Towards an indicator-based morphological informality model for Sub-Saharan Africa using open building footprint and road data (Version 1)

This study addresses the challenge of accurately mapping informal settlements, which are home to over a billion people globally. Current maps often simplify these areas into binary categories, ignoring the nuanced dimensions of deprivation. The research focuses on ”unplanned urbanization,” a key domain in informal settlement mapping, and proposes a method to classify morphological informality into three deprivation levels (low, medium, and high) based on two subdomains: small, dense structures (SDS) and irregular settlement layouts (ISL). The methodology involves analyzing building footprints and road network data using urban morphometrics, clustering these metrics into subdomains with k-means, and validating results with community-sourced reference data. Tested in Nairobi, Kenya, and Lagos, Nigeria, the model achieves good performance (F1 > 65 for indicator maps) but faces challenges in the medium informality class, particularly in Nairobi, where community feedback diverges significantly. Despite an overall accuracy of 48 % for Nairobi and 60 % for Lagos, the model offers a framework for continuous improvement. This work highlights the value of integrating local perspectives into mapping efforts and provides a scalable, transferable approach for identifying levels of morphological informality.