Machine learning and points of interest: typical tourist Italian cities

Giglio, Simona; Bertacchini, Francesca; Bilotta, Eleonora; Pantano, Pietro

doi:10.1080/13683500.2019.1637827

Cited by 37 publications

(27 citation statements)

References 59 publications

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“…For example, Pantano et al [25] used a single cluster analysis method to analyze pictures from Flickr and demonstrated the role of building appearance in tourism attraction. Giglio et al [26] used a single cluster analysis method to analyze Flickr pictures in order to demonstrate the relationship between human mobility and tourist attractions. The above study directly used Flickr data from a certain region as an experimental object.…”

Section: Introductionmentioning

confidence: 99%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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Technological advances have led to numerous developments in data sources. Geo-tagged photo metadata has provided a new source of mass research data for tourism studies. A series of data processing methods centering on the various types of information contained in geo-tagged photo metadata have thus been proposed; as a result, the development of tourism studies based on such data has advanced. However, an in-depth study of the data processing methods designed to conduct tourist flow prediction based on geo-tagged photo metadata has not yet been conducted. In order to acquire accurate substitutive data regarding inbound flows in cities, this paper introduces and designs several methods, including data screening, text data similarity calculation, geographical location clustering, and time series data modelling, in order to realize a data preprocessing model for inbound tourist flows in cities based on geo-tagged photo metadata. Wherein, the entropy filtering method was introduced to aid in determining whether the data were posted by inbound tourists; whether the inbound persons’ activities were related to tourism was judged through the calculation of tag text similarity; an efficient clustering method based on geographic grid partition was designed for cases in which the tag values were empty; finally, the time series of the inbound tourist flows of a certain region and period were obtained through data statistics and normalization. For the empirical research, Beijing City in China was selected as the research case, after which the feasibility and accuracy of the methods proposed in this paper were verified through data correlation analysis between Flickr data and real statistical yearbook data, as well as analysis of the prediction results based on a machine learning algorithm. The data preprocessing method introduced and designed in this paper provides a reference for the study of geo-tagged photo metadata in the field of tourism flow prediction. These methods can effectively filter out inbound tourist flow data from geotag photo metadata, thus providing a novel, reliable, and low-cost research data source for urban inbound tourism flow forecasting.

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Section: Introductionmentioning

confidence: 99%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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“…On the other hand, information from social networks has also been used to identify tourist attractions [24] and user behavior [25]. Based on this knowledge, the study of the behavior of a user acquires, in contemporary research, a privileged position to propose appropriate marketing strategies to promote a site [24,26].…”

Section: Related Workmentioning

confidence: 99%

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

et al. 2021

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In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing activities, tools and techniques, delivered with the use of electronic channels for the specific purpose of locating, building and improving long- term relationships with customers, to enhance their individual potential. In this paper, we refer to the analysis of information in three aspects: customer satisfaction, the study of customer behavior and the forecast of tourist demand. Specifically, we have created a novel dataset comprising the non-verbal preference assessment of tourists who are clients of the Sol Cayo Guillermo hotel belonging to the Melia hotel chain, in Jardines del Rey, Cuba. Then, by applying Computational Intelligence algorithms to this dataset, we achieve segment customers according to their non-verbal preferences, in order to increase their satisfaction, and therefore the client profitability. In order to achieve a good performance in the realization of this task, we have proposed two modifications of the Naïve Associative Classifier, whose results are compared with the most relevant computational algorithms of the state of the art. The experimentally obtained values of balanced accuracy and averaged F1 measure show that, by clearly improving the results of the state-of-the-art algorithms, our proposal is adequate to successfully use electronic customer relationship management in the tourist services provided by hotel chains.

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“…Geo-tagged photos enabled researchers to analyse tourists' spatial network related to cultural heritages (Nguyen, Camacho, et al, 2017;Nguyen, Hwang, et al, 2017), cultural ecosystem services (CES) (Retka et al, 2019) and conservation areas . The source of geo-tagged images are posted by tourists using social network platforms such as Facebook, Instagram, Twitter, Flickr, Panoramio and Foursquare (Encalada et al, 2017;Giglio et al, 2019a;Hausmann et al, 2018;Nguyen, Camacho, et al, 2017;Salas-Olmedo et al, 2018).…”

Section: Sources Of Big Datamentioning

confidence: 99%

A systematic literature review on the use of big data for sustainable tourism

Rahmadian

Feitosa

Zwitter

2021

Current Issues in Tourism

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Sustainable tourism research focuses on mitigating or remediating environmental, social and economic impacts on tourism. In the past years, Big Data approaches have been applied to the field of tourism allowing for remarkable progress. However, there seems to be little evidence to support that such approaches are an inspiration to sustainable tourism and are being implemented. In this context, we aim to obtain a comprehensive overview of the use of Big Data in sustainable tourism to address various issues and understand how Big Data can support decision-making in such scenarios. To that end, this paper reports on the results of a literature review via a combination of a Systematic Literature Review (SLR) in Software Engineering, and the use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. In summary, we investigated four facets: (a) sources of big data, (b) approaches, (c) purposes, and (d) contexts of application. The results suggest that the use of various approaches have impacted practices in sustainable tourism. The findings provide a thorough understanding of the state of the art of Big Data application in sustainable tourism and provide valuable insights to foster growth both in terms of research and practice.

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Machine learning and points of interest: typical tourist Italian cities

Cited by 37 publications

References 59 publications

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

A systematic literature review on the use of big data for sustainable tourism

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