and stimulate new areas of inquiry that may be useful in explaining the reasons for the development and intensification of anti-tourist sentiments.

Overtourism studies are increasingly focused on the relationship between tourists and residents. This includes the livability of the destination and the well-being of its residents; the growth of the tourism sector (particularly unchecked or unlimited growth), as well as the threat to natural heritage, such as beaches and mountains. A number of researchers have also highlighted the popularity of the term, as well as the lack of a theoretical understanding of the implications of it, and practical solutions to the problems posed by overtourism. This research aims to monitor the impact of, and understand the problems posed by, overtourism through approaching the phenomenon through the lens of big data analytics. The location of thisresearch is a UNESCO World Heritage site in Italy, namely the Dolomites. By using telco data, we were able to apply a big data analysis of a destination in order to monitor the movement of tourists and day visitors. By analyzing their behaviour at the destination, it has been possible to quantify daily visitors and analyse how they impact this natural site. In addition, it has beenpossible to compare statistical data with big data, which offers new insights into tourism at the destination. This research, by exploiting the value of big data in tourism, creates a heritage usage rate as well as new indicators for the measurement of overtourism. Ultimately, this can help to control tourism flows and mitigate negative externalities.