The Real-Time Crime Forecasting Challenge sought to harness the advances in data science to address the challenges of crime and justice. It encouraged data scientists across all scientific disciplines to foster innovation in forecasting methods. The goal was to develop algorithms that advance place-based crime forecasting through the use of data from one police jurisdiction.

The Challenge had three main aims:

Harness data science advances in other fields to crime forecasting. Encourage scientists from all fields to consider the challenges of crime and justice. Conduct the most comprehensive comparative analysis of crime forecasting software and algorithms to date.

Specifically, the Challenge tested how effectively and efficiently contestants’ crime forecasting algorithms could forecast police calls-for-service in four crime categories in Portland, Oregon,[1] for five forecast periods. View the challenge posting for additional details.

Challenge Winners

The scores are in and we have our winners! Winners were selected from submissions by five students, forty-two small teams/businesses, and fifteen large business.

Download the complete leaderboard (xlsx, 56 KB)! The leaderboard includes the names and scores for first, second, and third place submissions for every category, crime type, time frame, and score type (PAI and PEI*).[2]

Download the winning submission files for students (zip, 29 MB), small teams/businesses (zip, 47.5 MB), and large businesses (zip, 50.8 MB).