Police Activities and Community Views of Police in Crime Hot Spots

Abstract

Evidence on how hot spot policing affects community members’ views of police is very limited and inconclusive. Scholars have thus called for further study of community attitudes in hot spots to guide police in the formulation of hot spot strategies—an issue that is especially salient given recent public controversy surrounding policing, particularly in the United States. Using survey responses collected in 2018 from more than 1,000 community members living or working in more than 100 hot spots across 2 mid-sized cities in the United States, this study examines how community members’ perceptions of police activities in hot spots relate to their wider attitudes about police. Bivariate and multivariate analyses indicate that community members in hot spots in both cities exhibit more positive attitudes towards police along several dimensions (e.g., trust and confidence in police, views of police legitimacy, and perceptions of police responsiveness and procedural justice) when they see more frequent patrol and when they see positive police-community interactions. They have more negative views of police when they witness higher levels of investigative and enforcement activity. The findings support hot spot policing strategies that emphasize regular, systematic patrol in hot spots, complemented by positive community engagement efforts and problem-solving work. In contrast, they imply that enhanced enforcement activity in hot spots should be used judiciously.

Keywords:

• policing
• hot spots
• community attitudes
• survey

Acknowledgments

The authors thank the participating police agencies (identified anonymously) for their cooperation in conducting this study. The authors also thank Jackie Sheridan and William Johnson for research assistance.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 At the same time, focusing on hot spots can potentially reduce over-policing of lower risk areas (Weisburd, 2016).

2 Patterns in community views across these studies were also not consistently related to whether the interventions reduced crime and disorder.

3 Along similar lines, experiments in Tucson, Houston, and Cambridge (MA) showed that augmenting hot spot policing with procedural justice training for officers reduced arrests and improved some aspects of how community members viewed police (Weisburd et al., 2022). This suggests that an emphasis on procedural justice can enhance the benefits of hot spot policing or mitigate its negative consequences. These experiments compared different modes of hot spot policing (with and without procedural justice training), thus precluding an assessment of whether hot spot policing improved or worsened community attitudes in general (relative to conditions without hot spot policing).

4 All population statistics are based on figures for 2019 reported in the U.S. Census Bureau’s QuickFacts.

5 These calculations are based on figures reported in the Federal Bureau of Investigations’ Uniform Crime Reports for 2017 (https://ucr.fbi.gov/crime-in-the-u.s/2017/crime-in-the-u.s.-2017).

6 The study was approved by the Institutional Review Board of the National Opinion Research Center at the University of Chicago (IRB 00000967), which has a multiple project assurance with the U.S. Department of Health and Human Services for research involving human subjects.

7 The locations were larger than the types of micro places used in some hot spot studies (e.g., specific blocks or intersection areas), but they were within the range of area types and sizes commonly used in hot spot research and practice (see Braga et al., 2019; Eck, 2005; Koper, 2014), and they provided more precise areas of focus relative to neighborhoods and other police areal units (which is a key consideration in hot spot policing—see Braga et al., 2019: 6). We designed the hot spots to optimize operational areas and maximize base rates of crime for a long-term police intervention and study that were being planned for the locations.

8 The hot spots contained more than 21,000 addresses in Avalon and more than 36,000 in Willowdale.

9 For example, the hot spot community survey studies discussed above reported response or cooperation rates ranging from 9% to 61%, with most being less than 50% (Haberman et al., 2016; Ratcliffe et al., 2015; Kochel et al., 2015; Weisburd et al., 2011; 2021). Our response rate also compares favorably to the 50% average (approximately) which has been found in survey studies across a variety of fields including psychology, communications, and organizational research (Baruch & Holtom, 2008; Macias et al., 2008; Van Horn et al., 2009). However, there is no agreed upon standard for minimally acceptable response rates for surveys (Fowler, 2014) due in part to the fact that response rates are poor predictors of non-response bias (Groves & Peytcheva, 2008).

10 These items were taken from Rosenbaum et al. (2007) and Weisburd et al. (2010).

11 The police agencies in these cities employed foot patrols on an ad hoc and discretionary basis but did not have formal foot patrol programs.

12 See Table 1 for further description of these measures.

13 See Table 1 for further description of these additional police measures.

14 Depending on the measure, 11% to 24% of respondents reported recent police contact stemming from a police-initiated or citizen-initiated encounter of the sort described above.

15 Missing data for the items and scales used in the analysis was very low (almost always under 3%, as shown in Table 1). Given the rarity of missing values and the large samples available in each city, we treated missing data as random and did not impute values for missing data.

16 We considered estimating multi-level models that also incorporated characteristics of the hot spots. However, intraclass correlation coefficients (ICCs) estimated for the models were no greater than 10% (and nearly all were below 10%), indicating that hot spot characteristics contributed little or only very modestly to variation in the dependent measures. In practice, multilevel models offer few benefits when ICCs are smaller than 5% (Dyer et al., 2005). While ICCs at around 10% may be suggestive of a medium group-level variance (Hox et al., 2017), ICCs were below that level in most of our models (particularly in Avalon). Given the small ICCs and our primary interest in estimating individual-level rather than group-level effects, we controlled for hot spot-level variation using clustered standard errors.

17 The standardized regression coefficients should be viewed cautiously, as the use of clustered data may affect estimates of the variables’ standard deviations. However, the key results from the multi-city models were not sensitive to the use of clustered errors, suggesting that the clustering effects were not substantial,

18 White and Black respondents accounted for 91%-93% of the respondents in each city; hence, we excluded other race and ethnic groups from these analyses.

19 The focus on respondents from urban hot spots from two similar cities also minimizes other contextual differences (e.g., area crime and disorder levels) that can cause variation in the effects of policing on attitudes (e.g., Reisig et al., 2021; Zahnow et al., 2021).

20 To illustrate, the police stop and citizen-initiated contact measures had no significant relationships to attitudes in Avalon and varying positive and negative associations with a few attitudes in Willowdale. The police-initiated positive contact measure had positive relationships to some attitudes in Avalon but not in Willowdale. Differences in the nature and quality of these contacts may help to explain the inconsistent results.

21 Other model covariates had relationships with police-related attitudes that were generally inconsistent across outcomes and cities. To varying degrees, attitudes towards police were associated with race, age, gender, fear of crime, residential status, time living or working in the location, crime victimization, and perceived crime and disorder. The most consistent of these findings was that non-whites had more negative views of police. Respondents also tended to have worse views of police, particularly in the multi-city models, when they perceived higher levels of crime and disorder and, to a lesser extent, when they had greater fear of crime.

22 Our analysis assumes that people can reasonably differentiate between different types of police-community contacts based on visual and verbal cues. To some degree, however, people may interpret ambiguous police-community interactions as positive or adversarial based on whether they have positive or negative views of police, respectively. Although our model covariates may control for some of these tendencies, this form of reverse causality could account for some of the associations found in this study between different types of police-civilian contacts and views of police (this issue would not seem to apply to analyses of the patrol visibility measure). Interpreted in this manner, the policy implications are arguably the same (i.e., positive community engagement may improve perceptions of police while overuse of adversarial, enforcement actions may worsen them). However, the reverse causality interpretation implies that changing views about police in a positive manner will be more challenging, as police will have to overcome the proclivity of some community members to interpret their actions negatively.

Additional information

Funding

This work was supported by grant 2017-R2-CX-0017 from the National Institute of Justice (U.S. Department of Justice) to NORC and George Mason University. The views expressed are those of the authors. The data for this study has been deposited with the National Archive of Criminal Justice Data (NACJD).

Notes on contributors

Christopher S. Koper

Christopher S. Koper, Ph.D., is a Professor in the Department of Criminology, Law and Society at George Mason University and the Principal Fellow of George Mason's Center for Evidence-Based Crime Policy. He specializes in issues related to policing, firearms policy, and program evaluation.

Bruce G. Taylor

Bruce G. Taylor, Ph.D. is a Senior Fellow at NORC at the University of Chicago and a criminologist studying the intersecting areas of violence, health and justice. His research in law enforcement includes work on officer safety, health, officer use-of-force and violence against officers and proactive policing strategies.

Weiwei Liu

Weiwei Liu, Ph.D. is a Senior Research Scientist at NORC at the University of Chicago. She is a Criminologist specializing in risk and protective factors of violence, firearm violence prevention, evidence-based policing, police officer use-of-force, and officer safety and wellness.

Xiaoyun Wu

Xiaoyun Wu, Ph.D., is a Research Data Scientist at the National Policing Institute. Her areas of interest include police technologies, public safety, and data science and development.