Towards Modeling Road Access Deprivation in Sub-Saharan Africa Based on a New Accessibility Metric and Road Quality

TL;DR

This paper introduces a new model combining an accessibility metric and road quality data to classify urban areas by road access deprivation in Sub-Saharan Africa, aiding urban planning in data-scarce regions.

Contribution

The study develops and applies a novel, scalable model for assessing road access deprivation using open geospatial data and community validation in African cities.

Findings

High accuracy in identifying low deprivation areas (F1 > 0.74)

Moderate accuracy for medium deprivation (F1 > 0.52) in Nairobi and Lagos

Variable results for high deprivation (F1 0.26 to 0.69)

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.