Integrating geospatial datasets for urban structure assessment in humanitarian action

Abstract

More than half of the world-population lives in urban areas, with more than 1 billion people lacking basic services and infrastructure. Spatially targeted, data-driven policies are crucial for sustainable urban planning to improve these situations and increase the resilience. Earth observation (EO) can support the process of achieving the SDGs, in particular SDG 11. Aiming at such high-level targets requires a multi-source data environment, defining and extracting suitable EO-based indicators and linking them with socio-economic or environmental data. When embedded in the context of humanitarian response, where physical access to regions is often limited while at the same time, insights on several scales of intervention are key to rapid decisions, the integration of (potentially) heterogeneous datasets requires adequate data assimilation strategies and a good understanding of data quality. This paper investigates the usability of datasets regarding technical and organisational aspects from an application-driven point of view. We suggest a protocol considering various quality dimensions to evaluate via scoring the fitness of multi-source geospatial datasets to integration. The aim is to provide a general orientation towards data assimilability in the context of deriving higher-level indicators, while specific constraints and the need to relativize may occur for concrete use case.