Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

[EN] Promoting a tourist destination requires uncovering travel patterns and destination choices, identifying the profile of visitors and analyzing attitudes and preferences of visitors for the city. To this end, tourism-related data are an invaluable asset to understand tourism behaviour, obtain statistical records and support decision-making for business around tourism. In this work, we study the behaviour of tourists visiting top attractions of a city in relation to the tourist influx to restaurants around the attractions. We propose to undertake this analysis by retrieving information posted by visitors in a social network and using an open access map service to locate the tweets in a influence area of the city. Additionally, we present a pattern recognition based technique to differentiate visitors and locals from the collected data from the social network. We apply our study to the city of Valencia in Spain and Berlin in Germany. The results show that, while in Valencia the most frequented restaurants are located near top attractions of the city, in Berlin, it is usually the case that the most visited restaurants are far away from the relevant attractions of the city. The conclusions from this study can be very insightful for destination marketers.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors. 19(11):1-25. https://doi.org/10.3390/s19112612S1251911Travel and Tourism Competitiveness Report 2017http://reports.weforum.org/travel-and-tourism-competitiveness-report-2017/OECD Datahttps://data.oecd.org/Travel &Tourism: Economic Impact 2019 Worldhttps://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2019/world2019.pdfCohen, S. A., Prayag, G., & Moital, M. (2013). Consumer behaviour in tourism: Concepts, influences and opportunities. Current Issues in Tourism, 17(10), 872-909. doi:10.1080/13683500.2013.850064Yoo, C.-K., Yoon, D., & Park, E. (2018). Tourist motivation: an integral approach to destination choices. Tourism Review, 73(2), 169-185. doi:10.1108/tr-04-2017-0085Cohen, E. (1979). A Phenomenology of Tourist Experiences. Sociology, 13(2), 179-201. doi:10.1177/003803857901300203Decrop, A., & Snelders, D. (2005). A grounded typology of vacation decision-making. Tourism Management, 26(2), 121-132. doi:10.1016/j.tourman.2003.11.011Servidio, R., & Ruffolo, I. (2016). Exploring the relationship between emotions and memorable tourism experiences through narratives. Tourism Management Perspectives, 20, 151-160. doi:10.1016/j.tmp.2016.07.010Prayag, G., Hosany, S., Muskat, B., & Del Chiappa, G. (2016). Understanding the Relationships between Tourists’ Emotional Experiences, Perceived Overall Image, Satisfaction, and Intention to Recommend. Journal of Travel Research, 56(1), 41-54. doi:10.1177/0047287515620567Valls, J.-F., Sureda, J., & Valls-Tuñon, G. (2014). Attractiveness Analysis of European Tourist Cities. Journal of Travel & Tourism Marketing, 31(2), 178-194. doi:10.1080/10548408.2014.873310García-Palomares, J. C., Gutiérrez, J., & Mínguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. doi:10.1016/j.apgeog.2015.08.002Lu, Y., Wu, H., Liu, X., & Chen, P. (2019). TourSense: A Framework for Tourist Identification and Analytics Using Transport Data. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2407-2422. doi:10.1109/tkde.2019.2894131Buhalis, D. (2000). Marketing the competitive destination of the future. Tourism Management, 21(1), 97-116. doi:10.1016/s0261-5177(99)00095-3Indicators for Measuring Competitiveness in Tourism: A Guidance Documenthttp://dx.doi.org/10.1787/5k47t9q2t923-enLonghi, C., Titz, J.-B., & Viallis, L. (2014). Open Data: Challenges and Opportunities for the Tourism Industry. Tourism Management, Marketing, and Development, 57-76. doi:10.1057/9781137354358_4Open Data in Tourismhttps://www.europeandataportal.eu/en/highlights/open-data-tourismCox, C., Burgess, S., Sellitto, C., & Buultjens, J. (2009). The Role of User-Generated Content in Tourists’ Travel Planning Behavior. Journal of Hospitality Marketing & Management, 18(8), 743-764. doi:10.1080/19368620903235753Lu, W., & Stepchenkova, S. (2014). User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software. Journal of Hospitality Marketing & Management, 24(2), 119-154. doi:10.1080/19368623.2014.907758Pantano, E., Priporas, C.-V., & Stylos, N. (2017). ‘You will like it!’ using open data to predict tourists’ response to a tourist attraction. Tourism Management, 60, 430-438. doi:10.1016/j.tourman.2016.12.020Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. doi:10.1080/15230406.2014.890072Girardin, F., Calabrese, F., Fiore, F. D., Ratti, C., & Blat, J. (2008). Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4), 36-43. doi:10.1109/mprv.2008.71Alivand, M., & Hochmair, H. H. (2016). Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr. Cartography and Geographic Information Science, 44(2), 170-184. doi:10.1080/15230406.2016.1211489Bassolas, A., Lenormand, M., Tugores, A., Gonçalves, B., & Ramasco, J. J. (2016). Touristic site attractiveness seen through Twitter. EPJ Data Science, 5(1). doi:10.1140/epjds/s13688-016-0073-5Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Francalanci, C., & Hussain, A. (2015). Discovering social influencers with network visualization: evidence from the tourism domain. Information Technology & Tourism, 16(1), 103-125. doi:10.1007/s40558-015-0030-3Williams, N. L., Inversini, A., Ferdinand, N., & Buhalis, D. (2017). Destination eWOM: A macro and meso network approach? Annals of Tourism Research, 64, 87-101. doi:10.1016/j.annals.2017.02.007Salas-Olmedo, M. H., Moya-Gómez, B., García-Palomares, J. C., & Gutiérrez, J. (2018). Tourists’ digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13-25. doi:10.1016/j.tourman.2017.11.001Padilla, J. J., Kavak, H., Lynch, C. J., Gore, R. J., & Diallo, S. Y. (2018). Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. PLOS ONE, 13(6), e0198857. doi:10.1371/journal.pone.0198857Maeda, T., Yoshida, M., Toriumi, F., & Ohashi, H. (2018). Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS International Journal of Geo-Information, 7(3), 99. doi:10.3390/ijgi7030099Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255. doi:10.1016/s0261-5177(02)00071-7Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2016). Towards cross-domain data analytics in tourism: a linked data based approach. Information Technology & Tourism, 16(1), 71-101. doi:10.1007/s40558-015-0049-5Adamiak, C., Szyda, B., Dubownik, A., & García-Álvarez, D. (2019). Airbnb Offer in Spain—Spatial Analysis of the Pattern and Determinants of Its Distribution. ISPRS International Journal of Geo-Information, 8(3), 155. doi:10.3390/ijgi8030155Padron Municipal de Habitantes [Statistical Report: Residents in Valencia in 2018]https://bit.ly/2JnNNE

Site Selection Using Geo-Social Media: A Study For Eateries In Lisbon

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe rise in the influx of multicultural societies, studentification, and overall population growth has positively impacted the local economy of eateries in Lisbon, Portugal. However, this has also increased retail competition, especially in tourism. The overall increase in multicultural societies has also led to an increase in multiple smaller hotspots of human-urban attraction, making the concept of just one downtown in the city a little vague. These transformations of urban cities pose a big challenge for upcoming retail and eateries store owners in finding the most optimal location to set up their shops. An optimal site selection strategy should recommend new locations that can maximize the revenues of a business. Unfortunately, with dynamically changing human-urban interactions, traditional methods like relying on census data or surveys to understand neighborhoods and their impact on businesses are no more reliable or scalable. This study aims to address this gap by using geo-social data extracted from social media platforms like Twitter, Flickr, Instagram, and Google Maps, which then acts as a proxy to the real population. Seven variables are engineered at a neighborhood level using this data: business interest, age, gender, spatial competition, spatial proximity to stores, homogeneous neighborhoods, and percentage of the native population. A Random Forest based binary classification method is then used to predict whether a Point of Interest (POI) can be a part of any neighborhood n. The results show that using only these 7 variables, an F1-Score of 83% can be achieved in classifying whether a neighborhood is good for an “eateries” POI. The methodology used in this research is made to work with open data and be generic and reproducible to any city worldwide

The Route Towards The Shawshank Redemption: Mapping Set-jetting with Social Media

With the development of the Web 2.0, more and more geospatial data are generated via social media. This segment of what is now called “big data” can be used to further study human spatial behaviors and practices. This project aims to explore different ways of extracting geodata from social media in order to contribute to the growing body of literature dedicated to studying the contribution of the geoweb to human geography. More specifically, this project focuses on the potential of social media to explore a growing tourism phenomenon: set-jetting. Set-jetting refers to the activity whereby people travel to visit shooting locations that appear in movies. The case study presented here focuses on the Mansfield Reformatory (Ohio, USA), which was used as the shooting location for the film The Shawshank Redemption (Dir. Frank Darabont, 1994). Through the analysis of georeferenced data mined from Twitter, Flickr, and Tripadvisor, this project presents and discusses the differences and similarities between the use of these three platforms by set-jetters to share and access geodata associated with an alternative tourist destination. The results demonstrate the complementarity of each of these applications to studying set-jetting at different scales. While Twitter appears more appropriate to study this phenomenon at a global scale, Tripadvisor provides more relevant information at the regional level and Flickr can be mobilized to study the movements of set-jetters at a very local scale. Overall, beyond the methodological and technological issues associated with the use of these social media in studying the geography of set-jetting, these applications offer new perspectives for the tourism industry and open new research areas for academics as well

Sentiment Analysis in Tourism: Athens’s vibe through online platforms

Η βιομηχανία του τουρισμού αποτελεί παγκοσμίως μία βασική πηγή εισοδήματος, εργασιών και πλούτου. Συγκεκριμένα στην Ελλάδα, η οποία κατατάσσεται ανάμεσα στους κορυφαίους ταξιδιωτικούς προορισμούς, ο τουρισμός είναι θεμέλιος λίθος για την οικονομική ανάπτυξη και τις θέσεις εργασίας. Εν μέσω της πρόσφατης οικονομικής κρίσης, ο τουρισμός είναι ένας από τους λίγους τομείς που κατάφερε να επιβιώσει και να ευδοκιμήσει. Επί του παρόντος, η τοπική βιομηχανία εστιάζει στην επιμήκυνση των τουριστικών περιόδων και στην βελτίωση των παρεχόμενων υπηρεσιών. Κατ’ αυτόν τον τρόπο η Ελλάδα θα προσελκύσει περισσότερο κοινό με αποτέλεσμα να διατηρήσει την θέση της στην αγορά. Σε καθημερινή βάση, η Ελλάδα επενδύει σε προϊόντα και στρατηγικές που οδηγούν σε πιο εξατομικευμένη και ελκυστική εμπειρία διακοπών. H χρήση της τεχνολογίας και της επιστήμης των υπολογιστών ελαχιστοποιεί το ρίσκο των επενδύσεων και ταυτόχρονα, επισημαίνει και ικανοποιεί τις ανάγκες των επισκεπτών. Ειδικότερα, μέσω των τομέων της Εξόρυξης Δεδομένων και των “Μεγάλων” Δεδομένων, οι επιστήμονες μπορούν να αναλύουν τεράστιο ποσό δεδομένων και να εξάγουν μη προβλέψιμα συμπεράσματα. Η παρούσα πτυχιακή εργασία επιδιώκει να αφουγκραστεί τον “παλμό” της ελληνικής πρωτεύουσας, της Αθήνας. Ο κύριος στόχος είναι η εξαγωγή δεδομένων από την ανάλυση κριτικών/αναρτήσεων που έχουν συνταχθεί από τουρίστες σε διάφορες πλατφόρμες (Twitter, Google Maps, Foursquare, Airbnb). Τα παραπάνω στοιχεία συλλέχθηκαν, αποθηκεύτηκαν και συνδυάστηκαν σε ένα αρχείο, το οποίο περιέχει σημαντικές πληροφορίες για τις κριτικές/αναρτήσεις όπως για παράδειγμα το κείμενο, την προέλευση, την τοποθεσία, το θέμα και τον τύπο της δραστηριότητας που περιγράφεται. Κάθε κριτική/ανάρτηση μεταποιείται και μετατρέπεται σε διάνυσμα. Έπειτα αλγόριθμοι και εργαλεία ανάλυσης συναισθήματος χρησιμοποιούνται για να καθορίσουν το συναίσθημα κάθε δεδομένου. Τα αποτελέσματα της ανάλυσης είναι πολυδιάστατα. Διαγράμματα και άλλες πρακτικές οπτικοποίησης χρησιμοποιούνται για να παρουσιάσουν τη γενική αίσθηση των τουριστών για τις διάφορες περιοχές και παροχές αυτής της πόλης (φαγητό, διαμονή, νυχτερινή ζωή, μουσεία/ αρχαιολογικοί χώροι, διασκέδαση). Η έκβαση της έρευνας είναι ένας αποτελεσματικός και βασισμένος στην κοινή γνώμη τρόπος να ανιχνευτούν προβλήματα αλλά και διευκολύνσεις της Αθήνας. Στο μέλλον, θα ήταν ενδιαφέρον ο συνδυασμός αυτών των δεδομένων με περιβαλλοντικούς δείκτες (π.χ. εκπομπή καυσαερίου, κατανάλωση νερού) κατά τη διάρκεια τουριστικών περιόδων για να απεικονίσουμε το συνολικό αντίκτυπο του τουρισμού. Συνεπώς, η βιομηχανία των διακοπών θα παρέχει προσωποποιημένες αλλά ταυτόχρονα βιώσιμες δραστηριότητες.Tourism is a big worldwide industry and a major source of income, jobs and wealth in several countries. Particularly in Greece, which is considered to be one of the top global travel destinations, tourism is the keystone of economic growth and employment. Even during the recent economic crisis, tourism is one of the few sectors that has thrived. The local industry is currently focusing on expanding the tourist season and also improving the services provided. Therefore, more tourists will be attracted and Greece will maintain its advantageous position in the tourist market. On an everyday basis, Greece makes significant investments on products and strategies that will make the holiday experience more personalized and appealing. The use of technology and Data Science is able to minimize the risk of such investments, to highlight and fulfill visitors’ needs. Especially, by leveraging Data Mining and Big Data, scientists could analyze a huge amount of data and extract non-obvious results. This thesis seeks to “listen to the pulse” of Greece’s capital city, Athens. The main goal is to extract results from analyzing reviews and posts made by tourists on different platforms (Twitter, Google Maps, Foursquare, Airbnb). The above were collected, stored and combined into a single file that contained significant information, such as the review/post text, the origin, the location, the topic and the type of the mentioned activity. Each review/post was then processed and transformed into a vector. Afterwards, sentiment analysis algorithms and tools were used to define the sentiment of every data. The results of the analysis are multidimensional. Diagrams and other visualization practices were used to represent the general feeling of tourists about the different districts and provided services of this city (food, accommodation, nightlife, museums/archaeological spaces, entertainment). The outcome of the research proposes an effective way to detect both the city’s inconveniences and highlighted facilities, based on visitors’ opinion. In the future, it would be interesting to combine these data aspects with environmental indicators (e.g., gas emission, water consumption) during tourist periods to depict the overall impact on tourism. This way, the holidays’ industry can provide more personalized, yet sustainable activities

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management