References
- 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, 2011, ‘Mobile positioning’ in Mobile Methods, eds M Büscher, J Urry & K Witchger, Routledge, New York, pp. 183–199.
- 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.
- Blondel, VD, Decuyper, A & Krings, G 2015, ‘A survey of results on mobile phone datasets analysis’, EPJ DataScience, vol. 4, p. 10.
- 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.
- Cierpiał-Wolan, M (ed.) 2020, ‘Tourism in 2019’, Główny Urząd Statystyczny, Warszawa, Rzeszów.
- Cierpiał-Wolan, M (ed.) 2021, ‘Tourism in 2020’, Główny Urząd Statystyczny, Warszawa, Rzeszów.
- Covid-19 Mobility Project in Germany 2022, Mobility monitor. Available from: <
https://www.covid-19-mobility.org/ >. [12 December 2021]. - 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.
- GSMA Intelligence 2021, Research & Analysis, Available from: <
https://www.gsmaintelligence.com >. [15 December 2021]. - Guilford, JP 1965, Fundamental Statistics in Psychology and Education, McGraw-Hill, New York.
- 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.
- Halloran, ME, Vespignani, A, Bharti, N, Feldstein, LR, Alexander, KA, Ferrari, M, Shaman, J, Drake, JM, Porco, T, Eisenberg, JNS, Del Valle, SY, Lofgren, E, Scarpino, SV, Eisenberg, MC, Gao, D, Hyman, JM, Eubank, S & Longini, IM 2014, ‘Ebola: Mobility data’, Science, vol. 346(6208), p. 433.
- 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.
- ILO 2020, COVID-19 and the world of work: Updated estimates and analysis. Available from: <
https://www.ilo.org/ >. [13 December 2021]. - 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.
- Lu, X, Bengtsson, L & Holme, P 2012, ‘Predictability of population displacement after the 2010 Haiti earthquake’, PNAS, pp. 11576–11581.
- Lynch, C & Roper, C 2011, ‘The Transit Phase of Migration: Circulation of Malaria and Its Multidrug-Resistant Forms in Africa’, PLoS Med, vol. 8(5).
- Medicover 2021, Koronawirus [Coronavirus]. Available from: <
www.medicover.pl/koronawirus/statystyki/ >. [30 June 2021] - 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.
- Nielsen, NC 2011, ‘Tourist Mobility and Advanced Tracking Technologies’, Tourism Management, vol. 32, pp. 461–462.
- 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.
- Raun, J, Ahas, R & Tiru, M 2016, ‘Measuring tourism destinations using mobile tracking data’, Tourism Management, vol. 57, pp. 202–212.
- 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.
- Song, C, Qu, Z, Blumm, N & Barabási, AL 2010, ‘Limits of predictability in human mobility’, Science, vol. 327, pp. 1018–1021.
- 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.
- Vazquez-Prokopec, GM, Bisanzio, D, Stoddard, ST, Paz-Soldan, V & Morrison, AC 2013, ‘Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment’, PLoS ONE, vol. 8, no. 4, e58802.
- 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.
- Zhang, H, Song, H, Wen, L, Liu, C 2021, ‘Forecasting tourism recovery amid COVID-19’, Annals of Tourism Research, vol. 87, pp. 103–149.