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The structure and evolution of the African air transport network
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,...
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| Format: | Thesis |
| Language: | English English |
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Department of Statistical Sciences
2026
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| _version_ | 1867613162440753152 |
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| access_status_str | Open Access |
| author | Snaddon, David |
| author2 | Er, Sebnem |
| author_browse | Er, Sebnem Snaddon, David |
| author_facet | Er, Sebnem Snaddon, David |
| author_sort | Snaddon, David |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/42718 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:31:45.395Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/42718 The structure and evolution of the African air transport network Snaddon, David Er, Sebnem Britz, Stefan Network analysis Africa Air transport network 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. 2026-01-28T08:13:48Z 2026-01-28T08:13:48Z 2025 2026-01-28T08:11:43Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/42718 en eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Network analysis Africa Air transport network Snaddon, David The structure and evolution of the African air transport network |
| thesis_degree_str | Master's |
| title | The structure and evolution of the African air transport network |
| title_full | The structure and evolution of the African air transport network |
| title_fullStr | The structure and evolution of the African air transport network |
| title_full_unstemmed | The structure and evolution of the African air transport network |
| title_short | The structure and evolution of the African air transport network |
| title_sort | structure and evolution of the african air transport network |
| topic | Network analysis Africa Air transport network |
| url | http://hdl.handle.net/11427/42718 |
| work_keys_str_mv | AT snaddondavid thestructureandevolutionoftheafricanairtransportnetwork AT snaddondavid structureandevolutionoftheafricanairtransportnetwork |