Characterization of daily tourism behaviors based on place sequence analysis from photo sharing websites

Loiseau, Thomas-Joseph; Djebali, Sonia; Raimbault, Thomas; Branchet, Bérengère; Chareyron, Gaël

doi:10.1109/bigdata.2017.8258241

Cited by 5 publications

(5 citation statements)

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“…Other studies have focused on major European cities, such as Paris (Loiseau, et al, 2017), or on comparisons between the likes of Athens, Barcelona, Berlin, London, Madrid, Paris, Rome and Rotterdam (García-Palomares et al, 2015). Even in studies that have analysed aspects of a more social nature, such as long-standing problems of socio-spatial inequality, the cities chosen have tended to be located in the Western World, as in the case of Louisville, Kentucky (Shelton et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Tourist hot spots in cities with the highest murder rates

Agustí

2019

Tourism Geographies

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Social networks have facilitated access to the spatial location of usergenerated images and this has permitted studies of the spatial distribution of tourist images. To date, however, most studies have tended to focus on European or North American cities. The current research focuses on an analysis of the behaviour of tourists in cities with other social, economic and cultural contexts. To be more precise, we analyse the cities of Los Cabos, Acapulco, Tijuana and La Paz (Mexico), and Natal and Fortaleza (Brazil). These cities share the condition of being both tourist hot spots and some of the cities with the highest murder rates in the world. The main objective of this work is to analyse the spatial concentration of images taken by tourists in contexts that have hitherto received little analysis in academic literature. The results show a clear spatial concentration of user-generated images, especially around the spaces that offer the highest levels of safety.

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Section: Introductionmentioning

confidence: 99%

Tourist hot spots in cities with the highest murder rates

Agustí

2019

Tourism Geographies

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“…In the literature, the proposed ontology-based clustering method was derived from an extension of the AGNES clustering algorithm. Researchers in [5] designed an original approach to characterize the daily behaviors of tourists by analyzing the sequences of places that were visited by tourists per day, in which the geolocation information of tourists on photo-sharing websites was used as the data, from which the AGNES clustering algorithm formed clusters and carried out the experiment. The study in [6] proposed a point-of-interest (POI) recommendation method to plan tourism routes.…”

Section: Introductionmentioning

confidence: 99%

“…According to the literature review, tourism clustering research has predominantly focused on tourist attractions and tourist clustering. As seen in [1][2][3][4][5][6][7], clustering algorithms have been used in tourism research for POI extraction, data mining, algorithm modeling, transportation behavior, etc. The other clustering methods in [8][9][10][11][12][13][14][15] indicated that spatial and attribute data of tourist attractions were the main targets that were used to generate proper tourism categories, extract tourist preferences, and recommend appropriate tourist destinations.…”

Section: Introductionmentioning

confidence: 99%

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Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Zhou

Tian

2022

IJGI

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This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.

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“…Instagram images have been used to analyse tourist behaviour in various different cities. That said, in the majority of cases, these have been western cities, with long traditions of tourism: cities in the USA (Pat, Kanza, & Naaman, 2015), Europe (Loiseau, Djebali, Raimbault, Branchet, & Chareyron, 2017), Asia (Hu et al, 2015) or the Americas (Paül i Agustí, 2020). The present research adds the study of six African cities to the existing literature.…”

Section: Introductionmentioning

confidence: 99%

Mapping tourist hot spots in African cities based on Instagram images

Agustí

2020

Journal of Tourism Research

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Knowing in which areas tourists concentrate is fundamental for managing its effects. However, to date, research in this subject has mainly been carried out only in European and North American cities. The current article, in contrast, focuses on six African cities. We have analysed the spatial concentration of the images captured by Instagram users to identify the most visited locations. The results obtained show how there is a strong spatial concentration of images in African cities, little territorial continuity between the spaces visited, and a predominance of private attractions. These are important differences with respect to what has been described in the literature in other contexts.

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Characterization of daily tourism behaviors based on place sequence analysis from photo sharing websites

Cited by 5 publications

References 12 publications

Tourist hot spots in cities with the highest murder rates

Tourist hot spots in cities with the highest murder rates

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Mapping tourist hot spots in African cities based on Instagram images

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