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

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.