Intelligence-Led Policing: Adoption, Implementation, and Challenges

Introduction to Intelligence-Led Policing

Over time, policing methods have advanced considerably, with the most recent strategy for improving police response being intelligence-led policing (ILP). ILP is still in its initial developmental stages, is not yet wholly understood, and has not been adopted by all agencies (Taylor, Kowalyk, and Boba, 2007). Studying police managers' views and attitudes can help identify obstacles. When initiating this strategy, top police officers benefit from research findings that enable them to foresee problems and understand supervisors' mind-sets. Strategy transformations spring from shifts in objectives. For instance, London's Metropolitan Police was organized by Sir Robert Peel to focus not on response but on the prevention of crime (Johnson, 1988). Improvements were subsequently generated through technological advances such as automobiles and telephones, which served to reduce response time and expand an officer's patrol coverage (Phillips, 2012).

Intelligence-driven methods have their roots in financial limitations, which are a modern policing reality. New public control of policing via financial audits (Crawford, 1997) and growing demands for information from a risk-conscious society (Ericson and Haggerty, 1997) have produced a wealth of statistical information, enabling external agencies to monitor police performance. A large contribution to the intelligence-led policing approach traces back to two influential UK reports dealing with value-for-money strategies and financial management (Ratcliffe, 2002).

Policing is an endeavor that revolves around adequate, accurate knowledge. Over the past fifteen years, voices have called for a shift from a reactive, intuition-driven, traditional policing approach to one that is intelligence-driven, pre-emptive, and proactive (Collier, 2006). The latter promotes the use of evidence-based, accurate data and evaluations to provide direction to management and guide police action at every level. The aim is to complement intuition-driven actions with knowledge obtained from assessments of accumulated operational data, such as criminal characteristics and crime figures (Collier et al., 2004; Viaene et al., 2009).

Notably, ILP underscored the significance of supporting decision-making in policing with improved information at every organizational level. It serves as a framework for developing the process and technology integration essential for gathering, analyzing, and delivering relevant intelligence to police in their efforts to curb crime and safeguard the public. It also acts as the ideal facilitator of an operationalized business intelligence approach with enterprise-wide aims (Viaene et al., 2009).

ILP sometimes avoids the detailed parameters of a practical operational environment. The Amsterdam-Amstelland Police Department (AAPD), for example, has centered itself on a more advanced framework — CompStat — to drive its efforts. CompStat was introduced in 1994 by the New York City Police Department (NYPD) (Weisburd et al., 2004). This strategic, goal-oriented process makes use of operational strategy, information technology (IT), and managerial accountability to guide police actions (Walsh and Vito, 2004). It aims to instantiate ILP in U.S. policing, setting an example for other international police departments (Viaene et al., 2009).

Implementation of Intelligence-Led Policing

Beginning in the mid-1980s, declines in the cost of digital information storage and computing power, accompanied by the streamlining of digital operating systems, enabled the widespread diffusion of new technologies that facilitate crime fighting. With computerized police records allowing better managerial and statistical operation of police departments, it became apparent that these records could have potential use in criminal intelligence, crime investigations, and crime mapping. Law enforcement officials' interest in adopting geographic information systems for mapping crime incidence developed in tandem with research identifying criminal behavior and crime patterns in the emerging field of environmental criminology (Ratcliffe, 2004a).

With the introduction of ILP, improved IT was viewed as a solution for managing the anticipated surge in intelligence and information resulting from the new approach. The importance of an efficient IT intelligence system was recognized by police agencies as well as by businesses rapidly filling this niche market. Intelligence systems replaced conventional collator cards, as the former can store and sort large quantities of data; consequently, data requirements have also grown. However, several issues remain with police databases that must be addressed (Ratcliffe, 2002).

Many researchers have offered alternative explanations for bureaucrats' enthusiasm to gather information. They have indicated that key changes in society at the turn of the twentieth century contributed to rising public demand for information. Communities' growing fragmentation, the growth of a risk society, and heightened insecurity and fear have generated an enormous need for enhanced knowledge and security (Maguire, 2000). Private sector firms are filling this market — a trend evident in the growth of private security companies, insurance companies, burglar alarm manufacturers, and shopping-mall security guards. The public sector must also address its need for enhanced information, and police forces are now regarded as the primary custodians of crime-related knowledge (Ratcliffe, 2002).

ILP is a cutting-edge approach to crime prevention. It is a targeted, strategic, and future-oriented strategy that concentrates on the recognition, assessment, and management of existing and emerging risks (de Lint, 2006). ILP does not merely amass crime information; rather, it integrates data into policing's strategic mission, including efforts to curb terrorist and criminal activities (Carter and Carter, 2009). Still in its embryonic stage, ILP has yet to mature sufficiently for thorough examination of its success. Consequently, some agencies may choose to wait until the approach develops further in other police organizations. Despite potential barriers, ILP is expected to become a widely adopted strategy in the future (Phillips, 2012).

The National Intelligence Model (NCIS, 2000) offers an operational framework for organizing policing-related intelligence. In brief, the model supports an organized procedure of collecting, storing, and examining intelligence to back a tasking conference that analyzes issues and assigns police resources accordingly. The model endorses a specialized meeting structure for developing both rapid and long-term solutions to problems. It also defines numerous analytical products that must be produced to support the tasking process. The model rests on a clear distinction between crimes with local effect that can be handled by a single command unit (level 1); cross-border offenses affecting two or more command units (level 2); and national and global-level crimes (level 3) (Cope, 2004).

Street-level police officers are vital sources of primary data (Manning and Hawkins, 1989), which is crucial to crime analysis. However, the authority associated with controlling access to information resources represents a significant barrier embedded in the "need-to-know culture" surrounding intelligence work. The analysts' role as translators of information poses a potential challenge to officials' power; as Chan (2001, p. 146) notes, because information represents a source of power, IT can result in power struggles. IT can undermine street-level officers' autonomy and discretion while simultaneously enhancing the status of IT specialists. The potential value of primary data may explain why it is seldom shared (Cope, 2004).

Why Police Have Adopted Intelligence-Led Policing

The significance of understanding and managing risk in modern societies has shaped crime-control systems (Maguire, 2000). ILP exemplifies concerns with identifying, prioritizing, and intervening to reduce risk. Intelligence may be defined as information developed to guide police operations. Five stages are central to the ILP process. The first is Acquisition, which highlights the need for an information framework to help police forces gather, store, and catalogue information for cross-examination. The second is Analysis, which transforms raw data into actionable information by identifying crime patterns, linking criminal activities, or constructing detailed suspect profiles. The third is Review, which provides the means for prioritizing intelligence so that the most critical risks and crimes can be acted upon first. The fourth stage, Dissemination, concerns the distribution of intelligence products to relevant decision-makers. The fifth is Evaluation of Action, carried out by way of analysis. Research has shown that this stage is rarely completed, as analysts often lack the capability to conduct evaluations and receive limited feedback from operational officers regarding action taken. The process may be linear — with information passing sequentially through individual stages — or cyclical, incorporating feedback at every stage (Cope et al., 2001; Cope, 2004).

Crime investigation is critical to ILP's effectiveness, as it attempts to deliver the right data to the right individuals at the right times (Fletcher, 2000, p. 114). This investigation integrates information gathering and review into practical summaries — such as network charts or crime maps — for easier interpretation. When officers better understand the nature of criminal problems, recommendations for police action naturally follow. Throughout research, the notion of analysts offering recommendations regarding police tasking generated substantial debate among officers and analysts alike. Civilian criminal analysts were concerned about their limited expertise in policing operations (Cope, 2004).

ILP strives for greater policing efficiency, but it coexists with other, sometimes conflicting, efficiency measures. Many police organizations use performance metrics that attempt to measure every possible factor; consequently, ILP's benefits may be obscured by a culture of micro-management and operational statistics (Ratcliffe, 2003).

Australia's New South Wales (NSW) Police Service relies on the Computerized Operational Policing System (COPS) for the bulk of its operational policing IT support. This large system handles a wide range of tasks, including calls for service, intelligence reports, crime recording, and traffic accidents. The system suffers from a problem common to several police IT systems: obsolescence. COPS is slow and cumbersome; recording a single theft may require an officer to navigate numerous information screens in a non-mouse, non-Windows, text-based environment. One IT trainer at the NSW Police College noted that probationary officers new to the system could take more than an hour to record the details of a simple burglary. Under such conditions, information is likely to be omitted to save time, consequently reducing information quality and intelligence value. NSW's police service is not unique in having an IT infrastructure mismatched to intelligence requirements (Ratcliffe, 2002; Anderson, 1997).

A system known as PROMIS is used by the Australian Federal Police (AFP) and also by the National Crime Authority and Northern Territory Police. Data entry in PROMIS is more user-friendly than in COPS due to its Windows environment. However, PROMIS is a case management system, not an intelligence system. While entering data is straightforward, extracting valuable intelligence is an entirely different matter, often requiring data cleaning and the removal of duplicate entries. In several police services, new technology takes a long time to implement, and before deployment is complete, commercial and policing developments elsewhere have frequently produced something more advanced. Implementing case management or statistical systems as makeshift intelligence systems — without recognizing the flexible requirements of contemporary policing intelligence — routinely creates a substantial constraint on criminal intelligence capability. Collating details across different incidents is only one aspect of the intelligence cycle; upon collation, information must be analyzed in conjunction with other relevant data before worthwhile intelligence can be produced (Anderson, 1994; Ratcliffe, 2002).

In recent years, an expansion has occurred in sophisticated investigative tools available to intelligence officers. These include generic data-handling programs such as Access and Excel, which can be used to sort incident and crime databases, as well as professional software packages such as Analyst's Notebook from i2 and Xanalys' Watson, specifically designed to assist crime investigations. Geographic Information Systems (GIS), including ManInfo, can map incidents and crime, adding a spatial dimension to intelligence work. ManInfo is used alongside other analytical tools by NSW Police in its Operations and Crime Review (OCR), a platform for executives to evaluate a Local Area Command's performance in reducing crime, supplemented by analytical and cartographic support that charts crime patterns over time (Anderson, 1994, 1997; Ratcliffe, 2002).

It became evident that new intelligence units required analytical skills to generate genuine intelligence. Initially, a dedicated support staff of civilians was recruited to carry out this analysis. Early speculation that this role required a policing background proved unfounded. Analysts are regarded as key members of intelligence units and report directly to an Intelligence Coordinator drawn from the ranks of police inspectors. From an initial team of twelve analysts — with at least one in each area's intelligence team — the analytical organization grew to twenty-six members, with no fewer than two in each area (Anderson, 1997).

Analysts undertake crime-pattern examination and intelligence assessment work. A series of standard products has been developed to convey analytical findings to operational staff and managers. A weekly report reviews crime levels by geographic location and offense type. Priority locations and crimes are analyzed further and integrated with current intelligence on suspects to produce well-informed recommendations for forthcoming intelligence collection and enforcement activity. Weekly reports also serve as a key source document for weekly resourcing conferences — a British variation of the NYPD's CompStat process. Standard analytical products provide preset points for investigative contribution while permitting individual exploratory support (Anderson, 1997).

Conclusion

The initiative toward ILP must be tempered through pragmatic expectations. A review of the literature indicates that police agencies' ability to influence societal crime levels is limited, although evidence suggests that some degree of crime reduction is achievable. This may appear to undercut the case for ILP; however, an analysis of the wide-ranging potential causes underlying crime suggests that only a small proportion of those causes lie within police's sphere of influence. Through collaboration with external agencies, however, police can affect a broader range of contributing factors. There is growing recognition among police forces that outside agencies — including housing authorities, local councils, and education and health departments — have greater potential to influence a wider array of crime-related factors. Police are well positioned as "gatekeepers" to large quantities of crime information, and enhanced intelligence sharing beyond law enforcement boundaries may yield more substantial crime reductions. If adopted more effectively, the term ILP may itself become a misnomer — arguably better replaced by "intelligence-led crime reduction."

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