Integrating Law Enforcement Data to Deliver Accurate, Timely Information and Valuable Foresight

Transcription

1 White Paper Integrating Law Enforcement Data to Deliver Accurate, Timely Information and Valuable Foresight Law enforcement s opportunity to prevent crime with data integration, reporting and predictive analytics

2 Contents Introduction... 1 Law Enforcement s Premise... 1 Challenges Confronting Today s Law Enforcement. 1 New Opportunities... 2 Building an Information Repository... 2 Good, Reliable Data... 2 Collection of Varied Data Sources... 2 Delivering Useful Information and Insight... 3 Creating a Prevention-Focused Strategy... 4 Thinking Beyond Law Enforcement... 5 Conclusion... 5 About SAS... 5

3 1 Introduction The following quote from the International Association of Chiefs of Police embodies the challenges of today s state and local law enforcement agencies. Law enforcement leaders have obligations beyond the scope that any of them may have imagined a decade ago. Not only are they responsible for detecting, preventing, responding to, and solving crimes against people and property in their communities, they are now tasked with a more global approach to policing that challenges resources, skills, and personnel. 1 A sea change arrived for the law enforcement profession immediately following the terror attacks of Sept. 11, 2001; that change affected the scope of the more than 18,000 law enforcement agencies in the United States. The ramifications were swift, significant and vast; law enforcement officers were no longer just asked to serve as hometown protectors but also as homeland protectors. Over the last decade the increased responsibilities have been met with a resultant increase in proffered tactics, strategies, new technologies and resource challenges. While there are significant and complex challenges, the change in state and local law enforcement s scope provides an opportunity for agencies to evaluate where they are, where they have been, how to move forward individually and how to move forward collectively. Law Enforcement s Premise Sir Robert Peel was a cabinet-level official in 19th-century Great Britain, and via the Metropolitan Police Act of 1829, laid the foundation for the modern-day professional police force. In shaping the conceptual framework for policing, Peel focused on concepts like legitimacy, community integration and use of force. However, the crux of the policing mission can be found in Peel s first tenet: The basic mission for which the police exist is to prevent crime and disorder. Peel s philosophy, which provides a baseline understanding of policing and the prevention concept, is as sound today as it was in Challenges Confronting Today s Law Enforcement While Peel described a proactive ideal, many law enforcement agencies are presently forced into a reactive stance via running from call to call. The calls-for-service model is presently built from necessity and focuses on short-term response, drains already finite resources, and prohibits longer-range strategic approaches to solving crime and disorder problems. This necessity-driven, reactive approach derives from myriad challenges facing public safety entities: Lack of resources. The global economic recession has caused many law enforcement agencies to reduce both sworn and civilian personnel. These staffing reductions are compounded by parallel decreases in needed reductions in tax-supported equipment, training and services. Changes of scope. A constantly changing criminal environment and increased calls for state and local involvement in national security efforts has increased the number and type of complexities for law enforcement personnel from the patrol officer to the chief executive. Nonintegrated information. Individual law enforcement agencies often lack the necessary skills, time and/or technologies to pull together operationally relevant intelligence from vast sources of internal and external data. Without the ability to see the big picture, critical information often goes unseen. Opportunities for law enforcement will be driven from the result-oriented ideal of delivering the right information to the right people at the right time and place. 1 International Association of Chiefs of Police, Enhancing the Law Enforcement Intelligence Capacity, February 2010.

4 2 New Opportunities As law enforcement agencies will not likely see an influx of personnel or discretionary funding anytime soon, new opportunities to innovate and advance policing will be born out of sheer necessity. Likely, those new opportunities will emanate from the maximized utilization of the resources that remain: existing staff and accessible information. Opportunities for law enforcement will be driven from the result-oriented ideal of delivering the right information to the right people at the right time and place. While potentially perceived as overly simplistic, the above ideal is as valid today as it was during Peel s time. Achieving the ideal while coping with operational realities within a law enforcement environment can be fulfilled through data integration, reporting and predictive analytics methods. Obtaining the right information to drive decision making means that law enforcement must obtain: 1) good, reliable data from 2) a collection of varied data sources that will help provide the clearest possible picture for the law enforcement official. Building an Information Repository Obtaining the right information to drive decision making means that law enforcement must obtain: 1) good, reliable data from 2) a collection of varied data sources that will help provide the clearest possible picture for the law enforcement official. Good, Reliable Data Law enforcement agencies have a wealth of internal information at their disposal. Further, police regularly access data from other criminal justice entities, state or urban area fusion centers, federal agencies and other partners. In many cases, data available from multiple internal or external sources contains overlapping, complementary information on a given event, person or place. The information is contained in various structured and unstructured formats and is often susceptible to human error. Filtering, cleaning and validating the available information is the first step in turning massive amounts of data into useful knowledge (i.e., the right information ). Solid data is the foundation for a data-driven policing strategy. Collection of Varied Data Sources Law enforcement information is collected through a variety of disparate systems, including, but not limited to, computer-aided dispatch, records management systems, criminal informants and warrant databases. Each individually housed piece of data provides a single snapshot of an offender, a victim, a place or a time. While necessary to maintain, these independent snapshots of data provide limited insight as to how they relate to the other pieces of data or an overall context. However, while individual pieces of data may appear to be inconsequential, once combined with other pieces of information, they could help address a larger critical operational or policy issue. Operational insight and useful information are built by marrying the disparate sources to expand the single frame of reference into a linked, informed picture to better understand a given public safety issue and to build an effective response. This approach allows law enforcement agencies to view crime and disorder comprehensively in order to quickly obtain critical information and develop needed responses. This integrated approach provides a holistic view of crime and disorder. Without a complete, holistic view of the problem faced, any implemented solutions will only address a limited portion of the issue. A complete view of crime and disorder provides numerous benefits: Informed operational insight. Law enforcement is at its most effective when it has a full complement of information. Effective use of resources. Maximize the value of resources by discovering needs. Enhanced community engagement. Educate and engage the community to identify and mitigate community threats more quickly. How can law enforcement agencies maximize the value of internal and external data sources? A holistic view of a public safety issue allows police agencies to comprehensively review the issue to fully understand its depth and breadth. For example, suppose a state police agency finds that a disproportionate number of fatalities are occurring on a stretch of a roadway. A comprehensive view of the situation can enable causal factors and potential solutions to be determined.

5 3 To address the issue, the state police identifies relevant data sources to strategically target the problem. In addition to its own enforcement data, it identifies relevant data from engineering, education and emergency services perspectives. Through the integration of these disparate enforcement and non-enforcement data sources, the respective agencies can holistically examine the problem through a comprehensive view of all available information. The integrated data maximizes the value of each data source by providing insight into the scope of the problem and helps craft a more complete response. For example, the data may drive operational change such as additional median barriers, improved signage or repositioned emergency services or enforcement activities. Operational insight and useful information are built by marrying the disparate sources to expand the single frame of reference into a linked, informed picture to better understand a given public safety issue and to build an effective response. How can fusion centers utilize data integration methods to deliver meaningful intelligence and operational insight? Suppose a major city s fusion center is tasked with ingesting suspicious activity and crime data, analyzing the information and building useful intelligence to protect its people and property. Data comes into the fusion center from a variety of sources, including federal entities, police department personnel, surrounding agencies, citizen tip lines, other fusion centers and private companies. Given the broad and varied purposes of the data, an effective and efficient data integration strategy is imperative so as to adequately analyze potential terrorist and criminal threats and, therefore, potentially prevent a catastrophe. Delivering Useful Information and Insight Today s law enforcement agencies are striving to do more with less and to be smart with finite resource allocations. In an effort to reach their agency goals amid financial struggles, agencies are working to optimize resources. They are relying on all levels of their organizations to be accountable and make good decisions. Data integration fuels this organizational accountability via reporting what the data indicates. Reporting, or business intelligence, is the graphical depiction and interpretation of the data. These reports provide operational and strategic insight for law enforcement decision makers. Given the flexibility of reports, the generated reports can be customized to serve the needs of the user whether it is the chief, a first-line supervisor, an officer or a community member. Reporting can be broad-based to provide an aggregate view of crime data across a given jurisdiction, or it can be granular to determine how many robberies occurred in a given month in a specific patrol district. How can law enforcement agencies advance operational efficiency and organizational accountability? Municipal police chiefs are responsible for the service and protection of the community s citizenry. To ensure this responsibility is met, they establish a strategic approach to prevent crime and disorder and to ensure organizational accountability. Reporting can drive executive-level strategic directions, promote accountability regarding agency performance, engage community members, or guide an officer s interactions with the community. For example, providing front-line officers with real-time data can assist in readily identifying potential threats, putting in place needed interventions and solving community issues before they become long-term problems. For police leaders, adequately equipping officers with quality, timely information fosters problem identification and increased organizational accountability. Reporting can be broad-based to provide an aggregate view of crime data across a given jurisdiction, or it can be granular to determine how many robberies occurred in a given month in a specific patrol district.

6 4 Detailed, real-time reporting provides the assessment of organizational inputs and community outputs: How is patrol time being spent? What are our patrol patterns? Could our resources be maximized elsewhere? What types of activities are we focused on? Are we modifying our approach based on results? Are the patrols effective? Are crime and disorder increasing or decreasing? Is crime being displaced or is it being splintered into smaller groups? Is there a correlation between what the officers are doing and what is happening in a given neighborhood? What is the community s reaction to what the agency is doing? Reporting can drive executivelevel strategic directions, promote accountability regarding agency performance, engage community members, or guide an officer s interactions with the community. Creating a Prevention-Focused Strategy Obtaining the right information and relaying it to the right people is paramount for law enforcement; acquiring this information at the right time and place is even more critical. Law enforcement can use predictive analytics methods to shift from focusing on what happened to focusing on what may happen. Predictive analytics uses historical and current data from multiple sources to identify trends, discover interrelationships and create models to help anticipate crime and disorder. This allows law enforcement to look ahead toward the future by examining data in innovative ways in order to more proactively address the public safety needs of a community. The possibilities range from the practical application of deploying officers to areas where crimes are likely to occur to the complex assessment of patrol patterns and their impact on crime and/or calls for service. Law enforcement can use predictive analytics methods to shift from focusing on what happened to focusing on what may happen. While focusing on when and where crime and disorder occurs through integration of data, predictive analytics allows law enforcement to dig even deeper to discover why crime occurs and when and where it may occur in the future. Given this analysis, strategies can be created that tactically target shortterm issues and strategically address the long-term causes of crime and victimization. Predictive analytics provides law enforcement with: Valuable foresight. The operational insight generated from analytics allows agencies to position resources so as to prevent, versus respond to, acts of crime and disorder. Optimization of resources. Analytics enables law enforcement administrators to optimally allocate finite, scarce resources to maximize community safety. Strategic directions. Efficient and effective response to tactical situations will allow for more time devoted to strategically tackling long-term, complex problems. How can law enforcement agencies maximize their ability to prevent crime, disorder, crashes and other threats? Local police department: Many local police departments are looking to address crime more strategically by reversing their response model to a more proactive approach. Police departments can use integrated data to build predictive models that determine the probability of criminal occurrence, both in time and space. These models can facilitate the building of staffing deployments and adjust patrol districts to meet the challenges posed by the changing crime dynamics.

7 5 State highway patrol: Due to recent economic conditions, many state highway patrols are confronted with decreased staffing. In an effort to gain operational insight on where best to place limited resources while maintaining the core mission of preventing fatalities and injuries on the roadways, the state patrols can utilize crash analytics methods. Crash analytics builds predictive models to determine where crashes are most likely to occur under different scenarios and conditions. Such a patrol model allows for the strategic positioning and deployment of resources based on operational insight created by the patrol s own data. Fusion center: Predictive analytics enables fusion centers to build strategic and tactical responses for single jurisdictions as well as multiple jurisdictions. For example, suppose a state-based fusion center has assumed an all-crimes approach and is providing statewide analytic support to local agencies in battling gun violence. The center is ingesting data from local agencies on acts of violence committed with firearms. Conclusion Data integration, reporting and predictive analytics methods introduce a tremendous opportunity for law enforcement to improve crime prevention through accurate, timely information and valuable foresight. Additionally, these methods allow law enforcement to strategically position resources efficiently and effectively. A well-implemented approach provides a foundation to increase both community and officer safety, as well as quality of life for the citizenry. About SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions delivered within an integrated framework, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW. Using this collection of data, the fusion center analysts can build a probability of occurrence map. From the predictive maps, law enforcement can build proactive responses to the gun crime issue including prevention, suppression and intervention tactics. Thinking Beyond Law Enforcement What issues could be resolved if other data sources from city, state and federal services could be integrated with law enforcement data into an analytic environment? What if you could integrate law enforcement data with: Population change data? Youth demographics? Social service statistics? Unemployment data? Public school statistics?

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