Binyam Dele


Session

12-01
16:30
20min
Crowd Mapping for Food Security in South Omo Valley, Ethiopia
Binyam Dele, Yohannes Yehualashet

In the Lower Omo Valley in Ethiopia, a food security crisis is unfolding. According to the Famine Early Warning Systems Network (FEWSNET), the situation in the area has passed ‘stressed’ to reach the ‘crisis’ stage. Against this background, Humanitarian OpenStreetMap Team (HOT) has been supporting a few affected agro-pastoralist communities in mapping the facilities and the natural resources, as part of an effort to manage these resources better and provide evidence to assess the scale of the disaster. The results from a pilot project (2021) demonstrate that the data generated by those affected is more accurate, complete, and locally relevant than authoritative maps or global machine-generated maps. The Crowdmapping For Food Security in Ethiopia (2023) Project aims to support agro-pastoralist communities in mapping the natural resources and facilities they have access to and monitoring the state of such resources. The project further aims at integrating the land user-generated data (in whole or partially, depending on the communities’ decision) into OpenStreetMap. The South Omo Valley area in Ethiopia faces low agricultural productivity due to limited access to land and water resources, which is further compounded by natural disasters like floods and droughts that worsen food insecurity. To address these challenges, #OSM_Ethiopia organized a series of events at three universities in the region, namely Wolaita Soddo University, Arbaminch University, and Jinka University. The events aimed to explore the potential of open-source mapping to create resilient and food-secure communities, focusing on crowd-sourced mapping for food security.

The food security crisis mapping project aimed to improve resource management practices and evaluate the food security crisis in the Lower Omo Valley region of Ethiopia. Volunteers used a customized Sapelli mapping app to collect data in three formats (point, line, and polygon data). The data analysis team conducted a data-cleaning process to ensure consistency and accuracy. The objective of the cleaning process was to eliminate duplicate entries, fill in any missing data values, and standardize the data format. The Food Security Mapping project is an initiative to map food security data in the NYNGATOM district of the OMO zone in Ethiopia. The project is conducted remotely, with OSM Ethiopia mappers and volunteers from around the world contributing their time and skills to map the food security data. To achieve these goals, the project used satellite imagery and other open-source data sources to collect information on crop production, livestock, and other
relevant food security indicators and Create a map of the data collected to provide a visual
representation of the food security situation in the NYNGATOM district. OSM Ethiopia handled the project by collaborating with the University College of London (UCL) Grand Challenges funded project and HOT to ensure the successful implementation of the crowd mapping project for food security in Ethiopia.

Data analysis
Room 3