Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls
Abstract African cities, at the nexus of rapid urbanisation and escalating climate change, urgently require innovative approaches for climate-adaptive Green Infrastructure (GI) planning. While Spatio-temporal Big Data (STBD) offers powerful analytical capabilities, its integration with the rich, con...
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Springer
2025-08-01
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| Series: | Discover Cities |
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| Online Access: | https://doi.org/10.1007/s44327-025-00105-y |
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| author | Desmond Gagakuma |
| author_facet | Desmond Gagakuma |
| author_sort | Desmond Gagakuma |
| collection | DOAJ |
| description | Abstract African cities, at the nexus of rapid urbanisation and escalating climate change, urgently require innovative approaches for climate-adaptive Green Infrastructure (GI) planning. While Spatio-temporal Big Data (STBD) offers powerful analytical capabilities, its integration with the rich, context-specific insights of Local and Indigenous Knowledge (LK/IK) remains a critical, underexplored frontier. This study critically examines the imperatives, pathways, and pitfalls of synergising STBD and LK/IK for enhanced GI planning in urban Africa. It argues that transcending the limitations of relying solely on either technology-driven data or localised knowledge requires fostering a dynamic interplay between them. This study maps out tangible methodological, technological, institutional, and ethical pathways for effective knowledge co-production. Simultaneously, it critically assesses significant pitfalls, including deep-seated epistemological divides, pervasive power asymmetries, resource constraints, and the dangers of tokenism that can undermine genuine integration. Ultimately, this study contends that fostering this synergy is not merely beneficial but essential for developing technically sound, socially equitable, culturally appropriate, and ultimately, more sustainable GI solutions in African urban contexts, thereby contributing to resilient and just urban futures. |
| format | Article |
| id | doaj-art-918c22995e034aed8658613835bfd88c |
| institution | Kabale University |
| issn | 3004-8311 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Cities |
| spelling | doaj-art-918c22995e034aed8658613835bfd88c2025-08-20T04:01:47ZengSpringerDiscover Cities3004-83112025-08-012112810.1007/s44327-025-00105-ySynergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfallsDesmond Gagakuma0Social Equity Research Centre, RMIT UniversityAbstract African cities, at the nexus of rapid urbanisation and escalating climate change, urgently require innovative approaches for climate-adaptive Green Infrastructure (GI) planning. While Spatio-temporal Big Data (STBD) offers powerful analytical capabilities, its integration with the rich, context-specific insights of Local and Indigenous Knowledge (LK/IK) remains a critical, underexplored frontier. This study critically examines the imperatives, pathways, and pitfalls of synergising STBD and LK/IK for enhanced GI planning in urban Africa. It argues that transcending the limitations of relying solely on either technology-driven data or localised knowledge requires fostering a dynamic interplay between them. This study maps out tangible methodological, technological, institutional, and ethical pathways for effective knowledge co-production. Simultaneously, it critically assesses significant pitfalls, including deep-seated epistemological divides, pervasive power asymmetries, resource constraints, and the dangers of tokenism that can undermine genuine integration. Ultimately, this study contends that fostering this synergy is not merely beneficial but essential for developing technically sound, socially equitable, culturally appropriate, and ultimately, more sustainable GI solutions in African urban contexts, thereby contributing to resilient and just urban futures.https://doi.org/10.1007/s44327-025-00105-ySpatio-temporal Big DataLocal KnowledgeIndigenous KnowledgeClimate ChangeGreen InfrastructureSustainable Development |
| spellingShingle | Desmond Gagakuma Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls Discover Cities Spatio-temporal Big Data Local Knowledge Indigenous Knowledge Climate Change Green Infrastructure Sustainable Development |
| title | Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls |
| title_full | Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls |
| title_fullStr | Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls |
| title_full_unstemmed | Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls |
| title_short | Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls |
| title_sort | synergising spatio temporal big data and local knowledge for climate adaptive green infrastructure planning in urban africa pathways and pitfalls |
| topic | Spatio-temporal Big Data Local Knowledge Indigenous Knowledge Climate Change Green Infrastructure Sustainable Development |
| url | https://doi.org/10.1007/s44327-025-00105-y |
| work_keys_str_mv | AT desmondgagakuma synergisingspatiotemporalbigdataandlocalknowledgeforclimateadaptivegreeninfrastructureplanninginurbanafricapathwaysandpitfalls |