From Where to What: The Geo-Intervention Modelling Framework

Interventions implemented in geographic space (geo-interventions), have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To solve this, we produced a framework for transforming spatial data to model potential actions for generating geo-interventions. Finally, we conducted a case study of reducing traffic collisions in Toronto, Canada, to demonstrate the framework, which produced a machine learning model that discovered geo-interventions modifying red light camera, transit shelter, and wayfinding infrastructure predicted to reduce collisions by 5.7%. We highlight the importance of the framework for bridging research and practice through unified understanding, actionable outputs, human guidance, and iterative refinement. With recent advances in big data and artificial intelligence, we envision an acceleration in the discovery of geo-interventions, and emergence of interdisciplinary work towards predicting accurate and precise future real-world outcomes at scale.