Closing Case Predictive Policing
The Problem
Cities today have to do more with fewer resources, and their police departments are no exception. For example, since 2001 the Santa Cruz Police Department (SCPD) of Santa Cruz, California, has had to lay off 10 of its 104 police officers, while the city population increased by 10 percent. Consequently, the SCPD needed to develop a strategy to maintain its performance levels despite its growing resource constraints.
The IT Solution
In July 2011, the SCPD implemented an analytics system called PredPol (www.predpol.com). This system provides intelligence to the police about when and where future crimes are most likely to occur. The SCPD can utilize this information to determine how it can best deploy its officers to prevent those crimes. The information system consists of a sophisticated algorithm that analyzes large sets of data. This approach is called predictive policing.
Pred Pol's algorithm (mathematical formula) is based on one used by seismologists to predict earthquakes. It targets property crime such as home burglaries, car break-ins, and vehicle thefts. Such crimes tend to cluster and spread in a way that is similar to the tremors that follow a large earthquake.
The algorithm identifies hot spots, which are 500- by-500- foot areas at the highest risk for property crimes. The SCPD then divides the city into five regions and makes certain that at least one car is on duty in each one. Officers pick up their hot spot maps at the roll call meeting that precedes each shift. Each map contains a hot spot. Above each map is a set of statistics: the probability that a crime will take place in that hot spot that day, the two hour-long windows when that potential crime is most likely to occur, and the likelihood that the crime will be a property crime.
Before the SCPD implemented the software, individual officers had to decide where and how to focus their patrol time based on their limited experience of the area. Since the implementation, officers can clearly identify hot spots based on the maps they receive and then patrol those areas more heavily.
The Results
The impacts of Pred Pol on crime in Santa Cruz are promising. By the end of July 2011, property crime was down 27 percent from the year before, an impressive drop, particularly given the 25 percent rise recorded during the first 6 months of that year. The city also experienced a 19 percent reduction in burglaries within one year after Pred Pol went into operation. Furthermore, seven criminals were discovered inside the hot spots.
For instance, one afternoon, two women were detained at a hot spot after they were caught looking into cars in a triple-decker parking garage. One of the women had an outstanding arrest warrant for possession of methamphetamines, and the other was caught in possession of meth at the site. At another hot spot, police officers stopped a man for suspicious behavior. When they searched him, they found stolen goods from a burglary that had taken place nearby a few days before. These arrests point to the effectiveness of the SCPD's new predictive policing system.
Predictive policing saves Santa Cruz money. Every time the police prevent a crime, they save the city the costs of processing and booking the perpetrators, detaining them if need be prior to trial, trying them in court, and housing them in correctional institutions after they have been convicted.
When Santa Cruz introduced predictive policing, some police officers dismissed it as "voodoo magic." Relying on mathematics and statistics to combat property crime ran counter to many officers' ideas of police work. Some officers took it as an affront to their skills. Others were concerned that it would involve extra work. However, many officers came around when they realized that driving through a 500-by-500-foot hot spot during an hour-long window requires very little effort in to generate quite a lot of result out. This favorable result-to-effort ratio perfectly exemplify es the impact of analytics systems on various organizations, businesses, and government operations. Small, directed efforts, guided and informed by analytics systems, can bring about great change.
When the city of Los Angeles tested Pred Pol, the LAPD found the system to be twice as proficient as human analysts at predicting where burglaries and car break-ins would happen. When police officers followed the system's advice and focused their patrols within the designated areas, those areas experienced a 25 percent drop in reported burglaries. Significantly, this drop was an anomaly compared to neighboring areas.
Questions
1. What are the advantages of predictive policing to the cities of Santa Cruz and Los Angeles? Provide specific examples.
2. What are potential disadvantages of predictive policing to the cities of Santa Cruz and Los Angeles? To the SCPD? To the LAPD? Provide specific examples.
3. Which of the following choices best describes predictive policing? (1) A way to catch criminals; (2) a way to prevent crimes from happening; or (3) both? Support your answer.