Towards Modeling Road Access Deprivation in Sub-Saharan Africa Based on a New Accessibility Metric and Road Quality
Sebastian Hafner, Qunshan Zhao, Bunmi Alugbin, Kehinde Baruwa, Caleb Cheruiyot, Sabitu Sa'adu Da'u, Xingyi Du, Peter Elias, Helen Elsey, Ryan Engstrom, Serkan Girgin, Diego F. P. Grajales, Esther Judith, Caroline Kabaria, Monika Kuffer, Oluwatoyin Odulana, Francis C. Onyambu, Adenike Shonowo, Dana R. Thomson, Mingyu Zhu, João Porto de Albuquerque
TL;DR
The paper addresses road access deprivation in rapidly urbanizing Sub-Saharan African cities by introducing a distance-agnostic accessibility metric that counts buildings obstructing paths to the nearest motorable road, combined with road-surface data as a proxy for road quality. This framework classifies urban areas into three deprivation levels (low/medium/high) and produces gridded outputs at 100 m resolution for Nairobi, Lagos, and Kano, validated against community-sourced data. Results show strong identification of low deprivation, variable performance for medium deprivation, and city-dependent high-deprivation accuracy; misclassifications are linked to road-surface data quality and model-perception alignment. The approach offers a scalable, interpretable tool for urban planners in data-scarce contexts and supports targeted infrastructure planning, with future work to refine the medium class, account for natural barriers, and leverage Earth observation data.
Abstract
Access to motorable roads is a critical dimension of urban infrastructure, particularly in rapidly urbanizing regions such as Sub-Saharan Africa. Yet, many urban communities, especially those in informal settlements, remain disconnected from road networks. This study presents a road access deprivation model that combines a new accessibility metric, capturing how well buildings are connected to the road network, with road surface type data as a proxy for road quality. These two components together enable the classification of urban areas into low, medium, or high deprivation levels. The model was applied to Nairobi (Kenya), Lagos (Nigeria), and Kano (Nigeria) using open geospatial datasets. Across all three cities, the majority of built-up areas fall into the low and medium road access deprivation levels, while highly deprived areas are comparatively limited. However, the share of highly deprived areas varies substantially, ranging from only 11.8 % in Nairobi to 27.7 % in Kano. Model evaluation against community-sourced validation data indicates good performance for identifying low deprivation areas (F1 > 0.74), moderate accuracy for medium deprivation in Nairobi and Lagos (F1 > 0.52, lower in Kano), and more variable results for high deprivation (F1 ranging from 0.26 in Kano to 0.69 in Nairobi). Furthermore, analysis of grid cells with multiple validations showed strong agreement among community members, with disagreements occurring mainly between adjacent deprivation levels. Finally, we discussed two types of sources for disagreement with community validations: (1) misalignment between the conceptual model and community perceptions, and (2) the operationalization of the conceptual model. In summary, our road access deprivation modeling approach demonstrates promise as a scalable, interpretable tool for identifying disconnected areas and informing urban planning in data-scarce contexts.