Working with the Pittsburgh Bureau of Fire (PBF), the Fire Risk Analysis research team is using historical fire incident and inspection data, coupled with business permits and property condition information to develop predictive models of structure fire risk for commercial properties. PBF conducts regular fire inspections of commercial properties, as stipulated by the municipal fire code. With a prioritization method informed by the prediction probability of fire risk from machine learning models, and implemented in an interactive map visualization, they will be better able to target their inspections for the properties at greatest risk of fire.

You can read more about this project at our project page here. After deploying the model on the City of Pittsburgh's servers for usage by city officials, we released a technical report describing our process in detail, so that fire departments from other cities could adopt our approach and use our code (available on GitHub here).

This work was highlighted by the GovTech and MetroLab Network as their "Innovation of the Month" in January, 2018.

In April, 2018, Pittsburgh's Mayor Bill Peduto, Fire Chief Jones, and I announced the city's use of machine learning to improve their fire risk reduction practices in a press conference.

Lee, J., Lin, Y., and Madaio, M. (2018). A Longitudinal Evaluation of a Deployed Fire Risk Model. In the AI for Social Good Workshop at the Neural Information Processing System Conference. (NeurIPS 2018). [pdf]

Singh Walia, B., Hu, Q., Chen, J., Chen, F., Lee, J., Kuo, N., Narang, P., Batts, J., Arnold, G., and Madaio, M. (2018). A dynamic pipeline for spatio-temporal fire risk prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD). [pdf]

Metro21: Smart Cities Initiative (2018). Predictive Modeling of Building Fire Risk: Designing and evaluating predictive models of fire risk to prioritize property fire inspections. A Metro21 Research Publication. [pdf]