Can lives be saved through a better, data-based understanding of why atrocities occur? See how our client achieved more predictive results through crowdsourcing.
Mass atrocities bring untold devastation, uprooting of families, and can leave hundreds, even thousands of humans in extremely dire circumstances. A better understanding of the indicators of vulnerability – conditions that lead to atrocity – are helping to better model, and therefore predict where a mass atrocity is most likely to occur. Through better data solutions, prediction can then lead to prevention.
For this challenge, the client was seeking an improved, statistical model to out-predict existing subjective analysis models. However, there was no proof that an answer was present in the available data sources.
The challenge was structured in a series to first identify & validate existing and new public data sets on national and sub-national violence.
Then, a predictive modeling challenge was launched, using newly validated data sets.
The challenge resulted in 5 winning and powerful new data solutions that can be easily used for future improvement.
The newly created predictive models help identify community-level risk factors that make communities more or less likely to experience acts of mass violence.
The top solutions demonstrate a significant event-prediction correlation, meaning the core assumption that an improved statistical model could be created, was proven true.