“Big Data”-driven predictive policing works by analyzing regularly recorded crime data (location, time, and crime), using sophisticated computer models and algorithms, to predict places of expected criminal activity. The predictions are narrowly tailored, limited to 500-by-500-square foot areas, and usually updated on a daily basis. Police officers are then deployed to those identified areas to deter crime from happening. Crunching numbers from crime statistics has therefore become a routine part of police work. 
NYPD Commissioner Bill Bratton has been perhaps the most aggressive advocate of data-based policing in the world. He introduced the data-driven, real-time “CompStat” performance management system to the NYPD in 1994 . The system is based on tracking and stopping smaller crimes to stop bigger crimes, and to pinpoint hotspots where crimes are clustered. 22 years later, Compstat is still in use in NYC, and crime has fallen 75% — a fact Bratton calls a “New York miracle.” The city had 1,946 murders in 1993, or more than a five a day. It had 352 in 2015. 
CompStat is a combination of management, philosophy, and organizational management tools for police departments.
It offers a dynamic approach to crime reduction, quality of life improvement, and personnel and resource management, whereby police department executives identify spikes in crimes using comparative statistics and address those spikes through the use of targeted enforcement. It includes 4 components:
- Timely and accurate information or intelligence
- Rapid deployment of resources
- Effective tactics
- Relentless follow-up. 
On a weekly basis, personnel from each of the NYPD’s 77 precincts, 9 police service areas and 12 transit districts compile a statistical summary of the week’s crime complaints, arrests and summons activity, as well as a written report of significant cases, crime patterns and police activities. This data, with specific crime and enforcement locations and times, is forwarded to the chief of the department’s CompStat Unit, where information is collated and loaded into a city-wide database. The unit runs computer analysis on the data and generates a weekly CompStat report which presents important performance indicators, making it easy to discern emerging and established crime trends, as well as deviations and anomalies.
The CompStat program involves weekly crime control strategy meetings between the agency’s executives and the commanders of operational units – enhancing accountability by providing local commanders with considerable discretion and resources. The program also ensures that precinct commanders remain aware of crime and quality of life conditions within their areas of responsibility. 
Critics have pointed out that factors other than CompStat have led to a decrease in crime in NYC, such as an increased number and better training of police officers, end of the crack epidemic, housing vouchers, etc. . There have also been reports of manipulation of data – retired police officials complained that pressure from department brass prompted widespread statistical manipulation of CompStat data, specifically by downgrading reports of serious crimes to less serious offenses . Some people are also worried about discriminatory practices such as profiling , however, companies point out that crime prediction software only analyzes type, place and time of crimes rather than involving any information on individuals. 
Outlook & Recommendation
CompStat has been successfully exported to other U.S. cities and abroad. [2, 3] Furthermore, new products have been developed: thousands of police officers in 60 cities across the U.S. are using predictive maps from an extension of CompStat called “PredPol” to help them decide which areas they should patrol during their shifts. Crime in LA’s Foothill precinct went down 20% from 2013 to 2014 using the program, Atlanta saw a near 10% drop in crime for two precincts, and Santa Cruz’s police department witnessed a combined 38% drop in robberies and burglaries. 
The results of big data policing are promising. My recommendation is to implement the system in more cities, while also paying more attention to preventing data manipulation. Additionally, I would recommend that a data-driven approach be applied to other areas of policing, such as deciding which police officer is deployed. Last year a policeman in Texas, who had responded to two suicide calls that day, was then dispatched to a children’s pool party where he ended up pulling out his gun. Ideally, the station would have sent a less stressed officer. 
The applications of machine learning to public policy are vast. According to Stephen Goldsmith, a professor at Harvard and a former mayor of Indianapolis, it could also transform almost every sector of public policy. 
 Fortune. 2016. How Big Data Is Helping the NYPD Solve Crimes Faster. [ONLINE] Available at: http://fortune.com/2016/07/17/big-data-nypd-situational-awareness/. [Accessed 17 November 2016].
 NBC News. 2016. An Inside Look at the System That Cut Crime in New York By 75 Percent – NBC News. [ONLINE] Available at: http://www.nbcnews.com/news/us-news/inside-look-system-cut-crime-new-york-75-percent-n557031. [Accessed 17 November 2016].
 Wikipedia. 2016. CompStat – Wikipedia. [ONLINE] Available at: https://en.wikipedia.org/wiki/CompStat. [Accessed 17 November 2016].
 The Huffington Post. 2016. What The CompStat Audit Reveals About The NYPD | The Huffington Post. [ONLINE] Available at: http://www.huffingtonpost.com/2013/07/12/nypd-compstat_n_3587637.html. [Accessed 17 November 2016].
 Lauren C. Williams. 2016. Police Departments Use Big Data To Predict Where Crime Will Hit Next. [ONLINE] Available at: https://thinkprogress.org/police-departments-use-big-data-to-predict-where-crime-will-hit-next-56dc95457a07#.ptiyml7tr. [Accessed 17 November 2016].
 PredPol. 2016. 5 Common Myths About Predictive Policing | PredPol. [ONLINE] Available at: http://www.predpol.com/5-common-myths-predictive-policing-predpol/. [Accessed 17 November 2016].