Detecting Livelihoods: The Use of Earth Observation for Livelihood Mapping in Kenya

Livelihood mapping can play a crucial role in understanding and addressing poverty and development challenges. This study presents a novel methodology for high-resolution livelihood mapping using openly available geospatial data and a Kohonen Self-Organizing Map clustering algorithm. The study focuses on Kenya and aims to address three research objectives: (1) determine whether publicly available Earth Observation data can be effectively used to identify different livelihood zones in an asset-based approach, (2) develop a data-driven workflow for delineation of different livelihood zones within Kenya and (3) explore the value of this methodology for the future of socio-economic data collection. This study uses five different geospatial datasets to effectively create 25 clusters of livelihoods at the sub-county level in Kenya. Comparison with expert-created livelihood zones from 2011 and Demographic and Health Surveys (DHS) data further validate the approach's ability to differentiate livelihood patterns. The methodology also exhibits a significant correlation with wealth measures, providing deeper insights into poverty dynamics and offering potential for informing development strategies. Despite limitations this methodology presents a cost-effective and timely method of regularly updating livelihood mapping. By bridging the gap between high-resolution and large-scale livelihood mapping, this study contributes to the advancement of socio-economic research and poverty alleviation efforts

Evaluating Geospatial Data Adequacy for Integrated Risk Assessments: A Malaria Risk Use Case

International policy and humanitarian guidance emphasize the need for precise, subnational malaria risk assessments with cross-regional comparability. Spatially explicit indicator-based assessments can support humanitarian aid organizations in identifying and localizing vulnerable populations for scaling resources and prioritizing aid delivery. However, the reliability of these assessments is often uncertain due to data quality issues. This article introduces a data evaluation framework to assist risk modelers in evaluating data adequacy. We operationalize the concept of “data adequacy” by considering “quality by design” (suitability) and “quality of conformance” (reliability). Based on a use case we developed in collaboration with Médecins Sans Frontières, we assessed data sources popular in spatial malaria risk assessments and related domains, including data from the Malaria Atlas Project, a healthcare facility database, WorldPop population counts, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation estimates, European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation forecast, and Armed Conflict Location and Event Data Project (ACLED) conflict events data. Our findings indicate that data availability is generally not a bottleneck, and data producers effectively communicate contextual information pertaining to sources, methodology, limitations and uncertainties. However, determining such data’s adequacy definitively for supporting humanitarian intervention planning remains challenging due to potential inaccuracies, incompleteness or outdatedness that are difficult to quantify. Nevertheless, the data hold value for awareness raising, advocacy and recognizing trends and patterns valuable for humanitarian contexts. We contribute a domain-agnostic, systematic approach to geodata adequacy evaluation, with the aim of enhancing geospatial risk assessments, facilitating evidence-based decisions