References
- Abilpatta, Y 2023, Visual analysis of changes in European air transport during the COVID-19 pandemic in 2020 from interactive maps. Available from: <
https://www.flowmap.blue/1fBjftwAzfiyQdH-4MKW44hqVpg7VoawtIpLRwusMm5Q >. [16 October 2023]. - Alexander, L, Jiang, S, Murga, M & González, MC 2015, ‘Origin-destination trips by purpose and time of day inferred from mobile phone data’, Transportation Research Part C: Emerging Technologies, vol. 58, pp. 240–250.
- Andrienko, G, Andrienko, N, Jankowski, P, Keim, D, Kraak, M-J, MacEachren, A & Wrobel, S 2007, ‘Geovisual analytics for spatial decision support: Setting the research agenda’, International Journal of Geographical Information Science, vol. 21, no. 8, pp. 839–857.
- Bachir, D, Khodabandehlou, G, Gauthier, V, El Yacoubi & Puchinger, J 2019, ‘Inferring dynamic origin-destination flows by transport mode using mobile phone data’, Transportation Research Part C, 101, 254–275.
- Barczak, A, Dembińska, I, Rozmus, D & Szopik-Depczyńska, K 2022, ‘The impact of COVID-19 pandemic on air transport passenger markets: Implications for selected EU airports based on time series models analysis’, Sustainability, vol. 14, no. 7, pp. 43–45.
- Barvir, R & Vozenilek, V 2020, ‘Developing versatile graphic map load metrics’, ISPRS International Journal of Geo-Information, vol. 9, no. 12.
- Bogoch, II, Watts, A, Thomas-Bachli, A, Huber, C, Kraemer, MU & Khan, K 2020, ‘Potential for global spread of a novel coronavirus from China’, Journal of Travel Medicine, vol. 27, article number taaa011.
- Bowen, J & Rodrigue, J-P 2020, ‘Air transport’ in The geography of transport systems, ed. J-P Rodrigue, CRC Press, pp. 153–157. Available from: <
https://transportgeography.org/?page_id=1765 >. [20 May 2024]. - Boyandin, I, Bertini, E, Bak, P & Lalanne, D 2011, ‘Flowstrates: An approach for visual exploration of temporal origin-destination data’, Computer Graphics Forum, vol. 30, no. 3, pp. 971–980.
- Caceres, N, Wideberg, JP & Benitez, FG 2007, ‘Deriving origin-destination data from a mobile phone network’, IET Intelligent Transport Systems, vol. 1, no. 1, pp. 15–26.
- Gössling, S 2020, ‘Risks, resilience, and pathways to sustainable aviation: A COVID-19 perspective’, Journal of Air Transport Management, vol. 89, article number 101933.
- Guo, D 2009, ‘Flow mapping and multivariate visualization of large spatial interaction data’, IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1041–1048.
- Guo, D & Zhu, X 2014, ‘Origin-destination flow data smoothing and mapping’, IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 2043–2052.
- Guo, D, Zhu, X, Jin, H, Gao, P & Andris, C 2012, ‘Discovering spatial patterns in origin-destination mobility data’, Transactions in GIS, vol. 16, no. 3, pp. 411–429.
- Halás, M, Kraft, S & Klapka, P 2020, ‘Global spatial organisation of air transport: The definition of functional airline regions’, The Geographical Journal, vol. 186, no. 1, pp. 2–15.
- Huang, Z, Ling, X, Wang, P, Zhang, F, Mao, Y, Lin, T & Wang, FY 2018, ‘Modeling real-time human mobility based on mobile phone and transportation data fusion’, Transportation Research Part C: Emerging Technologies, vol. 96, pp. 251–269.
- International Air Transport Association 2020, Air Connectivity. Measuring the Connections that Drive Economic Growth. IATA. Available from: <
https://www.iata.org/en/iata-repository/publications/economic-reports/air-connectivity-measuring-the-connections-that-drive-economic-growth/ >. [15 June 2021]. - International Air Transport Association 2021, The impact of COVID-19 on aviation. IATA. Available from: <
https://airlines.iata.org/news/the-impact-of-covid-19-on-aviation >. [28 February 2021]. - International Civil Aviation Organization 2020, Economic Impacts of COVID-19 on Civil Aviation. Available from: <
https://www.icao.int/sustainability/Pages/Economic-Impacts-of-COVID-19.aspx >. [12 July 2021]. - Iqbal, M.S, Choudhury, CF, Wang, P & González, MC 2014, ‘Development of origin-destination matrices using mobile phone call data’, Transportation Research Part C: Emerging Technologies, vol. 40, pp. 63–74.
- Jenny, B, Stephen, DM, Muehlenhaus, I, Marston, BE, Sharma, R, Zhang, E & Jenny, H 2018, ‘Design principles for origin-destination flow maps’, Cartography and Geographic Information Science, vol. 45, no. 1, pp. 62–75.
- Jia, JS, Lu, X, Yuan, Y, Xu, G, Jia, J & Christakis, NA 2020, ‘Population flow drives spatio-temporal distribution of COVID-19 in China’, Nature, vol. 582, no. 7812, pp. 389–394.
- Koylu, C & Guo, D 2017, ‘Design and evaluation of line symbolizations for origin-destination flow maps’, Information Visualization, vol. 16, no. 4, pp. 309–331.
- Miller, S, Moat, HS & Preis, T 2020, ‘Using aircraft location data to estimate current economic activity’, Scientific Reports, vol. 10, pp. 1–7.
- Nielsen, TAS & Hovgesen, HH 2008, ‘Exploratory mapping of commuter flows in England and Wales’, Journal of Transport Geography, vol. 16, no. 2, pp. 90–99.
- Ni, L, Wang, XC & Chen, XM 2018, ‘A spatial econometric model for travel flow analysis and real-world applications with massive mobile phone data’, Transportation Research Part C: Emerging Technologies, vol. 86, pp. 510–526.
- OpenSky Network 2022. Available from: <
https://opensky-network.org/data/datasets#d4 >. [7 July 2023]. - Pinos, J, Vozenílek, V & Pavlíš, O 2020, ‘Automatic Geodata Processing Methods for Real-World City Visualizations in Cities: Skylines’, ISPRS International Journal of Geo-Information, vol. 9, no. 1.
- Popelka, S & Voženílek, V 2013, ‘Specifying of requirements for spatio-temporal data in map by eye-tracking and space-time-cube’ in Proceedings of the International Conference on Graphic and Image Processing (ICGIP 2012) vol. 8768, no. 87684.
- Polishchuk, T, Lemetti, A & Saez, R 2019, ‚Evaluation of flight efficiency for Stockholm Arlanda Airport using OpenSky Network data’ in OpenSky Workshop: Proceedings of the OpenSky Workshop 2019: EPiC series in computing, vol. 67, pp. 13–24.
- Pregi, L, Novotný, L & Gábor, Š 2022, ‘Vizualizácia priestorových procesov pomocou online mapovej aplikácie Flowmap. blue’ [‘Visualization of spatial processes using a web-based mapping application Flowmap.blue’], Kartografické listy, vol. 30, no.1, pp. 21–38.
- Rae, A 2009, ‘From spatial interaction data to spatial interaction information? Geovisualisation and spatial structures of migration from the 2001 UK census’, Computers, Environment and Urban Systems, vol. 33, no. 3, pp. 161–178.
- Slocum, TA, McMaster, RB, Kessler, FC & Howard, HH 2022, Thematic cartography and geovisualization (4th ed.), Boca Raton, FL: CRC Press.
- Strohmeier, M, Olive, X, Lübbe, J, Schäfer, M & Lenders, V 2021, ‘Crowdsourced air traffic data from the OpenSky Network 2019–2020’, Earth System Science Data, vol. 13, pp. 357–366.
- Sun, X, Wandelt, S & Zhang, A 2022, ‘COVID-19 pandemic and air transportation: Summary of recent research, policy consideration and future research directions’, Transportation research interdisciplinary perspectives, vol. 16, article number 100718.
- The World Health Organization 2020, Timeline: WHO’s COVID-19 response. Available from: <
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline >. [15 October 2023]. - The World Health Organization 2023, Coronavirus disease (COVID-19) Weekly Epidemiological Updates and Monthly Operational Updates. Available from: <
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports >. [15 October 2023]. - Tobler, WR 1987, ‘Experiments in migration mapping by computer’ The American Cartographer, vol. 14, no. 2, pp. 155–163.
- Vondráková, A & Vozenílek, V 2016, ‘User issues in geovisualization’ in 16th International Multidisciplinary Scientific Geoconference (SGEM 2016), Albena, Bulgaria, June 30–July 06, 2016, SGEM 2016, Vol. III Book Series: International Multidisciplinary Scientific GeoConference-SGEM, pp. 599–606.
- Wielebski, Ł & Medyńska-Gulij, B 2019, ‘Graphically supported evaluation of mapping techniques used in presenting spatial accessibility’, Cartography and Geographic Information Science, vol. 46, no. 4, pp. 311–333.
- Wielebski, Ł, Medyńska-Gulij, B, Halik, Ł & Dickmann, F 2020, ‚Time, spatial, and descriptive features of pedestrian tracks on set of visualizations’, ISPRS International Journal of Geo-Information, vol. 9, no. 6.
- Wood, J, Dykes, J & Slingsby, A 2010, ‘Visualisation of origins, destinations and flows with OD maps’. The Cartographic Journal, vol. 47, no. 2, pp. 117–129.
- Wood, J, Slingsby, A & Dykes, J 2011, ‘Visualizing the dynamics of London’s bicycle-hire scheme’, Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 46, no. 4, pp. 239–251.
- Yang, Y, Dwyer, T, Goodwin, S & Marriott, K 2016, ‘Many-to-many geographically-embedded flow visualization: An evaluation’, IEEE Transactions on Visualization and Computer Graphics, vol. 23, no.1, pp. 411–420.
- Yang, Y, Dwyer, T, Jenny, B, Marriott, K, Cordeil, M & Chen, H 2018, ‘Origin-destination flow maps in immersive environments’, IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 693–703.
- Zhang, X, Chen, X & Wang, J 2019, ‘A number-based inventory of size-resolved black carbon particle emissions by global civil aviation’, Nature Communications, vol. 10.
- Zhu, X & Guo, D 2014, ‘Mapping large spatial flow data with hierarchical clustering’, Transactions in GIS, vol. 18, no. 3, 421–435.
- Zhu, X, Guo, D, Koylu, C & Chen, Ch 2019, ‘Density-based multi-scale flow mapping and generalization’, Computers, Environment and Urban Systems, vol. 77, article number 101359.