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Entries must be submitted through the Office of Justice Programs’ Grants Management System (GMS). Contestants must submit entries under the appropriate contestant type by selecting the applicable solicitation within GMS. Contestant types types are: <ul> <li><strong>Student</strong>: Enrolled as a full-time student in high school or as a full-time, degree-seeking student in an undergraduate program (associate or bachelor’s degree).</li> <li><strong>Small Team/Business</strong>: A team comprised of 1-20 individuals, or a small business with less than 21 employees. Teams should enter using a team name and provide a list of all individuals on the team. A small business should enter using the name of its business.</li> <li><strong>Large Business</strong>: A business with more than 20 employees. A large business should enter using the name of its business.</li> </ul> Note that Student and Small Team/Business contestants may enter the Large Business category, but Large Businesses <em>may not</em> enter the Student or Small Team/Business categories. See the <a href="http://nij.gov/funding/Pages/fy16-crime-forecasting-challenge.aspx" target="_blank" rel="noopener">Real-Time Crime Forecasting Challenge</a> page on NIJ.gov for details on how to enter.
2018-11-26T07:06:27
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closed
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Inviting our nation’s brightest minds to develop algorithms that advance crime forecasting.
Department of Justice - Office of Justice Programs
false
ideation
02/28/2017 11:59 PM
02/22/2017 12:00 AM
FY16
$1,200,000
Joel Hunt
Agency Prize Authorization - DOJ
2152
No
See the <a href="http://nij.gov/funding/Pages/fy16-crime-forecasting-challenge.aspx" target="_blank" rel="noopener">Real-Time Crime Forecasting Challenge</a> on NIJ.gov for rules.
No
07/31/2017 12:00 AM
Technology demonstration and hardware
148991
Hosted on this platform
Yes
Scientific
Entries must be submitted through the Office of Justice Programs’ Grants Management System (GMS). Contestants must submit entries under the appropriate contestant type by selecting the applicable solicitation within GMS. Contestant types types are: <ul> <li><strong>Student</strong>: Enrolled as a full-time student in high school or as a full-time, degree-seeking student in an undergraduate program (associate or bachelor’s degree).</li> <li><strong>Small Team/Business</strong>: A team comprised of 1-20 individuals, or a small business with less than 21 employees. Teams should enter using a team name and provide a list of all individuals on the team. A small business should enter using the name of its business.</li> <li><strong>Large Business</strong>: A business with more than 20 employees. A large business should enter using the name of its business.</li> </ul> Note that Student and Small Team/Business contestants may enter the Large Business category, but Large Businesses <em>may not</em> enter the Student or Small Team/Business categories. See the <a href="http://nij.gov/funding/Pages/fy16-crime-forecasting-challenge.aspx" target="_blank" rel="noopener">Real-Time Crime Forecasting Challenge</a> page on NIJ.gov for details on how to enter.
NIJ will use the Prediction Accuracy Index to measure the effectiveness of the forecasts. See NIJ.gov for details: http://nij.gov//funding/pages/fy16-crime-forecasting-challenge.aspx#judgingcriteria
Effectiveness of the forecast
NIJ will use the Predictive Efficiency Index to measure the efficiency of the forecasts. See NIJ.gov for details: http://nij.gov//funding/pages/fy16-crime-forecasting-challenge.aspx#judgingcriteria
Efficiency of the forecast
40 prizes of $5,000 each.
200000
Students
40 prizes of $10,000 each.
400000
Small Teams/Businesses
40 prizes of $15,000 each.
Large Business Prize
600000
Warren, Yurick, Ledray, Scarborough
TAMERZONE, Intuidex, BATES ANALYTICS, TADIC, pennaiken, ANDY_NIJ, WILLIAM HERLANDS, KUBQR1, STEVEN YURICK, MURRAY MIRON, DYLAN FITZPATRICK, CCC, PTL
TEAM Kernel Glitches, PASDA, Codilime, GRIER, DataikuInc, JeremyHeffner, IMPAQ, GARAN TANALYTICS, Conduent Public Safety Solutions, MARUAN ALSHEDIVAT
false
true
This challenge seeks to harness the advances in data science to address the challenges of crime and justice. It encourages data scientists across all scientific disciplines to foster innovation in forecasting methods. The goal is to develop algorithms that advance place-based crime forecasting through the use of data from one police jurisdiction. &nbsp;
Launched
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70
true
Real-Time Crime Forecasting Challenge
true
0
false
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/challenge/real-time-crime-forecasting-challenge/
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