This work is licensed under the Creative Commons Attribution 4.0 International License.
Alieva, D., Holgado, D., de Juan, S., Ruiz-Frau, A., Villasante, S., & Maya-Jariego, I. (2022). Assessing landscape features and ecosystem services of marine protected areas through photographs on social media: comparison of two archipelagos in Spain. Environment, Development and Sustainability, 24(7), 9623–9641. https://doi.org/10.1007/s10668-021-01841-yAlievaD.HolgadoD.de JuanS.Ruiz-FrauA.VillasanteS.Maya-JariegoI. (2022). Assessing landscape features and ecosystem services of marine protected areas through photographs on social media: comparison of two archipelagos in Spain. Environment, Development and Sustainability, 24(7), 9623–9641. https://doi.org/10.1007/s10668-021-01841-ySearch in Google Scholar
Al-Sultany, G. A. (2018). Semantic based geotagged photos similarities for location’s ranking purposes. Journal of Engineering and Applied Sciences, 13(18), 7716–7720. https://doi.org/10.3923/jeasci.2018.7716.7720Al-SultanyG. A. (2018). Semantic based geotagged photos similarities for location’s ranking purposes. Journal of Engineering and Applied Sciences, 13(18), 7716–7720. https://doi.org/10.3923/jeasci.2018.7716.7720Search in Google Scholar
Arkema, K. K., Fisher, D. M., Wyatt, K., Wood, S. A., & Payne, H. J. (2021). Advancing sustainable development and protected area management with social media‐based tourism data. Sustainability, 13(5), 1–19. https://doi.org/10.3390/su13052427ArkemaK. K.FisherD. M.WyattK.WoodS. A.PayneH. J. (2021). Advancing sustainable development and protected area management with social media‐based tourism data. Sustainability, 13(5), 1–19. https://doi.org/10.3390/su13052427Search in Google Scholar
Balińska, A. (2020). City break jako forma turystyki miejskiej [City break as a form of urban tourism]. Zeszyty Naukowe Małopolskiej Wyższej Szkoły Ekonomicznej w Tarnowie, 46(2), 85–95. https://doi.org/10.25944/znmwse.2020.02.8595BalińskaA. (2020). City break jako forma turystyki miejskiej [City break as a form of urban tourism]. Zeszyty Naukowe Małopolskiej Wyższej Szkoły Ekonomicznej w Tarnowie, 46(2), 85–95. https://doi.org/10.25944/znmwse.2020.02.8595Search in Google Scholar
Barros, C, Moya-Gómez, B., & García-Palomares, J. C. (2019). Identifying temporal patterns of visitors to national parks through geotagged photographs. Sustainability, 11(24), 1-16. https://doi.org/10.3390/su11246983BarrosCMoya-GómezB.García-PalomaresJ. C. (2019). Identifying temporal patterns of visitors to national parks through geotagged photographs. Sustainability, 11(24), 1–16. https://doi.org/10.3390/su11246983Search in Google Scholar
Barros, C., Moya-Gómez, B., & Gutiérrez, J. (2020). Using geotagged photographs and GPS tracks from social networks to analyse visitor behaviour in national parks. Current Issues in Tourism, 23(10), 1291–1310. https://doi.org/10.1080/13683 500.2019.1619674BarrosC.Moya-GómezB.GutiérrezJ. (2020). Using geotagged photographs and GPS tracks from social networks to analyse visitor behaviour in national parks. Current Issues in Tourism, 23(10), 1291–1310. https://doi.org/10.1080/13683500.2019.1619674Search in Google Scholar
Bettaieb, B., & Wakabayashi, Y. (2021). Comparison of the areas of interest in Central Tokyo among visitors by rountry of residence using geotagged photographs. Geographical Review of Japan Series B, 93(2), 66–75. https://doi.org/10.4157/GEOGRE-VJAPANB.93.66BettaiebB.WakabayashiY. (2021). Comparison of the areas of interest in Central Tokyo among visitors by rountry of residence using geotagged photographs. Geographical Review of Japan Series B, 93(2), 66–75. https://doi.org/10.4157/GEOGRE-VJAPANB.93.66Search in Google Scholar
Broz, M. (2022). Flickr Statistics, User Count, & Facts (September 2022). Photutorial. https://pho-tutorial.com/flickr-statistics/BrozM. (2022). Flickr Statistics, User Count, & Facts (September 2022). Photutorial. https://pho-tutorial.com/flickr-statistics/Search in Google Scholar
Cai, G., Hio, C., Bermingham, L., Lee, K., & Lee, I. (2014, January 6-9). Mining Frequent Trajectory Patterns and Regions-of-Interest from Flickr Photos [Conference Paper]. 2014 47th Hawaii International Conference on System Sciences, Waikoloa, United States of America. https://doi.org/10.1109/HICSS.2014.188CaiG.HioC.BerminghamL.LeeK.LeeI. (2014, January6-9). Mining Frequent Trajectory Patterns and Regions-of-Interest from Flickr Photos [Conference Paper]. 2014 47th Hawaii International Conference on System Sciences, Waikoloa, United States of America. https://doi.org/10.1109/HICSS.2014.188Search in Google Scholar
Caldeira, A. M., & Kastenholz, E. (2018). Tourists’ spatial behaviour in urban destinations: The effect of prior destination experience. Journal of Vacation Marketing, 24(3), 247–260. https://doi.org/10.1177/1356766717706102CaldeiraA. M.KastenholzE. (2018). Tourists’ spatial behaviour in urban destinations: The effect of prior destination experience. Journal of Vacation Marketing, 24(3), 247–260. https://doi.org/10.1177/1356766717706102Search in Google Scholar
Chen, M., Arribas-Bel, D., & Singleton, A. (2019a). Understanding the dynamics of urban areas of interest through volunteered geographic information. Journal of Geographical Systems, 21(1), 89–109. https://doi.org/10.1007/s10109-018-0284-3ChenM.Arribas-BelD.SingletonA. (2019a). Understanding the dynamics of urban areas of interest through volunteered geographic information. Journal of Geographical Systems, 21(1), 89–109. https://doi.org/10.1007/s10109-018-0284-3Search in Google Scholar
Chen, W., Xu, Z., Zheng, X., & Luo, Y. (2019b). Geotagged photo metadata processing method for Beijing inbound tourism flow. ISPRS International Journal of Geo-Information, 8(12), 1-16. https://doi.org/10.3390/ijgi8120556ChenW.XuZ.ZhengX.LuoY. (2019b). Geotagged photo metadata processing method for Beijing inbound tourism flow. ISPRS International Journal of Geo-Information, 8(12), 1–16. https://doi.org/10.3390/ijgi8120556Search in Google Scholar
De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., & Yu, C. (2010, June 13-16). Automatic construction of travel itineraries using social breadcrumbs [Conference Paper]. HT’10 - 21st ACM Conference on Hypertext and Hypermedia, Toronto, Canada. https://doi.org/10.1145/1810617.1810626De ChoudhuryM.FeldmanM.Amer-YahiaS.GolbandiN.LempelR.YuC. (2010, June13-16). Automatic construction of travel itineraries using social breadcrumbs [Conference Paper]. HT’10-21st ACM Conference on Hypertext and Hypermedia, Toronto, Canada. https://doi.org/10.1145/1810617.1810626Search in Google Scholar
Derdouri, A., & Osaragi, T. (2021). A machine learning-based approach for classifying tourists and locals using geotagged photos: the case of Tokyo. Information Technology and Tourism, 23(4), 575–609. https://doi.org/10.1007/s40558-021-00208-3DerdouriA.OsaragiT. (2021). A machine learning-based approach for classifying tourists and locals using geotagged photos: the case of Tokyo. Information Technology and Tourism, 23(4), 575–609. https://doi.org/10.1007/s40558-021-00208-3Search in Google Scholar
Domènech, A., Mohino, I., & Moya-Gómez, B. (2020). Using Flickr geotagged photos to estimate visitor trajectories in world heritage cities. ISPRS International Journal of Geo-Information, 9(11), 1-28. https://doi.org/10.3390/ijgi9110646DomènechA.MohinoI.Moya-GómezB. (2020). Using Flickr geotagged photos to estimate visitor trajectories in world heritage cities. ISPRS International Journal of Geo-Information, 9(11), 1–28. https://doi.org/10.3390/ijgi9110646Search in Google Scholar
Giglio, S., Bertacchini, F., Bilotta, E., & Pantano, P. (2020). Machine learning and points of interest: typical tourist Italian cities. Current Issues in Tourism, 23(13), 1646–1658. https://doi.org/10.1080/13683500.2019.1637827GiglioS.BertacchiniF.BilottaE.PantanoP. (2020). Machine learning and points of interest: typical tourist Italian cities. Current Issues in Tourism, 23(13), 1646–1658. https://doi.org/10.1080/13683500.2019.1637827Search in Google Scholar
Girardin, F., Dal Fiore, F., Blat, J., & Ratti, C. (2007, November 8-10). Understanding of tourist dynamics from explicitly disclosed location information [Conference Paper]. 4th International Symposium on LBS & TeleCartography, Hong Kong. https://www.researchgate.net/publication/228787929_Understanding_of_ tourist_dynamics_from_explicitly_disclosed_ location_informationGirardinF.Dal FioreF.BlatJ.RattiC. (2007, November8-10). Understanding of tourist dynamics from explicitly disclosed location information [Conference Paper]. 4th International Symposium on LBS & TeleCartography, Hong Kong. https://www.researchgate.net/publication/228787929_Understanding_of_tourist_dynamics_from_explicitly_disclosed_location_informationSearch in Google Scholar
Girardin, F., Dal Fiore, F., Ratti, C., & Blat, J. (2008). Leveraging explicitly disclosed location information to understand tourist dynamics: a case study. Journal of Location Based Services, 2(1), 41–56. https://doi.org/10.1080/17489720802261138GirardinF.Dal FioreF.RattiC.BlatJ. (2008). Leveraging explicitly disclosed location information to understand tourist dynamics: a case study. Journal of Location Based Services, 2(1), 41–56. https://doi.org/10.1080/17489720802261138Search in Google Scholar
Hawelka, 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. https://doi.org/10.1080/15230406.2014.890072HawelkaB.SitkoI.BeinatE.SobolevskyS.KazakopoulosP.RattiC. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271. https://doi.org/10.1080/1523040 6.2014.890072Search in Google Scholar
Heikinheimo, V., Järv, O., Tenkanen, H., Hiippala, T., & Toivonen, T. (2022). Detecting country of residence from social media data: a comparison of methods. International Journal of Geographical Information Science, 36(10), 1931–1952. https://doi.org/10.1080/13658816.2022.2044484HeikinheimoV.JärvO.TenkanenH.HiippalaT.ToivonenT. (2022). Detecting country of residence from social media data: a comparison of methods. International Journal of Geographical Information Science, 36(10), 1931–1952. https://doi.org/10.1080/13658816.2022.2044484Search in Google Scholar
Hollenstein, L., & Purves, R. S. (2010). Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science, 1(2010), 21–48. https://doi.org/10.5311/JOSIS.2010.1.3HollensteinL.PurvesR. S. (2010). Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science, 1(2010), 21–48. https://doi.org/10.5311/JOSIS.2010.1.3Search in Google Scholar
Höpken, W., Müller, M., Fuchs, M., & Lexhagen, M. (2020). Flickr data for analysing tourists’ spatial behaviour and movement patterns: A comparison of clustering techniques. Journal of Hospitality and Tourism Technology, 11(1), 69–82. https://doi.org/10.1108/JHTT-08-2017-0059HöpkenW.MüllerM.FuchsM.LexhagenM. (2020). Flickr data for analysing tourists’ spatial behaviour and movement patterns: A comparison of clustering techniques. Journal of Hospitality and Tourism Technology, 11(1), 69–82. https://doi.org/10.1108/JHTT-08-2017-0059Search in Google Scholar
Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., & Prasad, S. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54(2015), 240–254. https://doi.org/10.1016/j. compenvurbsys.2015.09.001HuY.GaoS.JanowiczK.YuB.LiW.PrasadS. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54(2015), 240–254. https://doi.org/10.1016/j.compenvurbsys.2015.09.001Search in Google Scholar
Jing, C., Dong, M., Du, M., Zhu, Y., & Fu, J. (2020). Fine-grained spatiotemporal dynamics of inbound tourists based on geotagged photos: A case study in Beijing, China. IEEE Access, 8(2020), 28735–28745. https://doi.org/10.1109/ACCESS.2020.2972309JingC.DongM.DuM.ZhuY.FuJ. (2020). Fine-grained spatiotemporal dynamics of inbound tourists based on geotagged photos: A case study in Beijing, China. IEEE Access, 8(2020), 28735–28745. https://doi.org/10.1109/ACCESS.2020.2972309Search in Google Scholar
Kádár, B. (2014). Measuring tourist activities in cities using geotagged photography. Tourism Geographies, 16(1), 88–104. https://doi.org/10.1080/14616688.2013.868029KádárB. (2014). Measuring tourist activities in cities using geotagged photography. Tourism Geographies, 16(1), 88–104. https://doi.org/10.1080/14616688.2013.868029Search in Google Scholar
Kádár, B., & Gede, M. (2013). Where do tourists go? Visualizing and analysing the spatial distribution of geotagged photography. Cartographica: The International Journal for Geographic Information and Geovisualization, 48(2), 78–88. https://doi.org/10.3138/carto.48.2.1839KádárB.GedeM. (2013). Where do tourists go? Visualizing and analysing the spatial distribution of geotagged photography. Cartographica: The International Journal for Geographic Information and Geovisualization, 48(2), 78–88. https://doi.org/10.3138/carto.48.2.1839Search in Google Scholar
Kádár, B., & Gede, M. (2021). Tourism flows in large-scale destination systems. Annals of Tourism Research, 87(2021), 1-16. https://doi.org/10.1016/j.annals.2020.103113KádárB.GedeM. (2021). Tourism flows in large-scale destination systems. Annals of Tourism Research, 87(2021), 1–16. https://doi.org/10.1016/j.annals.2020.103113Search in Google Scholar
Kádár, B., & Gede, M. (2022). The measurable predominance of weekend trips in established tourism regions—The case of visitors from Budapest at waterside destinations. Sustainability, 14(6), 1-16. https://doi.org/10.3390/su14063293KádárB.GedeM. (2022). The measurable predominance of weekend trips in established tourism regions—The case of visitors from Budapest at waterside destinations. Sustainability, 14(6), 1–16. https://doi.org/10.3390/su14063293Search in Google Scholar
Lim, K. H., Chan, J., Leckie, C., & Karunasekera, S. (2018). Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowledge and Information Systems, 54(2), 375–406. https://doi.org/10.1007/s10115-017-1056-yLimK. H.ChanJ.LeckieC.KarunasekeraS. (2018). Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowledge and Information Systems, 54(2), 375–406. https://doi.org/10.1007/s10115-017-1056-ySearch in Google Scholar
Luo, F., Cao, G., Mulligan, K., & Li, X. (2016). Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago. Applied Geography, 70(2016), 11–25. https://doi.org/10.1016/j. apgeog.2016.03.001LuoF.CaoG.MulliganK.LiX. (2016). Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago. Applied Geography, 70(2016), 11–25. https://doi.org/10.1016/j.apgeog.2016.03.001Search in Google Scholar
Mariani, M., & Baggio, R. (2022). Big data and analytics in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 34(1), 231–278. https://doi.org/10.1108/IJCHM-03-2021-0301MarianiM.BaggioR. (2022). Big data and analytics in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 34(1), 231–278. https://doi.org/10.1108/IJCHM-03-2021-0301Search in Google Scholar
Martins, M. R., da Costa, R. A., & Moreira, A. C. (2022). Backpackers’ space–time behavior in an urban destination: The impact of travel information sources. International Journal of Tourism Research, 24(3), 456–471. https://doi.org/10.1002/jtr.2514MartinsM. R.da CostaR. A.MoreiraA. C. (2022). Backpackers’ space–time behavior in an urban destination: The impact of travel information sources. International Journal of Tourism Research, 24(3), 456–471. https://doi.org/10.1002/jtr.2514Search in Google Scholar
Martins, M., & Costa, R. (2022). Tracking technologies in tourism: A bibliometric and content review. In J. V. de Carvalho, P. Liberato & A. Peña (Eds.), Advances in Tourism, Technology and Systems (pp. 215–230). Springer Nature Singapore.MartinsM.CostaR. (2022). Tracking technologies in tourism: A bibliometric and content review. In de CarvalhoJ. V.LiberatoP.PeñaA. (Eds.), Advances in Tourism, Technology and Systems (pp. 215–230). Springer Nature Singapore.Search in Google Scholar
Merry, K., & Bettinger, P. (2019). Smartphone GPS accuracy study in an urban environment. PLoS ONE, 14(7), 1–19. https://doi.org/10.1371/journal.pone.0219890MerryK.BettingerP. (2019). Smartphone GPS accuracy study in an urban environment. PLoS ONE, 14(7), 1–19. https://doi.org/10.1371/journal.pone.0219890Search in Google Scholar
Miyasaka, T., Oba, A., Akasaka, M., & Tsuchiya, T. (2018). Sampling limitations in using tourists’ mobile phones for GPS-based visitor monitoring. Journal of Leisure Research, 49(3–5), 298–310. https://doi.org/10.1080/00222216.2018.1542526MiyasakaT.ObaA.AkasakaM.TsuchiyaT. (2018). Sampling limitations in using tourists’ mobile phones for GPS-based visitor monitoring. Journal of Leisure Research, 49(3–5), 298–310. https://doi.org/10.1080/00222216.2018.1542526Search in Google Scholar
Önder, I. (2017). Classifying multi-destination trips in Austria with big data. Tourism Management Perspectives, 21(2017), 54–58. https://doi.org/10.1016/j.tmp.2016.11.002ÖnderI. (2017). Classifying multi-destination trips in Austria with big data. Tourism Management Perspectives, 21(2017), 54–58. https://doi.org/10.1016/j.tmp.2016.11.002Search in Google Scholar
Önder, I., Koerbitz, W., & Hubmann-Haidvogel, A. (2016). Tracing tourists by their digital footprints: The case of Austria. Journal of Travel Research, 55(5), 566–573. https://doi.org/10.1177/0047287514563985ÖnderI.KoerbitzW.Hubmann-HaidvogelA. (2016). Tracing tourists by their digital footprints: The case of Austria. Journal of Travel Research, 55(5), 566–573. https://doi.org/10.1177/0047287514563985Search in Google Scholar
Padrón-Ávila, H., & Hernández-Martín, R. (2020). How can researchers track tourists? A bibliometric content analysis of tourist tracking techniques. European Journal of Tourism Research, 26(2020), 1–30. https://doi.org/10.54055/ejtr.v26i.1932Padrón-ÁvilaH.Hernández-MartínR. (2020). How can researchers track tourists? A bibliometric content analysis of tourist tracking techniques. European Journal of Tourism Research, 26(2020), 1–30. https://doi.org/10.54055/ejtr.v26i.1932Search in Google Scholar
Pereira, F. C., Vaccari, A., Giardin, F., Chiu, C., & Ratti, C. (2011). Crowdsensing in the Web: Analyzing the citizen experience in the urban space. In M. Foth, L. Forlano, C. Satchell, & M. Gibbs (Eds.), From Social Butterfly to Engaged Citizen: Urban Informatics, Social Media, Ubiquitous Computing, and Mobile Technology to Support Citizen Engagement. The MIT Press. https://doi.org/10.7551/mitpress/8744.003.0029PereiraF. C.VaccariA.GiardinF.ChiuC.RattiC. (2011). Crowdsensing in the Web: Analyzing the citizen experience in the urban space. In FothM.ForlanoL.SatchellC.GibbsM. (Eds.), From Social Butterfly to Engaged Citizen: Urban Informatics, Social Media, Ubiquitous Computing, and Mobile Technology to Support Citizen Engagement. The MIT Press. https://doi.org/10.7551/mitpress/8744.003.0029Search in Google Scholar
Salas-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(2018), 13–25. https://doi.org/10.1016/j.tourman.2017.11.001Salas-OlmedoM. H.Moya-GómezB.García-PalomaresJ. C.GutiérrezJ. (2018). Tourists’ digital footprint in cities: Comparing Big Data sources. Tourism Management, 66(2018), 13–25. https://doi.org/10.1016/j.tourman.2017.11.001Search in Google Scholar
Sarkar, J. L., & Majumder, A. (2021). A new point-of-interest approach based on multi-itinerary recommendation engine. Expert Systems with Applications, 181(2021), 115026. https://doi.org/10.1016/j.eswa.2021.115026SarkarJ. L.MajumderA. (2021). A new point-of-interest approach based on multi-itinerary recommendation engine. Expert Systems with Applications, 181(2021), 115026. https://doi.org/10.1016/j.eswa.2021.115026Search in Google Scholar
Shoval, N., Schvimer, Y., & Tamir, M. (2018). Tracking technologies and urban analysis: Adding the emotional dimension. Cities, 72 (Part A), 34–42. https://doi.org/10.1016/j.cities.2017.08.005ShovalN.SchvimerY.TamirM. (2018). Tracking technologies and urban analysis: Adding the emotional dimension. Cities, 72 (Part A), 34–42. https://doi.org/10.1016/j.cities.2017.08.005Search in Google Scholar
Solazzo, G., Maruccia, Y., Lorenzo, G., Ndou, V., Del Vecchio, P., & Elia, G. (2022). Extracting insights from big social data for smarter tourism destination management. Measuring Business Excellence, 26(1), 122–140. https://doi.org/10.1108/MBE-11-2020-0156SolazzoG.MarucciaY.LorenzoG.NdouV.Del VecchioP.EliaG. (2022). Extracting insights from big social data for smarter tourism destination management. Measuring Business Excellence, 26(1), 122–140. https://doi.org/10.1108/MBE-11-2020-0156Search in Google Scholar
Sottini, V. A., Barbierato, E., Bernetti, I., & Capecchi, I. (2021). Impact of climate change on wine tourism: An approach through social media data. Sustainability, 13(13), 1-18. https://doi.org/10.3390/su13137489SottiniV. A.BarbieratoE.BernettiI.CapecchiI. (2021). Impact of climate change on wine tourism: An approach through social media data. Sustainability, 13(13), 1–18. https://doi.org/10.3390/su13137489Search in Google Scholar
Spyrou, E., Korakakis, M., Charalampidis, V., Psallas, A., & Mylonas, P. (2017). A geo-clustering approach for the detection of areas-of-interest and their underlying semantics. Algorithms, 10(1), 1-22. https://doi.org/10.3390/a10010035SpyrouE.KorakakisM.CharalampidisV.PsallasA.MylonasP. (2017). A geo-clustering approach for the detection of areas-of-interest and their underlying semantics. Algorithms, 10(1), 1–22. https://doi.org/10.3390/a10010035Search in Google Scholar
Stepchenkova, S., & Zhan, F. (2013). Visual destination images of Peru: Comparative content analysis of DMO and user-generated photography. Tourism Management, 36(2013), 590–601. https://doi.org/10.1016/j.tourman.2012.08.006StepchenkovaS.ZhanF. (2013). Visual destination images of Peru: Comparative content analysis of DMO and user-generated photography. Tourism Management, 36(2013), 590–601. https://doi.org/10.1016/j.tourman.2012.08.006Search in Google Scholar
Straumann, R. K., Çöltekin, A., & Andrienko, G. (2014). Towards (re)constructing narratives from georeferenced photographs through visual analytics. Cartographic Journal, 51(2), 152–165. https://doi.org/10.1179/1743277414Y.0000000079StraumannR. K.ÇöltekinA.AndrienkoG. (2014). Towards (re)constructing narratives from georeferenced photographs through visual analytics. Cartographic Journal, 51(2), 152–165. https://doi.org/10.1179/174327741 4Y.0000000079Search in Google Scholar
Sun, X., Huang, Z., Peng, X., Chen, Y., & Liu, Y. (2019). Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data. International Journal of Digital Earth, 12(6), 661–678. https://doi.org/10.1080/17538947.2018.1471104SunX.HuangZ.PengX.ChenY.LiuY. (2019). Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data. International Journal of Digital Earth, 12(6), 661–678. https://doi.org/10.1080/17538947.2018.1471104Search in Google Scholar
Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., & Li, L.-J. (2016). YFCC100M: the new data in multimedia research. Communications of the ACM, 59(2), 64–73. https://doi.org/10.1145/2812802ThomeeB.ShammaD. A.FriedlandG.ElizaldeB.NiK.PolandD.BorthD.LiL.-J. (2016). YFCC100M: the new data in multimedia research. Communications of the ACM, 59(2), 64–73. https://doi.org/10.1145/2812802Search in Google Scholar
UNWTO. (2007). UNWTO Metadata project: Common Glossary. http://statistics.unwto.org/sites/all/files/docpdf/glossary.pdfUNWTO. (2007). UNWTO Metadata project: Common Glossary. http://statistics.unwto.org/sites/all/files/docpdf/glossary.pdfSearch in Google Scholar
TravelBI. (2023). Taxa de Sazonalidade [Seasonality Rate]. https://travelbi.turismodeportugal.pt/sustentabi-lidade/taxa-de-sazonalidade/TravelBI. (2023). Taxa de Sazonalidade [Seasonality Rate]. https://travelbi.turismodeportugal.pt/sustentabi-lidade/taxa-de-sazonalidade/Search in Google Scholar
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(2013), 1-7. https://doi.org/10.1038/srep02976WoodS. A.GuerryA. D.SilverJ. M.LacayoM. (2013). Using social media to quantify naturebased tourism and recreation. Scientific Reports, 3(2013), 1–7. https://doi.org/10.1038/srep02976Search in Google Scholar
Yan, Y., Eckle, M., Kuo, C.-L., Herfort, B., Fan, H., & Zipf, A. (2017). Monitoring and assessing post-disaster tourism recovery using geotagged social media data. ISPRS International Journal of GeoInformation, 6(5), 1-17. https://doi.org/10.3390/ijgi6050144YanY.EckleM.KuoC.-L.HerfortB.FanH.ZipfA. (2017). Monitoring and assessing post-disaster tourism recovery using geotagged social media data. ISPRS International Journal of Geo-Information, 6(5), 1–17. https://doi.org/10.3390/ijgi6050144Search in Google Scholar
Yun, H. J., & Park, M. H. (2014). Time–space movement of festival visitors in rural areas using a smart phone application. Asia Pacific Journal of Tourism Research, 20(11), 1–20. https://doi.org/10.1080/10941665.2014.976581YunH. J.ParkM. H. (2014). Time–space movement of festival visitors in rural areas using a smart phone application. Asia Pacific Journal of Tourism Research, 20(11), 1–20. https://doi.org/10.1080/10941665.2014.976581Search in Google Scholar
Zhou, X., Xu, C., & Kimmons, B. (2015). Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Computers, Environment and Urban Systems, 54(2015), 144–153. https://doi.org/10.1016/j.compenvurb-sys.2015.07.006ZhouX.XuC.KimmonsB. (2015). Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Computers, Environment and Urban Systems, 54(2015), 144–153. https://doi.org/10.1016/j.compenvurb-sys.2015.07.006Search in Google Scholar