The Reality of Evidence-based Decision Making in Humanitarian Programming: An Exploratory Study of WASH Programs in Uganda

With ongoing research, increased information sharing and knowledge exchange, humanitarian organizations have an increasing amount of evidence at their disposal to support their decisions. Nevertheless, effectively building decisions on the increasing amount of insights and information remains challenging. At the individual, organizational, and...

Airborne Wind Energy Resource Analysis: From Wind Potential to Power Output

The reciprocity of data integration in disaster risk analysis

Humanitarian organizations are increasingly challenged by the amount of data available to drive their decisions. Useful data can come from many sources, exists in different formats, and merging it into a basis for analysis and planning often exceeds organizations’ capacities and resources. At the same time, affected communities’ participation in...

Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making

The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict...

Turning data into action: supporting humanitarian field workers with open data

In the aftermath of disasters, information is of the essence for humanitarian decision makers in the field. Their concrete information needs is highly context-influenced and often they find themselves unable to access the right information at the right time. We propose a novel ICT-based approach to address these information needs more accurately...