PL EN


2020 | 1(45) | 69-76
Article title

A strategic tourism knowledge base for socio-economic and environmental data analytics: The role of Big Data analysis

Content
Title variants
PL
Strategiczna baza danych społeczno-ekonomicznych i środowiskowych w turystyce – rola analizy zbiorów typu Big Data
Languages of publication
EN PL
Abstracts
EN
This research studies the application of modern practices and technologies for the collection of a large volume and variety of data, in order to develop a research knowledge base for data mining and analysis in the tourism sector and especially on Cruise and Ship Lines Passengers. Emphasis was given to the application of appropriate methods of data analysis and processing, to produce tangible results for the benefit of sustainable tourism development. Current research focuses on structuring a data warehouse for the collected information in order to apply online analytical processing techniques on the stored data, as well as data mining and data visualization. A holistic approach is proposed, along with a new model for analyzing the impact of tourism activity in general—and cruises in particular—on local society. The results will be utilized as a strategic tool for decision-making by those involved in the tourism sector of cruise areas, with ways to maximize the benefits of tourism, such as increasing overnight stays and, more broadly, passenger consumption, and ways to reduce the environmental impact of visitors and passengers in the ecosystem of cruise areas.
PL
W artykule przedstawiono zastosowanie nowoczesnych narzędzi i technologii umożliwiających gromadzenie dużej ilości różnorodnych danych w celu tworzenia ich zbiorów, pozwalających na eksplorację i analizę naukową danych dotyczących sektora usług turystycznych, a zwłaszcza problemów linii i statków wycieczkowych oraz ich pasażerów. Szczególny nacisk położono na zastosowanie odpowiednich metod analizy i przetwarzania danych do uzyskiwania konkretnych wyników, które będą wspierać zrównoważony rozwój turystyki. Przeprowadzone badania koncentrują się na architekturze hurtowni danych, w których gromadzone są pozyskane informacje, umożliwiającej analityczne przetwarzanie zgromadzonych danych (OLAP), a także analizę zestawów danych (data mining) i ich wizualizację. Wyniki badań zostaną wykorzystane jako strategiczne narzędzie podejmowania decyzji w sektorze usług turystycznych, w szczególności w obszarze dotyczącym rejsów wycieczkowych, oraz maksymalizacji korzyści, takich jak zwiększenie liczby noclegów i szerzej – konsumpcji pasażerów, umożliwią także zmniejszenie wpływu odwiedzających i pasażerów statków wycieczkowych na ekosystemy odwiedzanych obszarów.
Year
Issue
Pages
69-76
Physical description
Dates
published
2020-03-31
Contributors
References
  • Aggarwal, C. C. (2011). An introduction to social network data analytics. In: C. C. Aggarwal (ed.). Social network data analytics (pp. 1–15). Boston: Springer. ISBN 9781441984616.
  • Apostolopoulos, Y., Leivadi, S., Yiannakis, A. (eds.). (2013). The sociology of tourism: Theoretical and empirical investigations. Vol. 1. London and New York: Routledge. ISBN 9780415271653.
  • Cevher, V., Becker, S., Schmidt, M. (2014). Convex optimization for Big Data: Scalable, randomized, and parallel algorithms for Big Data analytics. IEEE Signal Processing Magazine, 31(5), 32–43.
  • Chiappa, G. D., Baggio, R. (2015). Knowledge transfer in smart tourism destinations: Analyzing the effects of a network structure. Journal of Destination Marketing and Management, 4(3), 145–150.
  • Costa, C., Santos, M. Y. (2017). Big Data: State-of-the-art concepts, techniques, technologies, modeling approaches and research challenges. IAENG International Journal of Computer Science, 44(3), 285–301.
  • Cupul-Magaña, A. L., Rodríguez-Troncoso, A. P. (2017). Tourist carrying capacity at Islas Marietas National Park: An essential tool to protect the coral community. Applied Geography, 88, 15–23.
  • Dodds, R. (2007). Sustainable tourism and policy implementation: Lessons from the case of Calviá, Spain. Current Issues in Tourism, 10(4), 296–322.
  • Elastic. (2018). Open source search, analytics [online, accessed: 2018-01-05]. Retrieved from: http://elastic.co.
  • Frechtling, D. C. (2010). The tourism satellite account: A Primer. Annals of Tourism Research, 37(1), 136–153.
  • Fuchs, M., Höpken, W., Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations – A case from Sweden. Journal of Destination Marketing and Management, 3(4), 198–209.
  • Graymore, M. L., Sipe, N. G., Rickson, R. E. (2010). Sustaining human carrying capacity: A tool for regional sustainability assessment. Ecological Economics, 69(3), 459–468.
  • Gretzel, U., Werthner, H., Koo, C., Lamsfus, C. (2015). Conceptual foundations for understanding smart tourism ecosystems. Computers in Human Behavior, 50, 558–563.
  • ΗSA. (2016). Data for Arrivals at Hotels at Piraeus Destination [online, accessed: 2018-01-05]. Piraeus: Hellenic Statistic Association. Retrieved from Internet: https://www.statistics.gr/el/statistics/-/publication/STO12/2016.
  • Hu, H., Wen, Y., Chua, T.-S., Li, X. (2014). Toward scalable systems for Big Data analytics: A technology tutorial. IEEE Access, 2, 652–687.
  • Hunter, C. (2002). Sustainable tourism and the touristic ecological footprint. Environment, Development and Sustainability, 4(1), 7–20.
  • ISI − Integrated Spatial Investments. (2017). Integrated Urban Development Plan: Evaluation of the Integrated Spatial Investment Tool. Piraeus: Municipality of Piraeus.
  • Jones, C., Munday, M. (2007). Exploring the environmental consequences of tourism: A satellite account approach. Journal of Travel Research, 46, 164–172.
  • Kibana. (2018). Explore, visualize, discover data [online, accessed: 2018-01-05]. Mountain view: Elastic. Retrieved from: http://elastic.co/products/kibana.
  • Koutsouris, A. (2009). Social learning and sustainable tourism development; local quality conventions in tourism: A Greek case study. Journal of Sustainable Tourism, 17(5), 567–581.
  • Liu, H. (2012). Comprehensive carrying capacity of the urban agglomeration in the Yangtze River Delta, China. Habitat International, 36(4), 462–470.
  • Lozano-Oyola, M., Blancas, F. J., González, M., Caballero, R. (2012). Sustainable tourism indicators as planning tools in cultural destinations. Ecological Indicators, 18, 659–675.
  • Miah, S. J., Vu, H. Q., Gammack, J., McGrath, M. (2017). A Big Data analytics method for tourist behaviour analysis. Information and Management, 54, 771–785.
  • Miller, G. (2001). The development of indicators for sustainable tourism: Results of a Delphi survey of tourism researchers. Tourism Management, 22(4), 351–362.
  • Nakajima, E., Ortega, A. (2016). Carrying capacity using energy and a new calculation of the ecological footprint. Journal of Ecological Indicators, 60, 1200–1207.
  • Olmeda, I., Sheldon, P. (2002). Data mining techniques and applications for tourism Internet marketing. Journal of Travel and Tourism Marketing, 11(2–3), 1–20.
  • Papadopoulos, S. I. (1989). Strategy development and implementation of tourism marketing plans: Part 2. European Journal of Marketing, 23(3), 37–47.
  • Papageorgiou, K., Brotherton, I. (1999). A management planning framework based on ecological, perceptual and economic carrying capacity: The case study of Vikos-Aoos National Park, Greece. Journal of Environmental Management, 56(4), 271–284.
  • Ritchie, J. R. B., Crouch, G. I. (2000). Editorial: The competitive destination: A sustainability perspective. Tourism Management, 21(1), 1–7.
  • Slavakis, K., Kim, S.-J., Mateos, G., Giannakis, G. B.(2014). Stochastic approximation vis-à-vis online learning for Big Data analytics. IEEE Signal Processing Magazine, 31(6), 124–129.
  • Smeral, E. (2006). Tourism satellite accounts: A critical assessment. Journal of Travel Research, 45(1), 92–98.
  • Spilanis, I., Vayanni, H. (2004). Sustainable tourism: Utopia or necessity? The role of new forms of tourism in the Aegean Islands. In: B. Bramwell (ed.). Coastal mass tourism: Diversification and sustainable development in southern Europe (pp. 269–291). Bristol: Channel View Publications. ISBN 9781873150689.
  • Srinivasan, U., Arunasalam, B. (2013). Leveraging Big Data analytics to reduce healthcare costs. IT Professional, 15(6), 21–28.
  • Tomigová, K., Mendes, J., Pereira, L. N. (2016). The attractiveness of Portugal as a tourist destination: The perspective of Czech tour operators. Journal of Travel and Tourism Marketing, 33(2), 197–210.
  • Turner, R. K., Pearce, D., Bateman, I. (1994). Environmental economics: An elementary introduction. New York and London: Harvester Wheatsheaf. ISBN 0745010830.
  • Wall-Reinius, S., Ioannides, D., Zampoukos, K. (2017). Does geography matter in all-inclusive resort tourism? An investigation of the marketing approach of major Scandinavian tour operators. Tourism Geographies, 1–19. DOI: 10.1080/14616688.2017.1375975
  • Wong, S. W., Tang, B., van Horen, B. (2006). Strategic urban management in China: A case study of Guangzhou Development District. Habitat International, 30(3), 645–667.
  • Xiang, Z., Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188.
  • Zhao, S., Feng, K., Gui, S., Cai, H., Jin, W., Wu, C. (2013). The emergy ecological footprint for small fish farm in China. Ecological Indicators, 29, 62–67.
  • Zheng, V. W., Zheng, Y., Xie, X., Yang, Q. (2010). Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th International Conference on World Wide Web (pp. 1029–1038). New York: ACM. ISBN 9781605587998. DOI: 10.1145/1772690.1772795.
Document Type
Publication order reference
YADDA identifier
bwmeta1.element.desklight-80d71ea1-909a-4cd7-83ac-bd95c706ec59
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