Risk-Based Policing: Evidence-Based Crime Prevention with Big Data and Spatial Analytics

Risk-Based Policing: Evidence-Based Crime Prevention with Big Data and Spatial Analytics

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Overview

Risk-based policing is a research advancement that improves public safety, and its applications prevent crime specifically by managing crime risks. In Risk-Based Policing, the authors analyze case studies from a variety of city agencies including Atlantic City, New Jersey; Colorado Springs, Colorado; Glendale, Arizona; Kansas City, Missouri; Newark, New Jersey; and others. They demonstrate how focusing police resources on risky places and basing police work on smart uses of data can address the worst effects of disorder and crime while improving community relations and public safety. Topics include the role of big data; the evolution of modern policing; dealing with high-risk targets; designing, implementing, and evaluating risk-based policing strategies; and the role of multiple stakeholders in risk-based policing. The book also demonstrates how risk terrain modeling can be extended to provide a comprehensive view of prevention and deterrence.

Product Details

ISBN-13: 9780520295636
Publisher: University of California Press
Publication date: 11/20/2018
Edition description: First Edition
Pages: 168
Product dimensions: 6.90(w) x 9.80(h) x 0.60(d)

About the Author

Leslie W. Kennedy is University Professor of Criminal Justice at Rutgers University and Director of the Rutgers Center on Public Security.

Joel M. Caplan is Associate Professor at Rutgers University’s School of Criminal Justice and Deputy Director of the Rutgers Center on Public Security. He has professional experience as a police officer, 9-1-1 dispatcher, and emergency medical technician.

Eric L. Piza is Associate Professor at John Jay College of Criminal Justice, City University of New York. Prior to joining academia, he served as the Geographic Information Systems Specialist for the Newark Police Department in New Jersey.

Read an Excerpt

CHAPTER 1

INTRODUCTION TO RISK AND BIG DATA

KEY POINTS

• Risk-based policing focuses primarily on places and not people.

• Risk analysis provides evidence-based support for risk narratives about how factors combine to increase the probability of crime occurrence.

• Risk reduction strategies include specific information about where to go and also what to do when you get there.

• Risk-based policing encourages community engagement and multi-stakeholder participation in crime prevention through risk reduction activities.

INTRODUCTION TO RISK-BASED POLICING IN CRIME PREVENTION

In discussing how crime prevention tactics have evolved, Bratton and Kelling (2012) admit that police too often focus on arresting their way out of crime problems. Still, they advocate that there needs to be a strategy for policing that is problem-oriented, properly resourced in terms of personnel, and targeted at the locations that need the most attention. Bratton and Kelling observe, correctly we believe, that police agencies have become very sophisticated in their problem-orientations, even leading other municipal agencies in their ways and means. This trend has been tied to the increased training and openness to higher education of police leadership, coupled with commensurate enabling sources of funding (often federal grants), making them receptive to new ideas and better planning. In this context, there is an openness (among at least some police agencies) to experiment and to expand on their missions. This has happened even when they have been confronting severely challenging expectations brought on by major divestments in urban areas after the global financial collapse of the early twenty-first century and discontent with the effects of policing practices on various community populations and constituents. We frame these problems in this book to propose risk-based policing as an effective and sustainable approach to a new policing frontier and social climate, based on evidence and insights from research, policy, and practice.

Risk-based policing considers more than the mere possibility that crime will occur, which has been a long-standing focus of criminology, crime prevention, and policing studies. Risk-based policing advocates addressing the contextual reasons for crime emergence and persistence at particular places. It does not rely on an actuarial count of the numbers of offenders and victims, or the potential that the one will inevitably prey on the other. It seeks to avoid a simplistic view of crime incident clustering that ignores the factors that repeatedly enable these behaviors at certain locations. It considers more than a focus on individual personal characteristics and looks at places that attract or repel criminal behaviors based on certain qualities. These qualities can be identified, operationalized, and compiled to determine their influence on criminal outcomes. Assessment of spatial influences of environmental conditions is not arbitrary, though. It is set in risk terrain models, where vulnerability to crime is diagnosed and charted based on pattern analysis, past experiences, and comparisons to other similar places. Risk provides the metric to be standardized, scaled to different levels of investigation, and contrasted over place and time.

THE IMPORTANCE OF RISK

Risk is a common metric, calculated by everyone who enters a particular landscape. We develop an understanding of how environments can protect or threaten us through visual cues, reputations, and perceptions that form fears, feelings of safety, or insecurity. Environments exhibit identifiable patterns. People size up what a location looks like and then judge it against their memories and assumptions about the causal links between certain environmental features and the likelihood that these will support criminal activity. Obviously, based on experience or knowledge of an area, these perceptions will vary, and may oftentimes be inaccurate. But even initial perceptions matter. Experiences, knowledge, and perceptions will also be different among various stakeholders depending on the roles that they play in these environments. Police see places they patrol through a different lens of risk than do inhabitants, tourists or passers-through. These risk assessments are important in shaping the expectations that each stakeholder forms in interacting with people at various places across the landscape.

Through advances in technology and better data we have been able to articulate the spatial influences that help form these risk perceptions. We have better evidence about how environments link to human behavior. It is this information that risk-based policing embraces to manage resources to mitigate these risks to prevent crime and enhance public safety. Risk terrain modeling (RTM) is an analytical engine for this problem-solving enterprise. It is embedded in a larger ecosystem in which decisions are made about the best ways to deliver services while reducing risks to police personnel and members of the community being served. With RTM, risk-based policing accounts for the insights that police officers and others who live and work in these communities bring to the table. These stakeholders, when encouraged to think in terms of risk governance, identify what (from their experiences) impacts on the quality of life in communities and leads to conditions that are ripe for crime. Their testimonials form an important set of data for law enforcement to aid risk governance and shape the results of RTM into multilateral plans for action.

Efforts by police agencies to reach out to community leaders and other stakeholders have benefited police by improving their relationships with these groups, particularly as they demand more accountability and transparency. But it is important that the information gleaned from these interactions consists of more than just complaints. Community engagement can be an integral part of the mechanism for police to share the burden of public safety with other stakeholders and to implement risk reduction programs in ways that meet everyone's expectations. When community members realize their role in crime prevention, they become partners to help solve existing crime problems and to identify and address emerging public safety threats. This keeps police officers safer, too.

Important in all of this, as well, is the understanding of what offenders use as indicators of their successful criminal behavior. Many criminals continue to operate in locations where conditions support their illegal activities, such as drug markets, or facilities that can attract illicit behavior, such as bars or convenience stores. Divestment in some cities, compounded by major economic failures, such as the housing foreclosure crisis, have changed the social relevancy of landscape features in many areas and made them high-risk for illegal outcomes. One advance in policing within the last few decades is an operational response to the fact that crimes cluster around other crimes over time, a phenomenon termed hot spots and a practice of hot spots policing. The advancement that underlies risk-based policing derives from an emphasis on the fact that features of the landscape also concentrate and interact, which explains why crime emerges or persists where it does. Hot spots policing, by going to hot spots and making arrests, which consequently increases the intensity of the hot spot, is self-fulfilling. This is offset by a risk-based approach that reduces crime in a way that does not rely on repeated crime occurrences in order to make new deployment decisions. The risk perspective shifts the focus from problem orientation to risk mitigation by considering all aspects of the environment, not just the role of the offender or the cluster of known crime incidents. Focusing on mitigating risk to achieve crime drops means the features that once attracted illegal behavior become less likely to encourage crime, which results in less attractive behavior settings for criminal offending. Vulnerability to crime is reduced, so the crime reduction is more sustainable and long-term. But, further, because police can measure what contributed to these risky conditions in the first place, they have a better understanding of what works and what does not in responding to and deterring criminal behavior. As a consequence, prevention strategies can be transported to other similar locations within the same jurisdictions, and implemented there with expectations of repeated success. This takes part of the guesswork out of policing and provides more evidence-based validations of best practices.

BIG DATA

Views and opinions of "places" have changed as technology and data have improved. Municipalities now collect more detailed and accurate geocoded information, which permits police not only to describe criminogenic places, but also to better understand the relationships between features of the environment and criminal behaviors. In addition, we can visualize places through tools like Google Street View, which adds real context to places and allows us to search them quickly and easily for cues about illegal behaviors that might be enabled or emerging. We can overlay on these places information about people who use them and how they inhabit them at any time of the day, week or year. We can understand flows of people and concentrations of behavior. With social media and surveys, we can compare real-time and historical perceptions, allowing a better understanding of how people view their places. We can monitor law enforcement activities and police patrols using spatial technologies to help judge resulting solutions to crime problems. Now, the picture related to data is not completely rosy, as there are excesses and distortions that can come with systematically biased, improperly managed, or inadequately analyzed data (Ferguson 2017). But the data revolution has made an important impact on all aspects of life in modern society, and its role in policing has been dramatic. The positive contributions of data to crime prevention and risk reduction provide an important subtext to risk-based policing.

RISK-BASED POLICING

The advent of risk-based policing does not start a new era unrelated to ideas and practices that have led up to this point in time. In considering the issues of crime analysis, risk, and big data, we need to start with an understanding of the origins of policing and its progression to the modern era. Risk-based policing is an evolution, not a revolution, and its impacts are likely to advance policing only if we understand how we have come to the current state of affairs. Basic expectations and responsibilities of police will be documented in our review of the evolution of police up to the current century. This history of policing, presented in chapter 2, is the starting-off point to provide context to our ideas of integrating new information and modern analysis methods into the policing profession and public safety practice. We are hopeful that a consequence of this advocacy will be a shift in attitudes about the role police officers play in managing public safety and solving crime problems through risk governance and strategic partnerships with members of the communities they serve. Ultimately, risk-based policing is as simple as 1–2-3. As shown in figure 1, risk-based policing requires repeated cycles of (1) assessing environmental risks, crime patterns, and event contexts; (2) deploying people and resources to areas that need them most, then implementing risk reduction strategies at these places; and (3) checking for success by measuring desired outcomes in ways that inform the next round of risk assessments and deployments.

The ideas of risk-based policing are well imbedded in the theoretical approaches to crime analysis that criminologists, data analysts, and legal scholars have developed over the years. This lead-up serves as the bridge that we use for turning research into practice. Not only are we committed to explaining the ways in which risk-based policing with RTM can be used for risk governance, but we also strongly believe that we need to get all the elements of the process working correctly, and to address the pitfalls posed by poor data, improperly formulated research questions, and false conclusions. So, we spend some time in this book explaining our efforts at investigating new ways of analyzing data, selecting target areas, developing risk reduction strategies, presenting outcomes, and offering conclusions in ways that can be easily understood and made actionable by police agencies who want to engage in this enterprise.

CONCLUSION

In this chapter, we introduced the concept of risk-based policing, which addresses the contexts in which crime occurs and helps police focus on the underlying factors that contribute to these undesirable outcomes. This approach turns attention away from individuals and the clustering of their activities in hot spots and toward risky places that promote and support criminal behavior. These behavior settings can be defined by the combined spatial influences of environmental features that enhance the probability of crime, measured through the deployment of RTM to analyze the abundant spatial data now available to law enforcement agencies. Through the development of risk narratives, informed by empirical analysis, police can develop strategies for intervention and long-term crime prevention. In addition, using a risk perspective, they can evaluate the impact of their actions and develop sustainable programs to continue the positive outcomes of their crime reduction strategies. These evidence-based approaches can be shared with community members to advance collaborative problem-solving that involves stakeholders from many different backgrounds in addressing public safety issues.

In the next chapter we delve into a short overview of the history of policing, from the progressive era up to the modern day, as a way of contextualizing the role that evidence-based approaches play in crime prevention and problem solving. This sets the foundation for our more detailed presentation of the ways in which risk-based policing builds on past policing practices and offers a way to extend crime prevention successes while overcoming the challenges.

CHAPTER 2

THE EVOLUTION OF MODERN POLICING

KEY POINTS

• Policing has historically been subject to major innovations, most notably with the establishment of the professional era of policing that formed the basis for current police management structures and procedures.

• Evaluation of police strategies has demonstrated mixed success in advancing law enforcement practice through the professional model, but recent efforts to open police to evidence-based procedures have resulted in improvements in crime prevention outcomes.

• Advances have been made through recent incorporation of modern technology and big data in crime forecasting.

• Risk reduction strategies encourage police to experiment with more place-based interventions to overcome the complaints about overly aggressive, person-focused policing.

INTRODUCTION

The history of policing is bookmarked by distinct points in time that brought about significant changes in the mission and strategy of police. Whereas policing was once a highly politicized, ineffective institution, now there exists a great deal of empirical evidence demonstrating the ability of police to prevent crime and promote public safety. This transformation was made possible by a willingness of police leaders to adopt strategic innovations during particular times of crisis. The evolution of policing accelerated by crisis-catalyzed reforms and watershed moments is noteworthy, as innovations in the field vastly outpaced most other criminal justice institutions (Skogan and Frydl 2004). Nonetheless, contemporary policing is far from infallible, and there is certainly room for continued innovation and improvement.

Several scholars have argued that policing now finds itself in another era of crisis, with high-profile police use-of-force events highlighting rifts in the relationship between police and citizens, particularly in minority communities. In addition, while we have a body of evidence regarding the effects of general police strategies, much less is settled regarding the precise actions police officers should take when engaged in operational practices. While research has found that focusing officer deployment in geographic crime hot spots reduces crime, a review of the literature suggests that "what exactly police should be doing in crime hot spots remains an open question" (Haberman 2016, 489). Yet, what officers do in hot spots has great importance, because policing tactics have the potential of alienating communities in need, regardless of their real or perceived crime prevention utility (Sweeten 2016). In addition, while the identification of hot spots and subsequent deployments of resources to such areas is an increasingly common practice in contemporary policing, much less work has been done to contextualize hot spots. While criminologists have dedicated effort to identifying geographic features associated with hot spots (Bernasco and Block 2011; Caplan, Kennedy, and Miller 2011; Kennedy, Caplan, and Piza 2011), much less attention has been paid to developing crime prevention strategies that are tailored to the contextual features of these environments. Police have historically focused interventions on the people located within hot spot areas in an effort to remove certain ones to prevent criminal opportunity. For example, the treatment prescribed for hot spot areas in New York City in the 1990s was a "broken windows" style of policing whereby lower-level offenses were given higher priority and police officers were mandated to measure productivity and demonstrate success on the job by stopping, frisking, citing, and arresting individuals located in spatially defined problem areas. Ultimately, minority communities bore the brunt of this focused attention and treatment by police. Simmering frustrations and frayed relations between police and the public they serve are exacerbated when crime-analysis products fail to elucidate root attractors of illegal behavior, especially when responses to spatial intelligence fail to acutely address the qualities of places and fail to look beyond merely the people located there.

(Continues…)


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Table of Contents

Preface ix

Acknowledgments xi

Part 1 The Basic Principles Of Risk-Based Policing

1 Introduction to Risk and Big Data 5

Introduction to Risk-Based Policing in Crime Prevention 5

The Importance of Risk 6

Big Data 8

Risk-Based Policing 8

Conclusion 9

2 The Evolution of Modern Policing 11

Introduction 11

Police Reform and Professionalization 13

From Professionalism to Problem-Solving 16

The Importance of Places and Data Analysis in Contemporary Policing 18

Conclusion 21

3 Policing in the New Era of Public Safety and Law Enforcement 23

Focus on Places with Risk Terrain Modeling 23

The Central Tenets of Risk-Based Policing 27

Develop Spatial Risk Narratives 27

Solicit and Value Input from Multiple Stakeholders 28

Make Data-Driven Decisions 30

Balance Strategies for Crime Risk Reduction 31

Conclusion 32

4 Risk-Based Policing and ACTION 35

Introduction 35

Risk Governance and the Police Leader 36

ACTION Meetings 36

A Detailed Breakdown of the ACTION Agenda 39

The Uncertainty in Risk Governance 43

Conclusion 46

Part 2 Methods And Case Studies Of Risk-Based Policing

5 The Theory of Risky Places 53

Introduction 53

Theories Relevant to Risk-Based Policing 54

Conclusion 62

6 High-Risk Target Areas and Priority Places 63

Introduction 63

Studying Exposure and Vulnerability to Crime 64

Brooklyn as a Case Study 65

Conclusion 70

7 The Role of Police in Risk-Based Policing: Case Studies of Colorado Springs, Glendale, Newark, and Kansas City 71

Introduction 71

Risk Assessment Methodology 72

Findings 76

Connecting Risk Assessments to intervention 98

Conclusion 100

8 Facilitators and Impediments to Designing, Implementing, and Evaluating Risk-Based Policing Strategies: Insights from Completed Researcher-Practitioner Partnerships 102

Introduction 102

Researcher-Practitioner Partnerships 103

Planned Change and Program implementation 104

Risk-Based Policing Partnerships 106

Findings 107

Conclusion 115

9 The Roles of Multiple Stakeholders in Risk-Based Policing: Case Studies of Jersey City and Atlantic City 118

Introduction 118

ACTION Meetings in Jersey City 119

Risk-Based Policing in Atlantic City 120

Conclusion 124

10 People Make Risk-Based Policing and Data Actionable 126

Valuing Data: Lessons Learned 126

Beyond Training and into Active Problem Solving 128

Conclusion 131

Epilogue 133

References 137

Index 149

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