This is a data-driven Early Intervention System (EIS) for police departments. The system uses a police department's data to predict which officers are likely to have an adverse interaction with the public. An adverse incident can be defined on a department by department basis, but typically includes unjustified uses of force, officer injuries, preventable accidents and sustained complaints. This is done such that additional training, counseling and other resources can be provided to the officer before any adverse interactions occur.
Our modeling pipeline has some prerequists and structure documentation:
- Configure the Machine.
- Documentation about the structure and contents of the repositories.
- Setup Database Connection.
After the prerequisites and requirements are met, the full pipeline can be run (pipeline documentation).
Once the pipeline has been run, the results can be visualized using the webapp.
Deprecated Documentation Quick Links:
Please use Github's issue tracker to report issues and suggestions.
- 2016: Tom Davidson, Henry Hinnefeld, Sumedh Joshi, Jonathan Keane, Joshua Mausolf, Lin Taylor, Ned Yoxall, Joe Walsh (Technical Mentor), Jennifer Helsby (Technical Mentor), Allison Weil (Project Manager)
- 2015: Jennifer Helsby, Samuel Carton, Kenneth Joseph, Ayesha Mahmud, Youngsoo Park, Joe Walsh (Technical Mentor), Lauren Haynes (Project Manager).