Touristic site attractiveness seen through Twitter

Bassolas, Aleix; Lenormand, Maxime; Tugores, Antònia; Gonçalves, Bruno; Ramasco, José J.

doi:10.1140/epjds/s13688-016-0073-5

Cited by 43 publications

(37 citation statements)

References 40 publications

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“…The very few works that use geolocated tweets in the field of tourism tend to focus on comparing visitor's spatial behaviour between cities on the national or global scale (Bassolas et al, 2016;Hawelka et al, 2014;Sobolevsky et al, 2015), but do not address the spatial patterns within the city.…”

Section: Being Connected: Twittermentioning

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: Being Connected: Twittermentioning

confidence: 99%

Tourists' digital footprint in cities: Comparing Big Data sources

et al. 2018

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“…Right yellow node is an intersection and is the other end of that segment. In the second part of the algorithm (lines [11][12][13][14][15][16][17][18][19], we only compute the intersections of smoothed trajectories that have an overlap of bounds. As before, we proceed in the same way with the sequences choosing start and end points.…”

Section: Algorithmmentioning

confidence: 99%

“…One finding of our approach is the detection of regions with relevant importance where landscape interventions can be applied in function of the communities.arXiv 1/20The problem of modelling human behaviour in urban and natural environments has been widely studied. In urban environments, human mobility data have been extracted from internet and mobile phone connections [1,[16][17][18], geolocalized tweets [19][20][21], and GPS-tagged photos [13,22,23] among other sources. In natural environments, generally natural parks, visitors or hikers mobility data come from GPS devices instead of mobile phone connections due to insufficient precision or the lack of coverage.…”

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confidence: 99%

Analysing human mobility patterns of hiking activities through complex network theory

et al. 2017

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The exploitation of high volume of geolocalized data from social sport tracking applications of outdoor activities can be useful for natural resource planning and to understand the human mobility patterns during leisure activities. This geolocalized data represents the selection of hike activities according to subjective and objective factors such as personal goals, personal abilities, trail conditions or weather conditions. In our approach, human mobility patterns are analysed from trajectories which are generated by hikers. We propose the generation of the trail network identifying special points in the overlap of trajectories. Trail crossings and trailheads define our network and shape topological features. We analyse the trail network of Balearic Islands, as a case of study, using complex weighted network theory. The analysis is divided into the four seasons of the year to observe the impact of weather conditions on the network topology. The number of visited places does not decrease despite the large difference in the number of samples of the two seasons with larger and lower activity. It is in summer season where it is produced the most significant variation in the frequency and localization of activities from inland regions to coastal areas. Finally, we compare our model with other related studies where the network possesses a different purpose. One finding of our approach is the detection of regions with relevant importance where landscape interventions can be applied in function of the communities.

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“…Bassolas et al [17] assess the attractiveness of 20 worldwide touristic sites. The research assesses attractiveness on the basis of two metrics: average distance between the location of residence and the touristic site, and the area covered by the visitors' places of residence computed as the number of countries of residence.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data

Maeda

Yoshida

Toriumi

et al. 2018

IJGI

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Abstract:Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country.

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Touristic site attractiveness seen through Twitter

Cited by 43 publications

References 40 publications

Tourists' digital footprint in cities: Comparing Big Data sources

Tourists' digital footprint in cities: Comparing Big Data sources

Analysing human mobility patterns of hiking activities through complex network theory

Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data

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