Compstat -- Crime Fighting Information

COMPSTAT -- Crime Fighting Information System

In 1994, the New York City Police Department adopted a law enforcement crime fighting strategy known as COMPSTAT (COMPuter STATistics). The system utilizes imputes based on geographic information systems along with other data to determine which areas are high risk areas and at what times these areas fit the criteria. Historical data is collected and then analyzed to help the police use predictive policing to make best use of the assets and human resources. Before such a system, police relied upon random patrols to monitor crime. However, with the assistance of information technology police departments can use their resources in a more targeted manner to provide their organizations more efficiency and effectiveness in their efforts to combat crime.

Predictive Policing

Predictive policing is based on the assumption that crimes are not random. It was this assumption that led a group of researchers to develop a predictive policing program -- including two mathematicians, George Mohler and Martin Short; an anthropologist, Jeff Brantingham; and a criminologist, George Tita (Goode, 2011). The programing was initially based on models that were developed to predict aftershocks that occur after earthquakes. It uses previously recorded historical data to predict patterns of where and when future offenses might occur. In July, Santa Cruz began testing the software and the prediction method for property crimes like car and home burglaries and car thefts and so far the police department's crime analyst, the program has helped officers pre-empt several crimes and has led to five arrests (Goode, 2011).

Other locations have also used similar systems. In 2009, Police Chief Charlie Beck discussed how the LAPD has used COMPSTAT, intelligence-led policing and problem solving to reduce crime over the last eight years (National Institute of Justice, 2009). He compares the COMPSTAT system to the systems used by retailers such as Wal-Mart to predict sales. Wal-Mart will use weather data to determine what to stock in their stores based on historical data. For example, before a major weather event people are more likely to stock up on particular items such as bottled water and canned food. Colleen McCue illustrated the potential of predictive policing with an example the every New Year's Eve in Richmond, VA, the community experienced an increase in random gunfire and by being able to recognize this pattern there a 47-percent reduction in random gunfire, a 246-percent increase in seized weapons and a savings of $15,000 with 50 fewer officers deployed (National Institute of Justice, 2009).

Based on some initial field tests as well as the potential that these systems offer, COMPSTAT's many advocates assume the existence of several conditions that will allow COMPSTAT to transform inertial and conservative police departments into efficient crime-fighting machines (Willis, Mastrofski, & Weisburd, 2004). However, it is also noted that there are many potential issues with the implementation of the system. For example, using predictive policing will likely be at odds with many of the organizational cultures found in traditional police forces in many cities. Furthermore, different objectives may also take precedence over the use of COMPSTAT systems such as political goals as well as the ability for policing organizations to provide the needed resources to take advantage of a COMPSTAT system.

The various COMPSTAT systems can take various inputs, such as historical data, weather, or political events, and process these inputs to generate various sets of "hotspots." These hotspots can be updated daily and reflect the most likely time and space estimates of where crime is more likely to occur given the various factors that are presented to the algorithm. The outputs may represent a location and a time in which police officers should patrol given the likelihood of a crime occurring at this output. The system then can keep updated as more feedback is entered into the system to generate a new range of crime hotspots.

SWOT

Strengths

The system can optimize the best use of resources in a police organization and improve the likelihood that they will patrol the correct locations at the correct times.

The system can identify potential opportunities to staff officers at different levels given the historical data and the algorithim calculations thus reducing waste.

Weaknesses