Advancing urban geography pedagogy in a South African metropolis through geospatial intelligence and applied spatial modelling

Advancing urban geography pedagogy in a South African metropolis through geospatial intelligence and applied spatial modelling

Tolulope Ayodeji Olatoye & Raymond Nkwenti Fru

Issue: Vol. 7 No. 2 2026 Article 8 pp.114 – 135
DOI : https://doi.org/10.38159/jelt.2026728 Published online 10th March 2026.

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ABSTRACT

This study highlights the transformative potential of geo-spatial analysis and spatial prediction techniques in using the dynamic morphology of the Durban Metropolis as a model for fostering innovative, data-driven learning experiences. The methodology integrates quantitative and geospatial data analysis of Land Use and Land Cover (LULC) changes from 2004 to 2024 using GIS-based change detection tools to identify and predict LULC change patterns in 2034. The study was underpinned by a geospatial-analytical framework in which secondary remote sensing datasets, GIS-based LULC classification outputs, NDVI/NDBI indices, and Markov Chain spatial modelling served as the primary data collection instruments for interpreting urban change through a spatial–theoretical lens. The research findings revealed that built-up areas expanded significantly from 123.21 km² (5.38%) in 2004 to 442.92 km² (19.32%) in 2024, while agricultural lands, dense vegetation, and water bodies steadily declined, signaling ongoing environmental changes and urban pressures that are predicted to intensify to 520.3 km² (22.7%) by 2034. The study concludes that Durban Metropolis is rapidly expanding with a concomitant decline in vegetation LULC, thus highlighting the urgent need for sustainable urban planning and environmental conservation strategies. These findings have profound implications for urban geography pedagogy, providing data-driven insights that enhance curriculum development, equip students with spatial analysis skills, and promote informed decision-making on urban sustainability challenges. This study primarily contributes to the Scholarship of Teaching and Learning in its development and empirical validation of an integrative pedagogical model, and by articulating/testing a new signature pedagogy for urban geography.

Keywords: Education Curriculum; Geography Pedagogy; Geo-Spatial Analysis; Land Use Land Cover (LULC); Spatial Intelligence.

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AUTHOR(S)

Dr. Tolulope Ayodeji Olatoye is a Lecturer and Post-Doctoral Research Fellow in Geography at Sol Plaatje University, Kimberley, South Africa. He has a PhD degree in Geography and Environmental Science from the University of Fort Hare, South Africa, and two Masters degrees in Geographic Information Systems (GIS) and Geography respectively from Nigeria’s Premier University of Ibadan. Dr. Olatoye brings extensive research expertise in urban ecosystem conservation, coastal vegetation management, and sustainable forest practices. With over two decades of professional experience, Dr. Olatoye has held academic, research, and leadership roles. He has over 20 research publications in internationally accredited journals, book chapter publications and presented original research articles in over 20 international conferences. Recognized with scholarships and travel awards, he has contributed to high-impact projects and secured significant research grants such as the Andrew Mellon funded Project on Spatial Humanities and the NRF/NRF/DSI Global Change Programme of South Africa.

Dr. Raymond Nkwenti Fru is currently the Head of Department, Human Science Teaching at the Faculty of Education, Sol Plaatje University, Kimberley. South Africa. He is a Senior lecturer in History and Social Science Education. His research interest is in the area of school textbook analysis and other contentious and controversial discourses in History/Social Science Education.

TO CITE ARTICLE

Olatoye, Tolulope Ayodeji, and Raymond Nkwenti Fru. “Advancing Urban Geography Pedagogy in a South African Metropolis Through Geospatial Intelligence and Applied Spatial Modelling” Journal of Education and Learning Technology 7, no.2 (2026): 114 – 135. https://doi.org/10.38159/jelt.2026728