Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS

Palomares, Juan Carlos García; Puebla, Javier Gutiérrez; García, María del Carmen Mínguez

doi:10.1016/j.apgeog.2015.08.002

Cited by 311 publications

(222 citation statements)

References 20 publications

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“…Photographs taken by tourists can be differentiated from those taken by residents based on the time period in which the same user takes the photographs. The results obtained in the work of García-Palomares et al (2015) show clearly differentiated spatial distributions for tourists (more concentrated in sightseeing spots) and local residents (more widely dispersed throughout the city).…”

Section: Sightseeing: Photo-sharing Servicesmentioning

confidence: 93%

“…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). Photographs taken by tourists can be differentiated from those taken by residents based on the time period in which the same user takes the photographs.…”

Section: Sightseeing: Photo-sharing Servicesmentioning

confidence: 99%

“…Although there are several photo-sharing communities that allow the geolocation of photos (such as Flickr or Instagram), Panoramio is probably the most useful service for measuring tourist hotspots, as this website shows photographs taken of places or landscapes when sightseeing, which are then posted online once they have been georeferenced. The records of geolocated photographs can be used not only to identify sightseeing spots (García-Palomares et al, 2015), but also to analyse the spatial and temporal patterns of tourist flows in cities Girardin et al, 2008b).…”

Section: Introductionmentioning

confidence: 99%

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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: 93%

Section: Sightseeing: Photo-sharing Servicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tourists' digital footprint in cities: Comparing Big Data sources

et al. 2018

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“…The hot spot analysis using the G * i statistic has been commonly used in urban studies, particularly to identify significant hot spots of traffic accidents [43] or crime [44]. This approach has also been recently applied in tourism research [45]. The analysis was also based on Tobler's [40] First Law of Geography.…”

Section: Discussionmentioning

confidence: 99%

Seasonal Spatial Activity Patterns of Visitors with a Mobile Exercise Application at Seoraksan National Park, South Korea

et al. 2018

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Visitors' behavior in national parks can be influenced by seasonal variations in climate and preferred activities. Seasonality can produce different space consumption patterns, and impact visitor experience and natural resource use. The purpose of this study was to explore the seasonal spatial patterns of visitors' activities using a mobile exercise application within the context of Seoraksan National Park in South Korea. A dataset composed of 5142 starting and ending points of 2639 activities (hiking and walking) created by 1206 mobile exercise application users (January-December 2015) were collected from a leading mobile exercise application operator. GIS-based spatial analytical techniques were used to analyze the spatial patterns of activity points across seasons and days (weekdays/weekends). Results indicated considerable seasonal and daily variations in activity distribution and hot spots (i.e., locations of potential congestion or crowding). The findings enable park managers to mitigate negative impacts to natural resources as well as enhance visitors' experiences. Also, it allows potential visitors to decide when to visit certain sites via mobile application to ensure optimal conditions. Furthermore, the GPS-based exercise mobile application can be used as a new methodological approach to understand spatio-temporal patterns of visitors' behavior within national parks and other natural protected areas.

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“…Stepchenkova and Zhan (2013) utilized user-generated photos on the photo-sharing site, Flickr, to explore tourist perception on destination. Given the availability of geographical information embedded in the photos' metadata, recent works have resorted to geotagged photos as an alternative data source for tourist travel studies (Kadar 2013;Onder et al 2014;Vu et al 2015;Garcia-Palomares et al 2015;Vu et al 2017). However, these works focused mainly on tourist movements rather than on actual participated activities at the destination perhaps because of the lack of contextual information in the metadata of geotagged photos.…”

Section: Social Media As the New Data Sourcementioning

confidence: 99%

Tourist Activity Analysis by Leveraging Mobile Social Media Data

Law

et al. 2017

Journal of Travel Research

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Approaches to traditional travel diary construction rely on tourist participation and manual recording; hence, they are not only time-consuming but also limited in the scale and the number of samples. Online social network platforms have been used as alternative data sources for capturing the movements and travel patterns of tourists at a large scale. However, they fail to provide detailed contextual information on tourist activities for further analysis. In this paper, we present a new approach to travel diary construction based on the venue check-in data available in mobile social media with rich information on locations, time, and activities. Our case study focuses on the inbound tourism in Hong Kong using a data set comprising of 17,355 check-ins generated by 600 tourists. We demonstrate how the proposed travel diary can provide useful practical implications for applications in location management, transportation management, impact management, and tourist experience promotion among others.

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Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS

Cited by 311 publications

References 20 publications

Tourists' digital footprint in cities: Comparing Big Data sources

Tourists' digital footprint in cities: Comparing Big Data sources

Seasonal Spatial Activity Patterns of Visitors with a Mobile Exercise Application at Seoraksan National Park, South Korea

Tourist Activity Analysis by Leveraging Mobile Social Media Data

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