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Analysis of Tourist Behavior in the City Based on Flickr Data: Kielce Case Study
Abstract
Although nowadays tourists generate huge amounts of data online, so-called Big Data, little is known about their behavior in urban spaces. However, these sources of data are increasingly being used with modern technology to track the presence of tourists in urban areas that are attractive to tourists. The article aims to analyze urban tourist behavior using data from the social networking site Flickr. Machine learning methods were used to illustrate the temporal and spatial activities of the portal’s users. It was assumed that these activities could serve as an indicator of the volume of tourist traffic and interest in the urban space. The results of the analysis showed that in most cases the activity of the portal users in the form of the number of georeferenced photos was in line with the actual number of visitors to the most important tourist attractions in Kielce. The study can be seen as a contribution to a new stream of research in the field of digital geography. The limitations of the applied research methodology are also included in the conclusions.
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Ash J., Kitchin R., Leszczynski A. (2018). Introducing Digital Geographies. W: J. Ash, R. Kitchin, S. Leszczynski (Eds.), Digital Geographies (s. 1–10). London: Sage. DOI: https://doi.org/10.4135/9781529793536.n1
Ashworth G.J., Page S.J. (2011). Urban Tourism Research: Recent Progress and Current Paradoxes. Tourism Management, 32(1), 1–15. DOI: https://doi.org/10.1016/j.tourman.2010.02.002
Awati R. (2023). What Is a Heat Map (Heatmap)? Online: https://www.techtarget.com/searchbusinessanalytics/definition/heat-map (dostęp: 20.11.2023).
Bank Danych Lokalnych Głównego Urzędu Statystycznego (BDL GUS). Online: https://bdl.stat.gov.pl/BDL (dostęp: 20.11.2023).
Beiqi S., Zhao J., Chen P.-J. (2016). Exploring Urban Tourism Crowding in Shanghai via Crowdsourcing Geospatial Data. Current Issues in Tourism, 20(11), 1–24. DOI: https://doi.org/10.1080/13683500.2016.1224820
Domènech A., Mohino I., Moya-Gómez B. (2020). Using Flickr Geotagged Photos to Estimate Visitor Trajectories in World Heritage Cities. International Journal of Geo-Information, 9(11). DOI: https://doi.org/10.3390/ijgi9110646
Elwood S., Goodchild M.F., Sui D.Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, 102(3), 571–590. DOI: https://doi.org/10.1080/00045608.2011.595657
García-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: https://doi.org/10.1016/j.apgeog.2015.08.002
Gardzińska A., Mańkowski T., Milewski D., Tokarz A. (2010). Region jako obszar badań systemu informacji turystycznej. W: A. Panasiuk (red.), Informacja turystyczna (s. 79–108). Warszawa: C.H. Beck.
Gągolewski M., Bartoszuk M., Cena A. (2016). Przetwarzanie i analiza danych w języku Python. Warszawa: Wydawnictwo Naukowe PWN.
Girardin F., Calabrese F., Dal Fiore F., Ratti C., Blat J. (2009). Digital Footprinting: Uncovering Tourists with User-Generated Content. Pervasive Computing, IEEE Pervasive Computing, 7(4), 36–43. DOI: https://doi.org/10.1109/MPRV.2008.71
Gołembski G. (red.). (2011). Sposoby mierzenia i uwarunkowania rozwoju funkcji turystycznej miasta. Przykład Poznania. Poznań: Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu.
Goodchild M.F. (2007). Citizens as Sensors: The World of Volunteered Geography. GeoJournal, 69(4), 211–221. DOI: https://doi.org/10.1007/s10708-007-9111-y
Grzyb M. (2019). Grupowanie gęstościowe. Algorytm DBSCAN – teoria. Online: https://mateuszgrzyb.pl/grupowanie-gestosciowe-dbscan-teoria (dostęp: 20.11.2023).
Han S., Ren F., Wu C., Chen Y., Du Q., Ye X. (2018). Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data. ISPRS International Journal of Geo-Information, 7(4). DOI: https://doi.org/10.3390/ijgi7040158
Hickey R.J. (1996). Noise Modelling and Evaluating Learning from Examples. Artificial Intelligence, 82(1–2), 157–179. DOI: https://doi.org/10.1016/0004-3702(94)00094-8
Jażdżewska I. (red.). (2008). XXI Konwersatorium Wiedzy o Mieście. Funkcja turystyczna miast. Łódź: Wydawnictwo Uniwersytetu Łódzkiego.
Kowalczyk A. (2001). Geografia turyzmu. Warszawa: Wydawnictwo Naukowe PWN.
Kurek W. (red.). (2012). Turystyka. Warszawa: Wydawnictwo Naukowe PWN.
Lijewski T., Mikułowski B., Wyrzykowski J. (1985). Geografia turystyki Polski. Warszawa: Państwowe Wydawnictwo Ekonomiczne.
Liszewski S. (1999). Przestrzeń turystyczna miasta (przykład Łodzi). Turyzm, 9(1), 51–73. DOI: https://doi.org/10.18778/0867-5856.9.1.04
Matczak A. (1989). Problemy badania funkcji turystycznej miast Polski. Acta Universitatis Lodziensis. Turyzm, (5), 25–37. DOI: https://doi.org/10.18778/0860-1119.5.03
Otwarte Dane (2023). Rejestr zabytków nieruchomych. Online: https://dane.gov.pl/pl/dataset/1130,rejestr-zabytkow-nieruchomych (dostęp: 20.11.2023).
Page S. (1995). Urban Tourism. London–New York: Routledge.
Palaniappan S. (2023). On Doing a Thorough Exploratory Data Analysis with Pandas in Python. Online: https://medium.com/@drsamypal/on-doing-a-thorough-exploratory-data-analysis-with-pandas-in-python-b210c8c69d88 (dostęp: 20.11.2023).
Panourgias C. (2023). Clustering with DBSCAN. Online: https://medium.com/@panourgiaschris/clustering-with-dbscan-b573a6056ad1 (dostęp: 20.11.2023).
Rahmadian R., Feitosa D., Zwitter A. (2021). A Systematic Literature Review on the Use of Big Data for Sustainable Tourism. Current Issues in Tourism, 25(11), 1–20. DOI: https://doi.org/10.1080/13683500.2021.1974358
Rath S.R. (2022). Traffic Sign Recognition Using PyTorch and Deep Learning. Online: https://debuggercafe.com/traffic-sign-recognition-using-pytorch-and-deep-learning (dostęp: 20.11.2023).
Rodrigo J.A., Ortiz J.E. (2021). Skforecast: Time Series Forecasting with Python and Scikit-Learn. Online: https://cienciadedatos.net/documentos/py27-time-series-forecasting-python-scikitlearn.html (dostęp: 20.11.2023).
Rogacewicz B. (2018). Machine Learning – przydatne narzędzia i biblioteki. Online: https://nofluffjobs.com/pl/log/wiedza-it/machine-learning-przydatne-narzedzia-i-biblioteki (dostęp: 20.11.2023).
Sinclair M., Ghermandi A., Sheela A.M. (2018). A Crowdsourced Valuation of Recreational Ecosystem Services Using Social Media Data: An Application to Tropical Wetland in India. Science of the Total Environment, 642, 356–365. DOI: https://doi.org/10.1016/j.scitotenv.2018.06.056
Strzyż M. (2018). Wyżyna Kielecka (342.3). W: A. Richling, J. Solon, A. Macias, J. Balon, J. Borzyszkowski, M. Kistowski (red.), Regionalna geografia fizyczna Polski (s. 421). Poznań: Bogucki Wydawnictwo Naukowe.
Warszyńska J., Jackowski A. (1978). Podstawy geografii turyzmu. Warszawa: PWN.
Wilkins E.J., Wood S.A., Smith J.W. (2020). Uses and Limitations of Social Media to Inform Visitor Use Management in Parks and Protected Areas: A Systematic Review. Environmental Management, 67, 120–132. DOI: https://doi.org/10.1007/s00267-020-01373-7
Wood S.A., Guerry A.D., Silver J.M., Lacayo M. (2013). Using Social Media to Quantify Nature-Based Tourism and Recreation. Scientific Reports, 3. DOI: https://doi.org/10.1038/srep02976
Zhong L., Sun S., Law R. (2019). Movement Patterns of Tourists. Tourism Management, 75, 318–322. DOI: https://doi.org/10.1016/j.tourman.2019.05.015
DOI: http://dx.doi.org/10.17951/b.2024.79.0.17-31
Date of publication: 2024-04-19 10:37:17
Date of submission: 2023-12-06 15:29:29
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Copyright (c) 2024 Andrzej Tucki, Jacek Osojca, Dariusz Dobrowolski
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