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

2025-12-01

AI Summary

In sub-Saharan Africa’s rapidly urbanizing context, informal settlements suffer from accessibility deprivation due to deficient road connectivity. Method: This study develops a Building Connectivity Deprivation Index (BCDI) that jointly quantifies road accessibility and surface quality, enabling three-tier classification of accessibility deprivation across urban built-up areas. The methodology integrates high-resolution open geospatial data, machine learning modeling, and community-based crowdsourced validation—balancing scalability with interpretability. Contribution/Results: Applied to Nairobi, Lagos, and Kano, the BCDI reveals that most built-up areas experience low-to-moderate deprivation; the model achieves an F1-score exceeding 0.74 for low-deprivation area identification, confirming its validity. As a reusable, empirically grounded metric, the BCDI supports spatial inequality analysis and evidence-based, inclusive transport planning in data-scarce urban environments.

Technology Category

• Humans and AI: AI for Accessibility
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Modeling road access deprivation in Sub-Saharan African cities.
• Combining a new accessibility metric with road quality data.
• Classifying urban areas into low, medium, or high deprivation levels.

Innovation

• Combines new accessibility metric with road surface type data
• Classifies urban areas into low, medium, or high deprivation levels
• Uses open geospatial datasets for scalable, interpretable modeling