Enhancing Broadband Access in Urban Burkina Faso: A GIS and Machine Learning-Based Geomarketing Approach to FTTx Potential and Alternatives

This geomarketing study addresses the challenge of disparate broadband coverage in urban areas of Burkina Faso. Leveraging GIS and unsupervised machine learning techniques, such as hierarchical clustering and polygon segmentation were employed for spatial clustering and prioritization of broadband deployment zones. Through a comprehensive assessment of fiber-to-the-home (FTTH) potential, we introduce a novel scoring mechanism based on standard-of-living variables to estimate household affordability. Our technology planning encompasses diverse options, including FTTH, Fixed Wireless Access (FWA), and wireless mesh networks, adapted to areas with varying potential. Mapping this strategy over two quinquenniums underscores the dynamism of broadband space coverage. Our work offers a data-driven decision-making basis for public and private stakeholders, setting a course for equitable urban broadband expansion. This study not only bridges the digital divide but also advances socio-economic growth, underscoring the transformative impact of informed connectivity strategies in Burkina Faso