Saturday, September 22, 2012

Predictive Policing: Reducing Crimes Using Predictive Security Analytics






The ability to identify and characterize threats, and anticipate crime represents a game changing paradigm shift in operational public safety and security. Predictive policing allows command staff and police managers to leverage advanced analytics in support of meaningful, information-based tactics, strategy, and policy decisions in the applied public safety environment.



Law enforcement work is frequently reactive: officers respond to calls for service, quell disturbances, make arrests. Today more than ever, law enforcement work is also proactive: officers make connections and develop networks within their communities that lead to joint problem solving, they assess small problems and take steps to prevent them from becoming bigger.



Why Just Count Crime When You Can Prevent It?


Why just count crime when you can anticipate, prevent and respond more effectively? The Predictive Policing Model enables public safety and security, by executive staff to leverage predictive analytic in support of information based tactics, strategy and policy decisions. The Predictive Policing Model helps to prevent crime and respond more effectively, while optimizing limited resources, including personnel.

So What Is Predictive Policing?, The technique of integrating data analysis with professional law enforcement expertise to understand why a problem arises and how to avoid the next problem is called predictive policing. It builds on and melds pieces of community policing, intelligence-led policing and hot spots policing.

Predictive Security Analytics

The ability to anticipate or predict crime represents a paradigm shift in law enforcement. A smaller, more agile force can effectively counter larger numbers by leveraging intelligence. The opportunity to enter the decision cycle of our adversaries—drug dealers, gang members, terrorists—affords unique opportunities for prevention, thwarting, and information-based response, ideally preventing crime, ultimately changing public safety outcomes and the associated quality of life for many communities.

The analytic methods used in the predictive-policing model do not identify specific individuals. Rather, they surface particular times and locations predicted to be associated with an increased likelihood for crime. Identifying and characterizing the nature of the anticipated incident or threat increase the ability to create information-based approaches to prevention, thwarting, resource allocation, response, training, and policy. These fact-based approaches promise to increase citizen and officer safety alike.

The Predictive Policing Model leverages predictive analytics to enable information based approaches to public safety and security tactics, strategy and policy. Predictive analytics tools, techniques and processes support meaningful exploitation of public safety and security data necessary to turn data into knowledge and guide information based prevention, thwarting, mitigation and response.

The law enforcement uses data and analyzes patterns to understand the exact nature of a problem. Officers devise strategies and tactics to prevent or mitigate future harm. They evaluate results and revise practices and policing to improve situations. Larger departments combine an array of data with street intelligence and crime analysis to produce better assessments and make predictions about what might happen next if they take various actions.

Advanced analytics includes the systematic review and analysis of data and information using automated methods. Through advanced statistics techniques, machine learning tools, and artificial intelligence, critical pieces of information can be extracted from large data repositories. It is possible to prove or disprove hypotheses while discovering new or previously unknown information. In particular, unique or valuable relationships, trends, patterns, sequences, and affinities in the data can be identified and used proactively to categorize or anticipate additional actions or information. The risk-based deployment helps to allocate police resources when and where they are needed to prevent or deter crime through a strong police presence. This also ensures the ability to respond rapidly by proactively positioning resources when and where they are likely to be needed in order to guarantee a timely response. Ultimately, the incorporation of meaningful, operationally relevant analysis into information-based police tactics, strategy, and policy has been shown to increase public safety and change outcomes.

The law enforcement is familiar with the use of advanced analytics in support of fraud detection and identity theft prevention programs. Similar computational methods have been used to prevent and solve violent crimes, enhance investigative pace and efficacy, support information-based risk and threat assessment, and deploy police resources more efficiently.

Simply stated, advanced analytics includes the use and exploitation of mathematical techniques and processes that can be used to confirm things that we already know or think that we know, as well as discover new or previously unknown patterns, trends, and relationships in the data.

Predictive policing Enablers


"I’m not going to get more money. I’m not going to get more cops. I have to be better at using what I have, and that’s what predictive policing is about… If this old street cop can change the way that he thinks about this stuff, then I know that my [officers] can do the same. "



- Los Angeles Police Chief Charlie Beck



Police departments are being asked to do more with less. In some localities, significant budget reductions are requiring police managers and command staff to consider reductions in the retention of sworn personnel as Personnel costs represent the single largest budget line item in most public safety organizations. Thus the ability to use this resource more efficiently has become absolutely essential to police managers. As the law enforcement community increasingly is asked to do more with less, predictive policing represents an opportunity to prevent crime and respond more effectively, while optimizing increasingly scarce or limited resources, including personnel. The predictive-policing vision moves law enforcement from focusing on what happened to focusing on what will happen and how to effectively deploy resources in front of crime, thereby changing outcomes


Crime prevention is almost always the economical alternative. When considering the total cost of crime, the numbers include far more than the direct economic costs associated with the specific crime itself. In addition to the readily identifiable costs associated with investigative and prosecutorial resources and incarceration and court supervision, a number of additional, frequently unseen costs also must be considered when calculating the true cost of crime. These include the long-term cost of crime to the victims and their families, fear of crime, and the opportunity costs to communities struggling with crime.

Predictive Analytics, a proven solution from retailers

Advanced analytics are used in almost every segment of society to improve service and optimize resources. Specific tactics and techniques to execute the predictive-policing model can be found in business analytics. Some examples include customer loyalty programs that track purchases and provide specifically targeted coupons that are based on recent or related purchases and algorithms that create models of customer preferences and recommend products to similar customer groups. Similarly, agile supply chain management programs ensure the timely delivery of products and can anticipate changes in demand related to seasonal differences, recent purchases, or events likely to result in rapid changes in need or consumption like hurricanes, large winter storms, or other significant events.

The customers of Amazon retailer Web site are familiar with the phrase “Customers who bought this item also bought. . . .” This simple phrase demonstrates Amazon’s ability not only to segment their customer population but also to extend from it in a meaningful way. The ability to understand the unique groups in their customer base and to characterize their purchasing patterns allows Amazon not only to anticipate but also to promote or otherwise shape future behavior.

The popular retain chain Wal-Mart in US, in anticipation of a large weather event, it shift its supply chain to send duct tape, bottled water, and Pop-Tarts to the affected area in advance of the storm. After years of experience with large weather events, Wal-Mart has found increased sales of Pop-Tarts especially the strawberry Pop-Tarts. The reason for this is, Pop-Tarts can be eaten cold, they are tasty and mostly kids like them. The predictive analytics helps to anticipate the increased demand and being able to adjust the supply chain accordingly. This ensures that an adequate supply of strawberry Pop-Tarts is delivered to the stores in the affected area in advance of the storm when people are making their preparations.

Predictive Policing: THE paradigm shift in policing


The professional era of policing began in the 1960s. The policing strategy was based largely on reactive investigation, random patrol, and rapid response, that includes faceless cops chasing radio calls, which markedly increased detachment from the community.



The community-policing model emerged in the 1990s which emphasis on addressing the underlying conditions that enable and foster crime and for shifting some decision-making responsibilities to line officers. It emphasizes problem solving that includes the public as an active partner. This model put value on the three parameters say partnership, problem solving, and prevention, with the most important being prevention.



The next model is intelligence-led policing (ILP), it extends to include research-based approaches, information and communications technology, and increased information sharing and accountability. ILP encourages the use of criminal intelligence in support of collaborative, multijurisdictional approaches to crime prevention; and it emphasizes the role of analysis in tactical and strategic planning. ILP also includes attention to privacy and civil liberties.

Predictive analytic tools

CompStat: Over the past decade, many large police departments, including Los Angeles and New York City, have used CompStat, a system that tracks crime figures and enables police to send extra officers to trouble spots. CompStat is a "strategic control system" designed for the collection and feedback of information on crime and related quality of life issues.CompStat process also has an inherent opportunity for developing leaders and improving the leadership process. It also helps in developing, maintaining, or enhancing individual attributes like knowledge, skills, and abilities (KSAs)

CRUSH(Criminal Reduction Utilizing Statistical History) is an IBM predictive analytics system that attempts to predict the location of future crimes. It was developed as part of the Blue CRUSH program in conjunction with Memphis Police Department and the University of Memphis Criminology and Research department

PredPol predictive policing crime-prediction software. Based on models for predicting aftershocks from earthquakes, PredPol’s technology forecasts highest risk times and places for future crimes. The program complements officers’ intuition by targeting place-based prediction “boxes” as small as 500' by 500'.

Sample Case studies

Richmond, Virginia, was the ninth most dangerous city in the US, according to annual crime rankings published by Morgan Quitno Press. The IBM SPSS Modeler solution, which features inexpensive and easy-to-learn data mining technology, helped Richmond reduce its crime, dropping the city all the way from 5th to 99th.



In the San Fernando Valley, in one year after launching the program, officers are seeing double-digit drops in burglaries and other property crimes. The program has turned enough in-house sceptics into believers that there are plans to roll it out citywide by next summer.

Risk-based deployment was tested on New Year’s Eve in United States and found to markedly reduce random gunfire complaints. Police identified locations and times expected to be associated with increased complaints of random gunfire and proactively deployed police resources to those locations to prevent or deter crime or respond more rapidly. Random gunfire complaints were decreased by 47 percent, while the number of weapons recovered went up 246 percent. in addition, a reduction in police resources resulted in saving $15,000 in personnel costs.

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