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Towards a Spatial Measure of SDG 11.1.1: Open Data for Urban Deprivation Mapping
Author(s) -
Sai Ganesh Veeravalli,
Florencio Campomanes V,
Sebastian Hafner,
Stefanos Georganos,
Monika Kuffer,
John Friesen,
Dana R Thomson,
Robert Ndugwa,
Dennis Mwaniki,
Angela Abascal,
Peter Elias,
Tobi Eniolu Morakinyo,
Julio Pedrassoli,
Gabriel de Oliveira,
Anthony Boanada-Fuchs,
Boris Zerjav,
Juan Manuel D'Attoli
Publication year - 2025
Publication title -
2025 joint urban remote sensing event (jurse)
Language(s) - English
Resource type - Conference proceedings
eISSN - 2642-9535
ISBN - 979-8-3503-7183-3
DOI - 10.1109/jurse60372.2025.11076033
Subject(s) - computing and processing , geoscience , signal processing and analysis , transportation
Urban deprivation mapping is critical for addressing inequalities and achieving Sustainable Development Goal (SDG) 11.1.1, which focuses on ensuring access to adequate housing and services in urban areas. This study introduces a geospatial framework to operationalize previously conceptualized urban Domains of Deprivation related to unplanned urbanization, limited infrastructure, and limited services within city segments at the city-scale. Leveraging open, global datasets, including Google’s V3 building footprints and 2.5D building heights, the model assigns deprivation scores (ranging from 0 to 6) based on binary thresholds derived from median values. Validation against reference slum boundaries provided by the IDEAMAPS network achieved an F1- score of 0.45 for high-deprivation areas. The results highlight the spatial distribution of deprivation across Nairobi and demonstrate the reliability of dense building indicators for identifying informal settlements. The framework demonstrates computational efficiency, enabling citywide analysis using accessible resources, and highlights its potential to inform urban planning and targeted interventions through scalable geospatial methodologies aligned with SDG 11.1.1.

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