The structure and evolution of the African air transport network

Simple item page
dc.contributor.advisorEr, Sebnem
dc.contributor.advisorBritz, Stefan
dc.contributor.authorSnaddon, David
dc.date.accessioned2026-01-28T08:13:48Z
dc.date.available2026-01-28T08:13:48Z
dc.date.issued2025
dc.date.updated2026-01-28T08:11:43Z
dc.description.abstractThis dissertation studies the structure and evolution of the African Air Transport Network (AATN) from 2015 to 2023. With respect to the network's structure, a power-law distribution appropriately characterises the networks degree distribution. The ‘golden tri-angle' between Johannesburg O.R. Tambo, Cape Town, and Durban King Shaka plays a dominant role in the network's structure when considering airline seat capacity. Addis Ababa shows strong growth in node centrality, ending the period with the highest centrality across all observed centrality measures. Community detection reveals airport clusters that align with geographic regions, including African subregions and countries. k-coredecomposition reveals a growing core of the network spread across the continent with a higher concentration in West Africa. Longitudinal trends of network-wide indicators de-tail the network's evolution. A gradual decrease in the average clustering coefficient and degree assortativity coefficient but an increase in the Gini coefficient and largest degree suggest that the network aligns to a growing airline hub-and-spoke structure. During this period, the COVID-19 pandemic occurs and significantly affects the network's evolution, particularly in measures related to the sizing of the network, calling for an investigation into the changes from pre-pandemic to the end of recovery phase. When the network recovers, it does not revert back to its pre-pandemic structure completely, adding new routes and not reintroducing some old ones. Moreover, a similar magnitude of variability during the period is found in the global air transport network.
dc.identifier.apacitationSnaddon, D. (2025). <i>The structure and evolution of the African air transport network</i>. (). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/42718en_ZA
dc.identifier.chicagocitationSnaddon, David. <i>"The structure and evolution of the African air transport network."</i> ., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2025. http://hdl.handle.net/11427/42718en_ZA
dc.identifier.citationSnaddon, D. 2025. The structure and evolution of the African air transport network. . University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/42718en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Snaddon, David AB - This dissertation studies the structure and evolution of the African Air Transport Network (AATN) from 2015 to 2023. With respect to the network's structure, a power-law distribution appropriately characterises the networks degree distribution. The ‘golden tri-angle' between Johannesburg O.R. Tambo, Cape Town, and Durban King Shaka plays a dominant role in the network's structure when considering airline seat capacity. Addis Ababa shows strong growth in node centrality, ending the period with the highest centrality across all observed centrality measures. Community detection reveals airport clusters that align with geographic regions, including African subregions and countries. k-coredecomposition reveals a growing core of the network spread across the continent with a higher concentration in West Africa. Longitudinal trends of network-wide indicators de-tail the network's evolution. A gradual decrease in the average clustering coefficient and degree assortativity coefficient but an increase in the Gini coefficient and largest degree suggest that the network aligns to a growing airline hub-and-spoke structure. During this period, the COVID-19 pandemic occurs and significantly affects the network's evolution, particularly in measures related to the sizing of the network, calling for an investigation into the changes from pre-pandemic to the end of recovery phase. When the network recovers, it does not revert back to its pre-pandemic structure completely, adding new routes and not reintroducing some old ones. Moreover, a similar magnitude of variability during the period is found in the global air transport network. DA - 2025 DB - OpenUCT DP - University of Cape Town KW - Network analysis KW - Africa KW - Air transport network LK - https://open.uct.ac.za PB - University of Cape Town PY - 2025 T1 - The structure and evolution of the African air transport network TI - The structure and evolution of the African air transport network UR - http://hdl.handle.net/11427/42718 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/42718
dc.identifier.vancouvercitationSnaddon D. The structure and evolution of the African air transport network. []. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2025 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/42718en_ZA
dc.language.isoen
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Statistical Sciences
dc.publisher.facultyFaculty of Science
dc.publisher.institutionUniversity of Cape Town
dc.subjectNetwork analysis
dc.subjectAfrica
dc.subjectAir transport network
dc.titleThe structure and evolution of the African air transport network
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_sci_2025_snaddon david.pdf
Size:
5.11 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.72 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Masters