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, for five forecast periods. View the challenge posting for additional details.
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*).
|Winner||Contestant Type||No. of Victories|
|TAMERZONE||Small Team/ Business||10|
|Intuidex||Small Team/ Business||7^^|
|BATES ANALYTICS||Small Team/ Business||5|
|TADIC||Small Team/ Business||3|
|pennaiken||Small Team/ Business||3|
|ANDY_NIJ||Small Team/ Business||2|
|WILLIAM HERLANDS||Small Team/ Business||2|
|KUBQR1||Small Team/ Business||2|
|STEVEN YURICK||Small Team/Business||2|
|MURRAY MIRON||Small Team/ Business||2^^|
|DYLAN FITZPATRICK||Small Team/ Business||2^^|
|Center for Science and Law||Small Team/Business||1|
|TEAM Kernel Glitches||Large Business||9|
|GARAN TANALYTICS||Large Business||1|
|Conduent Public Safety Solutions||Large Business||1|
|MARUAN ALSHEDIVAT||Large Business||1|
^ Victory totals in the student category include four-way tie in both PAI and PEI* scores for the one- and two-week burglary forecasts.
^^ Multiple Small Team/Business entrants tied PEI* score in the one week forecast for burglary.
[note 1] The Portland Police Bureau provided five years of calls-for-service data as a training data set. Contestants were not limited to using the CFS data, but could also use any other data set of their choosing.
The data from the Portland Police Bureau is public and as such may be used by individuals/entities for secondary data analysis for non-Challenge related topics.
[note 2] On December 13, 2017, the leaderboard was updated to include scores for Center for Science and Law. In affected categories, a fourth place submission and score now is included.