By Hannah Ker, Data Scientist at MapAction
In March 2020, MapAction and the UN OCHA (Office for the Coordination of Humanitarian Affairs) Centre for Humanitarian Data embarked on a new level of collaboration by sharing a Data Scientist’s time between the two organisations. Both teams had a lot to offer and learn from each other, with MapAction bringing its geospatial expertise to the Centre’s Predictive Analytics (PA) team. Predictive analytics is a form of data science that uses current and historical facts to predict future events. For MapAction, this collaboration also constituted an important aspect of our Moonshot, which sees us transitioning from being a passive data consumer to an organisation that actively contributes to humanitarian datasets.
Looking back on our work over the past year, we can see how this collaboration has benefitted both organisations in many ways, with numerous positive repercussions more widely. Ultimately, the fruits of our joint working are examples of how data science can help to reduce suffering and save lives in humanitarian initiatives.
“Your input leap-frogged us forward. It is amazing to me how quickly we were able to do this together. A round of applause for your work and its contribution to unlocking critically needed aid for Ethiopians.“Josée Poirier, Predictive Analytics Technical Specialist, Centre for Humanitarian Data
In the latter part of the year, a MapAction team of volunteers helped the Centre’s PA team develop analysis for a drought-related anticipatory action framework which was designed to trigger mitigation activities ahead of a predicted drought crisis. The PA team aimed to better understand the reliability of various indicators used to predict potential food shortages caused by drought in Somalia and Ethiopia. These indicators were then used to trigger an early release of funds from the UN’s Central Emergency Response Fund (CERF). The MapAction team reviewed past literature, evaluated available satellite images, and created a prototype drought model in Google Earth Engine (a platform for visualising and analysing satellite imagery of Earth). These inputs helped the PA team to flag an upcoming crisis in Ethiopia and trigger an activation for a humanitarian response. In the words of Josée Poirier, Predictive Analytics Technical Specialist from the PA team: “Your input leap-frogged us forward. It is amazing to me how quickly we were able to do this together. A round of applause for your work and its contribution to unlocking critically needed aid for Ethiopians.”
The MapAction and PA teams also collaborated to implement and validate an approach for mapping flooding from satellite imagery. MapAction’s Data Scientist has been working with the PA team to help evaluate the impact of recent anticipatory action in Bangladesh which took place in July 2020 and was the fastest-ever allocation of CERF funds. To better understand how this aid was helpful to those affected, the PA team needs to know exactly when, where, and for how long flooding occurred. Contributing to this work also has direct benefits for MapAction’s own work, enabling us to add a new data processing method to our disaster-response toolbox. We then had the opportunity to test this methodology in our response to the devastating impacts of Hurricanes Eta and Iota in Central America.
Both organisations have made commitments to assist in the global pandemic response. The Centre PA team and MapAction Data Scientist have, in partnership with the Johns Hopkins University Applied Physics Laboratory (APL), developed a model to forecast the number of cases, hospitalisations, and deaths due to COVID-19 for six countries, tailored to each country’s specific humanitarian needs. Named OCHA-Bucky, the model offers sub-national projections, and takes into the account the effects of non-pharmaceutical interventions. Presently, MapAction is participating in a pilot project to aid vaccine rollout in vulnerable countries by surveying the current data landscapes and identify gaps in order to address the logistical challenges inherent in such tasks. Along a similar line of work, the Centre PA team and APL are planning on adding vaccination strategies to the OCHA-Bucky model.
There is substantial overlap between the broad technical goals of the two organisations. The Centre’s Humanitarian Data Exchange (HDX) contains over 18,000 datasets and it has created several automatic pipelines (software that carries out a series of data-processing steps) to systematically ingest data from its partners into its database. The Centre’s technical expertise has so far been a key input into the planning and development of a similar (albeit smaller scale) pipeline at MapAction, which is being created to automate the generation of core maps as part of the Moonshot initiative. This work will ensure that base maps essential for coordinating any type of humanitarian response are immediately available whenever they are needed.
The two organisations share similar data access platforms and are actively engaged in ongoing discussions regarding different ways to construct pipeline software. Finally, both HDX and MapAction ultimately seek to identify and rectify gaps in the humanitarian data landscape in order to ensure that those coordinating the preparations for and responses to different types of emergencies have the reliable, timely information they need.
Looking ahead in 2021
MapAction and the Centre for Humanitarian Data are continuing to plan ways to collaborate throughout the rest of the year and beyond.
In addition to sharing expertise in advanced analytics, we are working to make data-driven methods accessible to wider audiences in the humanitarian sector in order to improve the effectiveness of aid programmes. MapAction and the Centre’s Data Literacy team have identified an opportunity to come together to develop GIS training material. This work aims to help non-technical humanitarians make better use of geospatial data to understand the needs of affected communities and coordinate aid.
Both teams are also collaborating to ensure that our data science workflows and models are published openly and can be used by others in the field. Inspired by initiatives such as The Turing Way, we are formalising and adopting best practices to write high quality code, document methodologies, and reproduce results.
At the end of the first year of our collaboration, it is gratifying to reflect on how much we have been able to achieve together while learning from each other and expanding our collective knowledge. We’re grateful to the German Federal Foreign Office for making this work possible by funding our Data Scientist role. We’re looking forward to continuing to work together to push forward the boundaries of humanitarian data science.