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Variability of mobile phone network logins in the Białowieża National Park during the 2019 and 2020 summer holiday periods in the context of the COVID-19 pandemic
Aguiléra, V, Allio, S, Benezech, V, Combes, F & Milion, C 2014, ‘Using cell phone data to measure quality of service and passenger flows of Paris transit system’, Transportation Research Part C: Emerging Technologies, vol. 43, pp. 198–211.
Ahas, R, Aasa, A, Mark, Ü, Pae, T, Kull, T 2007, ‘Seasonal tourism spaces in Estonia: Case study with mobile positioning data’, Tourism Management, vol. 28, no. 3, pp. 898–910.
Ahas, R, Aasa, A, Roosea, A, Mark, Ü, Silm, S 2008a, ‘Evaluating passive mobile positioning data for tourism surveys: An Estonian case study’, Tourism Management, vol. 29, no. 3, pp. 469–486.
Ahas, R, Saluveer, E, Tiru, M, Silm, S 2008b, ‘Mobile Positioning Based Tourism Monitoring System: Positium Barometer’ in Information and Communication Technologies in Tourism, eds P O’Connor, W Höpken & U Gretzel, Springer Computer Science. Springer-Verlag, pp. 475–485, Innsbruck.
Ahas, R, Silm, S, Järv, O, Saluveer, E & Tiru, M 2010, ‘Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones’, Journal of Urban Technology, vol. 17, pp. 3–27.
Bekhor, S, Cohen, Y & Solomon, C 2013, ‘Evaluating long-distance travel patterns in Israel by tracking cellular phone positions’, Journal of Advanced Transportation, vol. 47, pp. 435–446.
Bel, F, Lacroix, A, Lyser, S, Rambonilaza, T & Turpin, N 2014, ‘Domestic demand for tourism in rural areas: Insights from summer stays in three French regions’, Tourism Management, vol. 46, pp. 562–570.
Bengtsson, L, Lu, X, Thorson, A, Garfield, R & von Schreeb, J 2011, ‘Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: apost-earthquake geospatial study in Haiti’, PLoS Med, vol. 8, no 8.
Bertocchi, D, , Camatti, N & van der Borg, J 2021, ‘Tourism Peaks on the Three Peaks. Using big data to monitor where, when and how many visitors impact the Dolomites UNESCO World Heritage Site’, Rivista Geografica Italiana - Open Access, vol. 3, pp. 59–81.
Bogoch, II, Brady, OJ, Kraemer, MU, German, M, Creatore, MI, Kulkarni, MA, Brownstein, JS, Mekaru, SR, Hay, SI, Groot, E, Watts, A & Khan, K 2016, ‘Anticipating the international spread of Zika virus from Brazil’, Lancet, vol. 387(10016), pp. 335–336.
Bojic, I, Massaro, E, Belyi, A, Sobolevsky, S & Ratti, C 2015, ‘Choosing the Right Home Location Definition Method for the given Dataset’, in Social Informatics, Proceedings of the 7th International Conference, SocInfo 2015, eds T-Y Liu, C Scollon & W Zhu, Springer International Publishing, Beijing, pp. 194–208.
Calabrese, F, Ferrari, L & Blondel, VD 2015, ‘Urban sensing using mobile phone network data: a survey of research’, ACM ComputSurveys, vol. 47, pp. 1–20.
Data Analytics@IFISC 2020, Mobility reduction in Spain after the adoption of COVID confinement measures. Available from: <https://analytics.ifisc.uib-csic.es>. [28 March 2021].
Demissie, MG, Phithakkitnukoon, S, Sukhvi-bul, T, Antunes, F, Gomes, R & Bento, C 2016, ‘Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal’, IEEE Transactions on Intelligent Transportation Systems, vol. 17, pp. 2466–2478.
Gao, S, Rao, J, Kang, Y, Liang, Y & Kruse, J 2020, ‘Mapping county-level mobility pattern changes in the United States in response to COVID-19’, SSRN, 2 April. Available from: <https://papers.ssrn.com/>. [9 October 2021].
Goodchild, MF 2007, ‘Citizens as voluntary sensors: Spatial data infrastructure in the world of web 2.0’, International Journal of Spatial Data Infrastructures Research, vol. 2, pp. 24–32.
Grinberger, AY, Shoval, N & McKercher, B 2014, ‘Typologies of tourists’ time–space consumption: a new approach using GPS data and GIS tools’, Tourism Geographies, vol. 16, pp. 105–123.
Hallo, JC, Beeco, JA, Goetcheus, C, McGee, J, McGehee, NG & Norman, WC 2012, ‘GPS as a Method for Assessing Spatial and Temporal Use Distributions of Nature-Based Tourists’, Journal of Travel Research, vol. 51, pp. 591–606.
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, vol. 41, pp. 260–271.
Kalinowski, S, Wyduba, W. 2020, ‘Moja sytuacja w okresie koronawirusa. Raport końcowy z badan [My situation in the coronavirus period. The final report of the study]’, Wyd. IRWiR PAN, Warszawa.
Khalid, U, Okafor, LE, Burzynska, K 2021, ‘Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic?’, Current Issues in Tourism, vol. 24, no. 19, pp. 2801–2820.
Kuusik, A, Ahas, R, Tiru, M 2009, ‘Analysing Repeat Visitation on Country Level with Passive Mobile Positioning Method: an Estonian Case Study’, Estonian Discussions on Economic Policy, vol. 17, pp. 140–155.
Kuusik, A, Nilbe, K, Mehine, T, Ahas, R 2014, ‘Country as a free sample: the ability of tourism events to generate repeat visits. Case study with mobile positioning data in Estonia’,
Lama, R, Rai, A 2021, ‘Challenges in developing sustainable tourism post COVID-19 pandemic’ in Tourism destination management in a post-pandemic context (Tourism security-safety and post conflict destinations) eds VG Gowreesunkar, SW Maingi, H Roy and R Micera, Emerald Publishing
Larijani, AN, Olteanu-Raimond, AM, Perret, J, Brédif, M & Ziemlicki, C 2015, ‘Investigating the mobile phone data to estimate the origin destination flow and analysis; case study: Paris region’, Transportation Research Procedia, vol. 6, pp. 64–78.
Montjoye, YA, Hidalgo, CA, Verleysen, M & Blondel, VD 2013, ‘Unique in the Crowd: The privacy bounds of human mobility’, Science Report, vol. 3, p. 1376.
Nilbe, K, Ahas, R & Silm, S 2014, ‘Evaluating the Travel Distances of Events Visitors and Regular Visitors Using Mobile Positioning Data: The Case of Estonia’, Journal of Urban Technology, vol. 21, pp. 91–107.
Od 1G do 5G, czyli historia technologii mobilnej [From 1G to 5G, or the history of mobile technology] 2020. Available from: <https://www.orange.pl/poradnik/siec-komorkowa/od-1g-do-5g-czyli-historia-technologii-mobilnej>. [4 March 2020].
Oliver, N, Lepri, B, Sterly, H, Lambiotte, R, Deletaille, S, De Nadai, M, Letouzé, E, Salah, AA, Benjamins, R, Cattuto, C, Colizza, V, de Cordes, N, Fraiberger, SP, Koebe, T, Lehmann, S, Murillo, J, Pentland, A, Pham, PN, Pivetta, F, Saramäki, J, Scarpino, SV, Tizzoni, M, Verhulst, S & Vinck, P 2020, ‘Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle’, Science Advances, vol. 6, no. 23.
Panigutti, C, Tizzoni, M, Bajardi, P, Smoreda, Z & Colizza, V 2017, ‘Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models’, Royal Society Open Science, 17 May. Available from: <https://royalsocietypublishing.org/>. [1 December 2021].
Poletto, C, Pelat, C, Levy-Bruhl, D, Yazdanpanah, Y, Boelle, PY & Colizza, V 2014, ‘Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV)epidemic in the Middle East and risk ofinternational spread using a novel maximum likelihood analysis approach’, Eurosurveillance, vol. 19, no. 23, p. 23.
Pullano, G, Valdano, E, Scarpa, N, Rubrichi, S & Colizza, V 2020, ‘Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study’, The Lancet Digital Health, vol. 2, pp. e638–e649.
Purcell, WM, Burns, O, Voss, A 2021, ‘COVID-19 and sustainable tourism’ in COVID-19: Paving the Way for a More Sustainable World, eds W Leal Filho, World Sustainability Series. Springer, Cham.
Ricciato, F, Widhalm, P, Craglia, M & Pantisano, F 2015, ‘Estimating Population Density Distribution from Network-based Mobile Phone Data’, Joint Research Centre.
Sakarovitch, B, Bellefon, M, Givord, P & Vanhoof, M 2018, ‘Estimating the Residential Population from Mobile Phone Data, an Initial Exploration’, Economie et Statistique, vol. 505–506, pp. 109–132.
Saluveer, E, Raunb, J, Tirua M, Altinb L, Kroonc, J, Snitsarenkoc, T, Aasab, A, Silmb, S 2020, ‘Methodological framework for producing national tourismstatistics from mobile positioning data’, Annals of Tourism Research, vol. 81, 102895.
Silm, S & Ahas, R 2010, ‘The Seasonal Variability of Population in Estonian Municipalities’, Environment and Planning A: Economy and Space, vol. 42, pp. 2527–2546.
Smoreda, Z, Olteanu-Raimond, AM & Couronné, T 2013, ‘Spatiotemporal data from mobile phones for personal mobility assessment’ in Transport Survey Methods: Best Practice for Decision Making, eds J Zmud, Emerald Group Publishing Limited, Bingley, pp. 745–768.
Stanisz, A 2006, Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny, Tom 1. Statystyki podstawowe [An accessible course in statistics from STATISTICA PL on examples from medicine, Vo. 1. Basic statistics], StatSoft Polska, Kraków.
Steenbruggen, J, Tranos, E & Nijkamp, P 2014, ‘Data from mobile phone operators: A tool for smarter cities?’, Telecommunications Policy, vol. 39, pp. 335–346.
Steenbruggen, J, Tranos, E & Rietveld, P 2016, ‘Can Motorway Traffic Incidents be detected by Mobile Phone Usage Data? An Empirical Application in the Netherlands’, Journal of Transport Geography, vol. 54, pp. 81–90.
Tatem, AJ, Qiu, Y, Smith, DL, Sabot, O, Ali, AS & Moonen, B 2009, ‘The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents’, Malaria Journal, vol. 8(287).
The Data Against Corona Taskforce 2020, Covid-19: Belgium analyses telecom data to measure the impact of confinement. 2020. Available from:<https://press.telenet.be>. [16 July 2021].
Tizzoni, M, Bajardi, P, Decuyper, A, Kon Kam King, G, Schneider, CM, Blondel, V, Smoreda, Z, González, MC & Colizza, V 2014, ‘On the use of human mobility proxy for the modeling of epidemics’, PLoS Computational Biology, vol. 10, no. 7.
UKE 2021, Raport o stanie rynku telekomunikacyjnego w Polsce w 2020 r. [Report on the condition of the telecommunications market in Poland in 2020] Available from: UKE [30 June 2021].
Vanhoof, M, Combes, S & de Bellefon, MP 2017, ‘Mining mobile phone data to detect urban areas’, Proceedings of the Conference of the Italian Statistical Society, eds A Petrucci & R. Verde, Firenze University Press, Florence.
Vanhoof, M, Hendrickx, L, Puussaar, A, Verstraeten, G, Ploetz, T & Smoreda, Z 2017, ‘Exploring the use of mobile phone data for domestic tourism trip analysis’, Netcom, vol. 31, pp. 335 – 372.
Wang, X, Lai, IKW, Zhou, Q, Pang, YH 2021, ‘Regional travel as an alternative form of tourism during the COVID-19 pandemic: impacts of a low-risk perception and perceived benefits’ International Journal of Environmental Research and Public Health, vol. 18, 9422.
Wesolowski, A, Buckee, CO, Bengtsson, L, Wetter, E, Lu, X & Tatem, AJ 2014, ‘Commentary: Containing the Ebola Outbreak - the potential and Challenge of Mobile Network Data, PLoSCurr, 29 September.
Wesolowski, A, Eagle, N, Noor, AM, Snow, RW & Buckee, CO 2013, ‘The impact of biases in mobile phone ownership on estimates of human mobility’, Journal of The Royal Society Interface, vol. 10(81).
Wesolowski, A, Eagle, N, Tatem, AJ, Smith, DL, Noor, AM, Snow, RW & Buckee, CO 2012, ‘Quantifying the impact of human mobility on malaria’, Science, vol. 338, pp. 267–270.
Ye, J, Hu, Q, Ji, P & Barthelemy, M 2020, ‘The effect of interurban movements on the spatial distribution of population in China’, March. Available from: <https://hal-cea.archives-ouvertes.fr>. [20 July 2021].
Zhang, D, Guo, B, Li, B & Yu, Z 2010, Extracting social and community intelligence from digital footprints: an emerging research area, in Ubiquitous Intelligence and Computing, Proceedings of the 7th international conference on Ubiquitous Intelligence and Computing, Berlin, pp. 4–18.