Best Practices for Intelligence Led Policing

Critical Review of Knowledge, Gaps, and Best Practices in Interpreting in ILP

Introduction

Intelligence-led policing (ILP) is a policing strategy that relies on the use of intelligence to guide police operations. The aim of ILP is to proactively prevent and solve crime, rather than simply responding to incidents after they have occurred. A recent review of the literature found that ILP initiatives have been associated with reductions in crime and disorder, as well as improvements in police efficiency and effectiveness (Summers & Rossmo, 2019). Despite the promising evidence for ILP, there are still some challenges associated with its implementation. Interpreting is a key component of ILP. As ILP is a police strategy that relies on the collection and analysis of intelligence to identify and investigate criminal activity, interpreting is essential because when data is properly interpreted, it can help police to identify patterns and trends that may otherwise be missed. This, in turn, can help them to target their resources more effectively and to prevent crime before it happens. Additionally, interpretation can help police to build stronger cases against suspected criminals by identifying links between different pieces of evidence. However, it is important to note that data interpretation is not an exact science, and there is always the potential for human error. As such, police must exercise caution when using data to inform their decision-making. Overall, ILP has the potential to be an effective policing strategy, but its success depends on careful planning and implementation—particularly when it comes to interpretation. This paper provides a critical review of best practices of interpretation with respect to ILP.

Established Areas of Knowledge

It has been established that interpreting in ILP has practical implications. For example, Summers and Rossmo (2019) point out that as a result of interpretation, “the profiles of top offenders should be systematically disseminated to front line officers to augment the effectiveness of police patrol and minimize the possibility of crime displacement” (p. 31). However, Capellan and Lewandowski (2018) find that interpreting data based on content and characteristics of threats does not allow for a consistent assignation of risk. In other words, there is evidence indicating that interpretations of data work well but are also limited. For example, one of the primary potential risks that needs to be considered when interpreting crime data is that the content of the data may not be accurate; another is that the characteristics of the threats may be incorrectly represented; and a third is that the data may not be representative of the population as a whole (Capellan & Lewandowski, 2018). All of these factors can lead to a false sense of security or an inaccurate assessment of risk. In order to properly interpret crime data, it is necessary to understand all of these potential sources of error but also how to interpret effectively and under what conditions it can be most effective. Only then can interpretation begin to be used to assign a consistent level of risk to different types of criminal activity.

Indeed, James (2018) explains that ILP is best used to interpret when it is used to support other alternative strategies in policing. The empirical evidence suggests, as Ratcliffe points out, that ILP is a complementary approach to policing; but like the world of technology around everyone, it is one that is still growing. Just as Big Data is a field that has grown alongside the rise of technology in everyday lives, it will play an increasingly large role in policing. In fact, evidence shows that Big Data is revolutionizing the way police forces around the world operate (Brayne, 2020). By harnessing the power of data analytics, police forces are able to detect crime patterns, identify potential hotspots, and allocate resources more effectively. In addition, big data is helping police to tackle crime more proactively by identifying risk factors and individuals who are likely to commit crimes. For example, predictive policing algorithms have been shown to be effective in reducing crime rates in cities such as New York and Los Angeles. As big data continues to evolve, it is clear that it will play an increasingly important role in policing and crime prevention.

Gaps

Despite the fact that intelligence-led policing has become increasingly prevalent in recent years, there remain significant gaps in knowledge regarding how data is interpreted and used in this context. This is due, in part, to the fact that intelligence-led policing is often conducted behind closed doors, making it difficult for researchers to access the necessary data. Additionally, the interpretation of data is often highly subjective, making it difficult to draw definitive conclusions about its effectiveness (Ronn, 2022). James (2018) explains that it is important to understand why some interpret with objectivity and some with subjectivity, and where the lines can be seen in ILP. Indeed, when applying objective frameworks, studies have suggested that intelligence-led policing can be effective in reducing crime rates so long as the interpretation of data is credible and valid (Gkougkoudis et al., 2022). In one study, researchers found that when police officers used validly interpreted data to identify and target high-crime areas, they were able to significantly reduce crime rates in those areas (James, 2018). While more research is needed to confirm these findings, as Ratcliffe himself points out in his book, these findings do provide a promising glimpse into the potential of data-driven policing (James, 2018).

Best Practices

The US Department of Justice (2005) has stated that “more than 30 years ago, the National Advisory Commission on Criminal Justice Standards and Goals supported the idea that any law enforcement agency with at least 75 sworn personnel should employ at least 1 full-time intelligence professional. Best practices suggest having 1 intelligence analyst for every 75 sworn officers in generalized law enforcement agencies, with 1 for every 12 sworn officers in agencies with complex criminal investigative responsibilities, such as organized crime, narcotics, gangs, terrorism, and fraud” (p. 18). Thus, best practices in applying interpretation in ILP are based on the following: First, agencies must have access to quality data. This data can come from a variety of sources, including incident reports, arrest records, and tips from the community. But primarily there has to be a department or head dedicated to intelligence (US Department of Justice, 2005). Without such an arrangement it is not possible to apply the approach with any effectiveness.

Second, this data must be analyzed in a systematic way in order to identify patterns and trends. Police forces have long recognized the importance of data in understanding and addressing crime. In recent years, advances in technology have made it possible to gather and analyze data on a much larger scale than ever before (James, 2018). This has allowed police forces to identify patterns and trends that would otherwise have gone unnoticed. However, data must be analyzed in a systematic way in order to be of any use (Summers & Rossmo, 2018). Police officers need to be trained in how to properly collect and interpret data. Officers who are not properly trained in data collection and analysis may inadvertently bias their results, which could lead to skewed decision-making (Ronn, 2022). Without this training, the data will be of little value in preventing or solving crimes—but if, in accordance with best practices identified by the US Department of Justice (2005), once these patterns have been identified, law enforcement can then develop targeted strategies to address the problem.

Additionally, it is important to evaluate the results of these strategies on a regular basis and make adjustments as necessary. When pursuing objective analysis, it is important to evaluate the results on a regular basis and make adjustments as necessary (Brayne, 2020). This is especially true when aiming for long-term objectives, as the intervening months and years can easily lead to complacency. By regularly checking in and assessing progress, it is possible to stay on track of reaching targeted growth, consistency, and stability, as well as to be able to adjust the plan as needed (Gkougkoudis et al., 2022). Evaluations may involve changing the tactics used, the resources allocated, or even the goal itself (Capellan & Lewandowski, 2018). What is important is that the evaluation be done honestly and with an eye towards continued improvement. By following these best practices, law enforcement agencies can more effectively combat crime and keep their communities safe.

Burcher and Whelan (2019) identify “three relational themes that inhibit the successful implementation of ILP: analysts and data, analysts and tools, and analysts and decision makers” (p. 139). To overcome these obstacles, Burcher and Whelan (2019) recommend as a best practice based on evidence that interpretation be rooted in understanding: specifically “to better understand the structure and operations within law enforcement agencies, including the similarities and differences among organizational units, in order to better understand how these nuances shape” how data is interpreted in ILP (p. 139). As the empirical evidence illustrates, the way in which structures and operations among organizational units impact how data is handled can be seen in a number of ways. For example, the way in which police officers are deployed can impact the amount and quality of data that is collected (Burcher & Whelan, 2019). If officers are concentrated in high-crime areas, they will naturally encounter more criminal activity and be able to gather more detailed information. Conversely, if officers are deployed in low-crime areas, they will have less contact with potential suspects and may not collect as much useful data. Thus, it is a recommended best practice that some form of centralization of data and interpretation be implemented.

Another way in which organizational structures can impact data handling and interpretation is through the use of technology (Gkougkoudis et al., 2022). If police departments invest in sophisticated crime-mapping software, for example, they will be able to more effectively track and analyze patterns of criminal activity. However, if police departments do not have access to such technology, they will be at a disadvantage when it comes to understanding and responding to crime patterns.

Ultimately, however, the way in which police agencies interact with other organizations will impact how data is handled. For example, if police departments share information freely with prosecutors and probation officers, those agencies will be better equipped to make use of the data (US Department of Justice, 2005). However, if there are strict barriers to information sharing, it will hinder the effective use of data. In sum, the way in which structures and operations among organizational units impact how data is handled is significant and should be taken into consideration when designing policing strategies.