Modern law enforcement agencies are increasing their crime prevention efforts through data-driven initiatives. Fortunately, for organizations with up to date records management systems (RMS) and computer-aided dispatch systems, some of the best data for that purpose is already in their possession. This benefit is exponential when the RMS is integrated with an organization’s computer aided dispatching (CAD) system and mobile technology.
One of the primary advantages of data-driven crime prevention is the ability to focus resources where they are actually needed. Most commonly referred to as hot spot policing, this effective technique begins with the examination of data already found in an agency’s CAD and Police RMS. Through a combination of reported crimes, arrests, and suspicious activities documented by patrol units, police professionals can pinpoint certain areas in which to concentrate their efforts.
In decades past, agencies would attempt to thwart criminal activity by increasing routine patrols in what was then identified as “high crime areas.” The problem with that approach resulted in trying to cover too large of an area and not having a real impact on crime. Additionally, some agencies found that these saturation attempts quickly eroded the trust they shared with the community. In turn, information and tips from the public which the police rely on to help solve crime also greatly decreased.
The data-driven approach of hot spot policing tends to focus on much smaller areas. Research has shown that crime often happens in clusters of a few blocks down to the singular street level. By identifying these hot spots, police can develop an operational strategy to combat criminal activity.
This is where modern law enforcement software come into play. Using the features in both CAD and the RMS, agencies can isolate hot spots and determine the frequency of crime based on type, time and day, and possible suspect(s). This level of specificity allows for a tailored approach based on a host of factors. Any combination of patrol officers, detectives, supervisors, and crime analysts can develop a customized plan to address the unwanted activity in a particular hot spot.
Perhaps the data points to a simple increase in patrol as a probable solution. Maybe the use of unmarked cars or undercover officers is the right tactic. Other information found in an agency’s database may call for the use of a bait car, the aviation unit, or a multi-agency task force. The benefit of the data-driven approach to crime prevention is that much of the guesswork is taken out of the equation. What feels right may not always be the best means to the desired end.
How Does All This Good Data Get into an Agency’s System
For an agency to use data to prevent crime, the information first has to be present and available. Fortunately, state-of-the-art CAD and RMS make data management an almost seamless process. These three examples will help illustrate data development:
- A patrol officer happens upon a subject walking near an industrial area at 2 am. All of the businesses have been closed for hours and her suspicions about this subject’s activities are certainly justified. The officer confronts the subject and begins an interaction to determine what he is doing in the area. He provides a version of events that involves him walking to a friend’s house in a neighborhood a few miles away. A check of his ID reveals no active warrants. His story is a little odd but since he was on the sidewalk and not actually around any closed businesses, the officer completes an electronic field interview card and releases him.
- In the next scenario, a man discovers his credit card was used to make several online purchases which he did not authorize. He reports this to the police who, after interviewing him, find that his wallet was likely stolen from his car. He admitted to the officer that he often leaves his car doors unlocked both at home and when he is at work. The unauthorized purchases were for gift cards that were sent to an address out of state. The IP addresses involved in the transactions appear to have been masked and the out of state recipient turned out to be a private mailbox company. Although the officer entered all of the details into the RMS by completing his report, the case appears to have reached a dead end.
- In the final example case, the police are called to the scene of a domestic dispute. A neighbor reports a husband and wife are having a loud argument on their back porch. When officers arrive, they separate the couple and try to calm the situation. It appears to have been an argument over finances. They each told the officers that money has been tight, and tensions were running high. They confirmed that no physical disturbance had occurred, and it had been a verbal dispute. As is common, the police agency’s policy required a report which contained all pertinent information.
A few weeks later, a crime analyst was evaluating case data by running various reports within the agency’s integrated CAD/RMS. His findings were passed to a detective for further follow-up. The investigation and analysis of the data point to the subject in the verbal disturbance with his wife. It turns out, his financial struggles led him to be bribed. For a few hundred dollars, he provided the gate code to the secure parking lot at his place of employment, an industrial complex. It was also discovered this same complex had a series of auto burglaries in recent weeks. The thief, it seems, was particularly interested in stealing credit cards which he sold to someone who was making illegal purchases of gift cards.
Perhaps a single piece of information does not seem important when initially included in a law enforcement report. However, technology permits even small amounts of data to be evaluated for possible connections to other records. The combination of law enforcement professionals and quality data can greatly increase the effectiveness of an agency’s crime prevention efforts.