Abstract
Air travel has now become one of the most commonly used modes of transportation across the world due to its ease of access, faster commute, and reasonable costs. Its increasing demand has made it possible to achieve connectivity to nearly every part of the world, with a growing number of direct flights to major cities. Studying the network of flight routes through social network analysis (SNA) helps us determine the airports that are significant players in the industry. By calculating the clustering coefficient and the average shortest path, we can ascertain that the world airport network (WAN) has the characteristics of a small-world network. In contrast, some regional networks possessed features of both small-world and scale-free networks. Previous studies conducted have primarily focused on complex air networks in a particular region. What sets our study apart is the use of a large dataset to analyse the properties of air transport across various parts of the world. Our aim through this project was to better understand the characteristics and patterns of air transport around the world. We used various measures of SNA to arrive at our output, which included a comparison of regional airport networks, their importance in the network, and influence airports have on WAN. The tools used for analysis were designed with Python and the network handling package Networkx.
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References
Azzam M, Klingauf U, Zock A (2013) The accelerated growth of the worldwide air transportation network. Eur Phys J Spec Top 215:35–48. https://doi.org/10.1140/epjst/e2013-01713-7
Bagler G (2008) Analysis of the airport network of India as a complex weighted network. Phys A Stat Mech its Appl 387:2972–2980. https://doi.org/10.1016/j.physa.2008.01.077
Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A (2010) Modeling the spatial spread of infectious diseases: the global epidemic and mobility computational model. J Comput Sci 1:132–145. https://doi.org/10.1016/j.jocs.2010.07.002
Barbasi AL, Albert R (1999) Emergence of scaling in random networks. Science (80-.) 286:509–512
Batagelj V, Mrvar A (1999) Pajek—program for large network analysis. Connnections 21:47–57
Borgatti SP, Everett MG, Freeman LC (2002) UCINET for Windows: software for social network analysis
Brockmann D, Helbing D (2013) The hidden geometry of complex, network-driven contagion phenomena. Science (80-.) 342:1337–1342. https://doi.org/10.1126/science.1245200
Burghouwt G, Hakfoort J (2001) The evolution of the European aviation network, 1990–1998. J Air Transp Manag 7:311–318. https://doi.org/10.1016/S0969-6997(01)00024-2
Cai KQ, Zhang J, Du WB, Cao XB (2012) Analysis of the Chinese air route network as a complex network. Phys B Chin. https://doi.org/10.1088/1674-1056/21/2/028903
Chen G, Wang X, Li X (2015) Network topologies: basic models and properties. Fundam Complex Netw 9:99. https://doi.org/10.1002/9781118718124.ch3
Cheung DP, Gunes MH (2012) A complex network analysis of the United States air transportation. In: Proceedings of 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp 699–701. https://doi.org/10.1109/ASONAM.2012.116
Colizza V, Barrat A, Barthélemy M, Vespignani A (2006) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci USA 103:2015–2020. https://doi.org/10.1073/pnas.0510525103
Cristureanu CBA (2007) Airports driving economic and tourism development. Rom Econ J 25:31–44
Da Rocha LEC (2009) Structural evolution of the Brazilian airport network. J Stat Mech Theory Exp. https://doi.org/10.1088/1742-5468/2009/04/P04020
Gegov E, Postorino MN, Atherton M, Gobet F (2013) Community structure detection in the evolution of the United States airport network. Adv Complex Syst 16:1–21. https://doi.org/10.1142/S0219525913500033
Guaullamet MP (2018) A complex network approach to the Argentinian Airport Network. https://doi.org/10.13140/RG.2.2.33946.54723
Guida M, Maria F (2007) Topology of the Italian airport network: a scale-free small-world network with a fractal structure? Chaos Solitons Fractals 31:527–536. https://doi.org/10.1016/j.chaos.2006.02.007
Hagberg A, Swart P, Schult D (2008) Exploring network structure, dynamics and function using NetworkX. Proc. Scipy 08.
Heer J, Card SK, Landay JA (2005) Prefuse: a toolkit for interactive information visualization Jeffrey. In: Proceedings of the SIGCHI conference on human factors in computing systems, p 421. https://doi.org/10.1145/1054972.1055031
Holme P (2015) Modern temporal network theory: a colloquium. Phys J B Eur. https://doi.org/10.1140/epjb/e2015-60657-4
Hossain M, Alam S, Rees T, Abbass H (2013) Australian airport network robustness analysis: a complex network approach. Australasian Transport Research Forum, ATRF 2013—proceedings
Jia T, Qin K, Shan J (2014) An exploratory analysis on the evolution of the US airport network. Phys A Stat Mech its Appl 413:266–279. https://doi.org/10.1016/j.physa.2014.06.067
Kan Z, Hu C, Wang Z, Wang G, Huang X (2010) NetVis: a network security management visualization tool based on treemap. In: Proceedings—2nd International Conference on Advanced Computer Control, ICACC 2010, vol 4, pp 18–21. https://doi.org/10.1109/ICACC.2010.5487236
Li Z, Dawood SRS (2016) World city network in China: a network analysis of air transportation network. Mod Appl Sci 10:213. https://doi.org/10.5539/mas.v10n10p213
Lordan O, Sallan JM, Simo P (2014) Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda. J Transp Geogr 37:112–120. https://doi.org/10.1016/j.jtrangeo.2014.04.015
Madadhain J, Fisher D, Smyth P, White S, Boey Y (2005) Analysis and visualization of network data using JUNG. J Stat Softw 10:1–35
Mastny L, Peterson JA (2001) Traveling light: new paths for international tourism. Worldwatch Inst. 159.
Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256. https://doi.org/10.1137/S003614450342480
Nicolaides C, Cueto-Felgueroso L, González MC, Juanes R (2012) A metric of influential spreading during contagion dynamics through the air transportation network. PLoS ONE 7:1–10. https://doi.org/10.1371/journal.pone.0040961
Paleari S, Redondi R, Malighetti P (2010) A comparative study of airport connectivity in China, Europe and US: which network provides the best service to passengers? Transp Res Part E Logist Transp Rev 46:198–210. https://doi.org/10.1016/j.tre.2009.08.003
Patokallio J (2017) Open flight—airport, airline and route dataset
Rocha LEC (2017) Dynamics of air transport networks: a review from a complex systems perspective. Chin J Aeronaut 30:469–478. https://doi.org/10.1016/j.cja.2016.12.029
Rochat Y (2009) Closeness centrality extended to unconnected graphs: the harmonic centrality index. Appl Soc Netw Anal 117
Ruhnau B (2000) Eigenvector-centrality—a node-centrality. Soc. Networks 22:357–365. https://doi.org/10.1016/S0378-8733(00)00031-9
Saleena P, Swetha PK, Radha D (2018) Analysis and visualization of airport network to strengthen the economy. Int. J. Eng. Technol. 7:708–713. https://doi.org/10.14419/ijet.v7i2.9915
Song MG, Yeo GT (2017) Analysis of the air transport network characteristics of major airports. Asian J Shipp Logist 33:117–125. https://doi.org/10.1016/j.ajsl.2017.09.002
Suau-Sanchez P, Voltes-Dorta A, Rodríguez-Déniz H (2016) The role of London airports in providing connectivity for the UK: Regional dependence on foreign hubs. J Transp Geogr 50:94–104. https://doi.org/10.1016/j.jtrangeo.2014.11.008
Tang J, Mascolo C, Latora V, Fisica D (n.d.) Temporal distance metrics for social network analysis categories and subject descriptors. pp 31–36
Wang J, Mo H, Wang F, Jin F (2011) Exploring the network structure and nodal centrality of China’s air transport network: a complex network approach. J Transp Geogr 19:712–721. https://doi.org/10.1016/j.jtrangeo.2010.08.012
Watts D, Stogats S (1998) Collective dynamics of ‘small-world’ networks Duncan. Lett to Nat. https://doi.org/10.1111/cobi.13031
Whitaker J (2011) The Matplotlib basemap toolkit. https://github.com/matplotlib/basemap.
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Prabhakar, N., Anbarasi, L.J. Exploration of the global air transport network using social network analysis. Soc. Netw. Anal. Min. 11, 26 (2021). https://doi.org/10.1007/s13278-021-00735-1
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DOI: https://doi.org/10.1007/s13278-021-00735-1