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Mapping Attitudes Towards the Police at Micro Places

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Abstract

Objectives

We examine satisfaction with the police at micro places using data from citizen surveys conducted in 2001, 2009 and 2014 in one city. We illustrate the utility of this approach by comparing micro- and meso-level aggregations of policing attitudes, as well as by predicting views about the police from crime data at micro places.

Methods

In each survey, respondents provided the nearest intersection to their address. Using that geocoded survey data, we use inverse distance weighting to map a smooth surface of satisfaction with police over the entire city and compare the micro-level pattern of policing attitudes to survey data aggregated to the census tract. We also use spatial and multi-level regression models to estimate the effect of local violent crimes on attitudes towards police, controlling for other individual and neighborhood level characteristics.

Results

We demonstrate that there are no systematic biases for respondents refusing to answer the nearest intersection question. We show that hot spots of dissatisfaction with police do not conform to census tract boundaries, but rather align closely with hot spots of crime. Models predicting satisfaction with police show that local counts of violent crime are a strong predictor of attitudes towards police, even above individual level predictors of race and age.

Conclusions

Asking survey respondents to provide the nearest intersection to where they live is a simple approach to mapping attitudes towards police at micro places. This approach provides advantages beyond those of using traditional neighborhood boundaries. Specifically, it provides more precise locations police may target interventions, as well as illuminates an important predictor (i.e., nearby violent crimes) of policing attitudes.

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Notes

  1. Although the literature review discusses studies that assess multiple forms of attitudes toward police (e.g., satisfaction, legitimacy, confidence), our analysis focuses solely on global satisfaction with police. Although research suggests that different types of global evaluations of police tend to be highly correlated (see, e.g., Maguire and Johnson 2010), an implication of this choice is that the results may not generalize to other types of policing attitudes. Additionally, the implications of concentrated dissatisfaction vs. concentrated illegitimacy may differ. We return to this possibility in the discussion.

  2. The bi-square kernel can be written as \(\left[ {1 - \left( {\frac{{d_{ij} }}{b}} \right)^{2} } \right]^{2}\) when \(d_{ij} < b\) and zero otherwise. The term \(d_{ij}\) represents the distance between two observations, and \(b\) is an arbitrary value chosen by the researcher (here it is set to 2000 m).

  3. Additionally, we fit a spatial error model, but examining the AIC or BIC model selection criteria the spatial lag model produced a better fit to the data. Each model resulted in similar inferences and effects sizes for the independent variables as well.

  4. Standardized effect sizes are calculated as \(\beta_{i} \cdot s\left( {x_{i} } \right)/s\left( y \right)\), where \(\beta_{i}\) is the reported regression coefficient for variable \(x_{i}\), and \(s\left( {x_{i} } \right)\) and \(s\left( y \right)\) are the standard deviations for the independent and dependent variable respectively.

  5. Moran’s I is calculated by averaging the residuals for the expanded weighted data and then calculating Moran’s I. So if an observation was nested in two census tracts, and it had residuals of 0.5 and 0.7, it would be aggregated to a single value of 0.6 before calculating Moran’s I.

  6. Variance inflation factors for the person level coefficients continue to be small in these multi-level models (under 3). The neighborhood level demographic covariates are slightly higher with each other but are still generally small (under 6).

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Acknowledgements

This research was partially supported by Award No. 2013-IJ-MU-0012, awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice, to the John F. Finn Institute. The opinions, findings, and conclusions or recommendations in this article are those of the authors and do not necessarily reflect those of the Department of Justice.

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Authors and Affiliations

  1. School of Economic, Political and Policy Sciences, Program in Criminology and Criminal Justice, The University of Texas at Dallas, 800 West Campbell Road, Mail Station GR 31, Richardson, TX, 75080-3021, USA

    Andrew P. Wheeler

  2. School of Criminal Justice, Rutgers University—Newark, 123 Washington St, Newark, NJ, 07102, USA

    Jasmine R. Silver

  3. John F. Finn Institute for Public Safety, 421 New Karner Rd., Suite 12, Albany, NY, 12205, USA

    Robert E. Worden & Sarah J. Mclean

  4. School of Criminal Justice, University at Albany, SUNY, Albany, USA

    Robert E. Worden

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  1. Andrew P. Wheeler
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Appendices

Appendix A: Multiple Imputation Analysis

While there were a total of 1804 surveys that had valid, geocodable addresses, the complete case analysis in text relied on only 1331 observations. As is typical for survey data, although any one variable had relatively few missing values, when discarding cases with any missing data resulted in an appreciable number of cases being dropped, here around 26% of the cases.

We use multiple imputation analysis to attempt to correct for this missing data problem. Specifically, we use multiple imputation through chained equations (Allison 2002). Given that there were already 18 variables used in the regression analysis, we do not consider any additional auxiliary variables in the multiple imputation process. We also do not use the spatial coordinates for the surveys in the imputation process, as this may introduce spatial autocorrelation into the results. We do use the number of violent crimes and years of the survey as variables to predict the missing data items, even though these values have no missing data.

The way multiple imputation works is that for each variable, all other variables are used to predict the missing values. This procedure then iterates through all variables, until the equations predicting the missing data converge. Once these equations have converged, one then draws a random value for the missing data from each individual equation, and this would make one imputed dataset. This procedure is replicated m times, here 5, and the original regression analysis predicting attitudes towards the police are estimated for each imputed dataset. This allows one to see individual variability for each of the imputed datasets, but also to combine the regression coefficients into one pooled result (Allison 2002: p. 31).

The variables age, adults in home, children in home, and attitudes towards the police were predicted as linear regression equations. This is consistent with the fact we predict attitudes using linear regression for the complete case analysis. We limit the predicted outcomes for attitudes towards the police, adults in home, and children in home to the ranges within the data, and round the results to the nearest integer. All other variables were predicted using multinomial logistic regression, which assigns the multiple imputation to the most probable category. We use the SPSS MULTIPLE IMPUTATION procedure to generate the 5 imputed datasets, and then re-estimate Model 4 from the paper 5 separate times.

To facilitate interpretation, only coefficients and standard errors for selected variables are displayed in A1, although all individual model results are available upon request. These variables were chosen as the most interesting given the results discussed in the paper. This table shows the coefficient estimates for each of the individual imputations, as well as the pooled results and the complete case estimates. One can verify that the estimates only change slightly across the imputations. For the pooled results, the effect of being African American or Other race still fails to reach statistical significance (with a higher standard error than coefficient estimate), but is positive in the imputed models. Violent crimes nearby has slightly smaller estimates for the imputed models (0.15 for the complete case and only 0.11 for the imputed models), but are still statistically significant and the largest effects according to the standardized effect sizes. For all imputed models one would fail to reject the null hypothesis of no spatial autocorrelation in the model residuals as well (Table 4).

Table 4 Coefficient Estimates across five imputations for selected regression coefficients
Full size table

Appendix B: Multilevel Ordinal Logistic Regression

In the main analysis we use linear regression to predict general attitudes towards the police. These are not measured as a continuous variable though, and are measured as a Likert item at integers from 1 (very satisfied) to 4 (very dissatisfied). While the main analysis relied on linear regression as suitable spatial models for ordinal regression have not been developed, we estimate a multi-level ordinal logistic regression as a robustness check to make sure our inferences are not altered by using linear regression. Table 5 reports the coefficients on a random effects model nesting survey responses within intersections. This model also includes census tract level variables, and is equivalent to Model 4 in the paper, with the exception that the random effect for census tracts was omitted, as the zero variance of the census tract random intercept prevented the model from converging. This model is weighted, the same as Model 4 in the main manuscript. This model was estimated using the clmm function in the R package ordinal (Christensen 2015). The model uses a logistic link function, and does not place any structure on the thresholds. Standardized coefficient sizes are estimated the same way as the linear regression models, except that the variance of the latent predicted value is used instead of the variance of the observed values (Long 1997: p. 129).

Table 5 Multilevel ordinal logistic regression model predicting satisfaction with police
Full size table

One can see that the main inferences from the paper: that the number of local violent crimes is still the largest effect on attitudes towards the police (according to the standardized coefficient estimates), the effect of African-Americans still fails to reach statistical significance, those older have more satisfaction with the police, and those in census tracts with a larger proportion of minorities have greater levels of dissatisfaction with the police, all result in the same inferences.

Appendix C: Inverse Distance Weighted Maps for Each Survey

The main analysis aggregates each year into one inverse distance weighted map. While we showed that attitudes towards the police are quite stable over the time period (see Fig. 2) one might similarly ask if the spatial distribution of attitudes are similarly stable. Figure 6 displays the inverse distance weighted maps for each survey year, 2001, 2009, and 2014. The same bins are used as the main analysis. One can see that the same general trend holds, although the same hot spots of dissatisfaction are more prominent in 2001 and 2009. 2014 illustrates how the resulting map can be greatly influenced by a few outliers. The hot-spot of dissatisfaction at the top of the map is driven by 3 out of 4 responses near that area that give a response of 4.

Fig. 6
figure 6

Inverse distance weighted (Inv. Dist. Weight) maps for each individual survey. The title of each panel corresponds to the year, and n corresponds to the number of surveys in each year. The color scheme is consistent across each year

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Wheeler, A.P., Silver, J.R., Worden, R.E. et al. Mapping Attitudes Towards the Police at Micro Places. J Quant Criminol 36, 877–906 (2020). https://doi.org/10.1007/s10940-019-09435-8

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