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| What is data-driven policing? |
| 4 benefits of data-driven policing |
| Addressing challenges to data |
| Choosing an accurate solution |
From October 19 through October 22, more than 16,000 public safety professionals will be gathering in Boston to attend the International Association of Chiefs of Police (IACP) Annual Conference and Exhibition, North America’s largest law enforcement event.
The theme of this year’s conference is “Equipped to Innovate”. While law enforcement agencies are always seeking ways to improve and even reinvent the ways they do their jobs, the need to innovate has become increasingly urgent as their work grows more and more challenging.
One innovative approach that numerous agencies have been exploring and incorporating is data-driven policing. It’s not a completely new strategy, but as digital tools become more sophisticated, it’s becoming more and more powerful.
The shift from traditional policing methods to data-driven analysis can significantly improve:
- Public safety
- Resource allocation
- Crime prevention strategies
That makes it a topic that both law enforcement professionals and the public should better understand.
What is data-driven policing?
Data-driven policing is the process of using data from several sources to help law enforcement determine not only where crimes are occurring but also where they are likely to occur. Data analysts work in tandem with police officers to collect and analyze any information that might identify criminal activity so that law enforcement personnel in the field can quickly take action.
The origins of data-driven policing trace back to the 1970s, when law enforcement agencies began digitizing their records.
By the 1990s, departments created computerized databases for faster, more efficient access to information. New York City, for instance, began using CompStat, a digital tool incorporating crime analytics and data visualization.
Another milestone in the development of data-driven policing was reached in 2011 when the Los Angeles Police Department implemented its Operation LASER and PredPol policing software. Since then, more and more agencies nationwide have developed and used their own programs.
Data-driving policing uses several types of data, including:
- Crime reports and incident data, both current and historical
- Demographic information about the community and its residents
- Geographic and spatial data about the community and its natural and built elements
- Open-source intelligence (OSINT), which is data available from publicly available sources, including public records, social media, news sources, and other publications
- Sensor data, which includes data picked up by traffic cameras, gunshot detection systems, CCTV, and cellphone towers
Four benefits of data-driven policing
Advocates for data-driven policing cite four benefits in particular:
1. Enhancing crime prevention
One of the ways that data can boost prevention is through what’s called predictive policing, which uses machine learning and artificial intelligence (AI) to analyze the types of data listed above. This approach uses AI-generated algorithms to predict where and when crimes might occur. This information can help law enforcement make better tactical and resource-allocation decisions to stop criminal activity before it happens.
2. Improving investigative capabilities
A key element of data-driven policing involves identifying patterns of past criminal activity. This includes seeking geographic “hot spots” where such activities regularly take place. Hot spots exist for many reasons—notably, the high number of potential victims and offenders in the area. Data gathering and analysis can assist police in generating computerized maps and reviewing incident reports in order to plot crime data and identify commonalities in criminal activity and modus operandi. Law enforcement can then use this information to solve cases more quickly.
Similarly, law enforcement can use data to analyze networks of organized crime. Network analysis can help investigators visualize large amounts of data, allowing them to identify the structural relationships between individuals and criminal organizations.
3. Optimizing resource allocation
A lack of adequate staffing is one of the top challenges hindering law enforcement from fully preventing and investigating criminal activity. By using data analytics, police agencies can optimize office scheduling and deployment, better-balancing workload and coverage. This, in turn, can improve officer efficiency and safety, thus boosting job satisfaction.
Similarly, police forces can use data analytics to inform budgeting decisions. Through cost-benefit analyses of policing strategies, data insights can help agencies better conduct long-term financial planning.
4. Increasing community engagement
Police officers can’t work alone. They need the help and cooperation of the communities they serve to solve criminal cases and reduce their incidence. Law enforcement can use data to help pinpoint community needs, develop outreach programs for crime reduction, and measure the effectiveness of those initiatives.
In addition, police can share their data with their communities. This kind of transparency can help build greater trust between community members and law enforcement. In addition, this can encourage more cooperation from citizens in identifying and preventing potential crimes.
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Addressing challenges to data
Though these are significant benefits, data-driven policing techniques have to be used with care in order to navigate challenges.
Algorithmic bias and fairness
Data-driven policing does have its critics, just as using AI in law enforcement work does. They argue that it can discriminate against people of color and economically disadvantaged classes. Arrest data, particularly for drug and nuisance crimes, can be influenced by racial bias in police officers’ choices about whom to investigate.
Algorithms may “predict” a higher incidence of crime in minority communities than actually exists. That leads to more police focus on those communities, thus adding more “data” reinforcing their status as hot spots for crime.
Balancing human judgment and data insights
Researchers have been working on the problem of algorithmic bias. One recommendation they make is simply to be aware that such biases exist and that algorithms need to be adjusted—by humans, not machines—to reduce that bias. For instance, agencies should put less emphasis on arrest data alone. Another recommendation for reducing data “feedback loops” is using what’s called “the Koper curve,” which requires that officers spend no more than 15 minutes patrolling the same area.
In other words, data-driven policing shouldn’t be driven solely by data. The future of law enforcement technology lies in protecting and training law enforcement officers. Relying too much on machine-analyzed data can result in distorted analysis that can work against successful crime reduction.
Data needs to be combined with human oversight and officer experience. This is why it’s essential for agencies to train officers and investigators to use data-driven insights effectively and correctly.
Data privacy and security
Law enforcement data is highly sensitive. Agencies need to be sure that they’re protecting it from hackers and other bad actors.
They should familiarize themselves with all pertinent regulations for protecting sensitive data and comply with them. Though some data can be publicly shared, a great deal of it needs to be secured so that it doesn’t fall into the wrong hands.
Choosing an accurate solution
If used properly and responsibly, data-driven policing is an innovative approach to law enforcement that can provide significant benefits. If used improperly, it can work against what police forces are trying to accomplish. Data-driven policing is effective only if officers and investigators are trained in its correct use—and the data itself is reliable.
Public safety agencies are certainly aware of the usefulness of technology tools. However, not all solutions can deliver reliable, relevant information. Some technologies are technically out-of-date. And tools that have been developed more recently may gather data that isn’t current or is otherwise unreliable.
To be successful, data-driven policing requires access to a wide range of accurate data. Reliable data should include real-time incarceration and arrest records, as well as information gleaned from public and proprietary records and brought together in one place so that investigators can analyze it and generate truly actionable insights.
An example of a digital data toolbox developed specifically for public safety agencies is Thomson Reuters’ law enforcement investigative software and solutions. These tools incorporate Thomson Reuters CLEAR, an investigative platform that accesses a broad range of public and proprietary records in a single working environment.
To learn more about how data and technology are changing and improving how law enforcement conducts its crucial work, download Thomson Reuters’ new white paper, Data and Technology: How Law Enforcement Agencies See the Future.
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