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Crime Places in Context: An Illustration of the Multilevel Nature of Hot Spot Development

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An Erratum to this article was published on 24 February 2016

Abstract

Objectives

The present study provides an illustration of a statistical test of the Brantinghams’ theory about the formation of hotspots and the effects that nodes, paths, and environmental backcloth have on their development.

Methods

We used multilevel Poisson regression analysis to explain variation in the count of incidents at each address. Place-level proximity to nodes and paths was measured by using the Euclidian distance from each location to the closest carry-out liquor store, on-premises drinking establishment, and bus route. The broader environmental backcloth was represented by various census block-group characteristics, including density of commercial land use. A three-way place-level interaction as well as a cross-level interaction involving all four key independent variables were used to estimate the Brantinghams’ concept of the overlay of nodes, paths, and backcloth.

Results

The three-way interaction involving the distance to the closest on-premises liquor establishment, the distance to closest carry-out liquor facility, and the distance to the closest bus route was significantly and negatively related to place-level crime incidents. This three-way interaction had effects which varied across neighborhood contexts, with stronger negative effects on crime occurring in neighborhoods characterized by high levels of commercial density.

Conclusion

This study supported the notion of a multilevel theory of crime places and has implications for more effectively addressing crime. In particular, those places with multiple nodes and paths in their proximal environments and dense commercial land within their broader environments likely need additional crime prevention measures to get the same benefit relative to places with multiple nodes and paths in the proximal environments yet little commercial density within their broader environment.

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Notes

  1. We recognize that this dichotomy is an oversimplification. For example, some prominent scholars in the crime-and-place tradition recognize streets and blocks as “meso-level,” lying between micro-level individuals and/or specific addresses and macro-level neighborhoods, cities, counties, and so on (see, e.g., Taylor 2015). For the purposes of this paper, we define neighborhoods as census-defined collections of city blocks, and these are treated as “macro-level” units. In contrast, units smaller than these collections of blocks, including single blocks, streets, parcels, addresses, or intersections are viewed as “micro-level” places (see also Weisburd et al. 2012).

  2. Curman et al. (2015) provided a partial replication of Weisburd and colleagues’ Seattle studies by examining over one million calls for service to the Vancouver Police Department (VPD) from 1991 to 2006 at the street segment level in Vancouver, British Columbia. Using group-based trajectory models (GBTM) and the k means statistic, the authors found considerable overlap between the street segment patterns in Vancouver and Seattle. For example, the authors noted that crime on Vancouver’s street blocks was highly concentrated, with 100 % of all criminal activity occurring at only 60 % of street blocks. Additionally, these crime patterns remained stable over time. Unlike Weisburd et al. (2012), however, Curman et al. (2015) did not estimate developmental trajectories of crime using opportunity-related covariates.

  3. Braga et al. (2011) note that while street segments and intersections located along major roads in Boston do likely provide plentiful targets, there are also likely more passers-by with better lighting at such locations.

  4. The circle in this figure does not reflect an actual census block and its boundaries.

  5. Combining co-incident locations and creating a count variable for each location was accomplished using the “collect event” tool in ArcGIS 10.1.

  6. We inspected the possibility of collinearity among all distance measures. The maximum value for VIF was 2.9 for all place-level distance variables.

  7. We checked the possible overlap between these facilities but found none. Moreover, liquor stores associated with major grocers (such as Kroger stores) and gas stations were deleted from the main list of liquor establishments.

  8. In all cases of variable skewness, various transformations were considered: natural log, log base ten, and square root. The transformation which yielded the closest approximation to a normal distribution was used.

  9. The interaction term was centered after each term was multiplied and that product was logged.

  10. This value of alpha is lower than a traditional cutoff of 0.7; however, according to Hair et al. (2007), values above 0.6 are considered acceptable, especially when analyzing reliability of scales made up of few items.

  11. In order to calculate the spatial lag variable, first, the spatial weight matrix is created for each block group’s neighbors using queen contiguity, which identifies neighboring block groups as the ones that share a border. Next, the average of crime counts of all locations within each block group is calculated. Finally, the block group average crime value is used within the spatial weight matrix to calculate the lag value for each block group. The value of the spatial lag ranges from 1.3 to 4.8.

  12. It should be noted that we also examined the possibility of collinearity among block-group-level variables using VIF values. The diagnostic did not indicate substantial collinearity among these variables (e.g., the maximum VIF was 2.5).

  13. The negative binomial regression command (nbreg) is used in Stata to see whether the dependent variable is over-dispersed or not. The value of alpha coefficient (0.56) and its significant value indicated that our dependent variable is over-dispersed.

  14. This method (using deviance statistics from linear models) might not reflect the exact improvement across count-based models. However, in the absence of a better test, it should provide us with a good sense of the fit of our count-based models relative to one another, especially since the results from the linear analysis were consistent with our count based analysis across all models.

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Author information

Authors and Affiliations

  1. Department of Criminology, College of Arts and Sciences, University of South Florida—Sarasota Manatee, 8350 N. Tamiami Trail, Sarasota, FL, 34243, USA

    Rustu Deryol

  2. Department of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA

    Pamela Wilcox & John Wooldredge

  3. Department of Criminology, California State University—San Bernardino, San Bernardino, CA, USA

    Matthew Logan

Authors
  1. Rustu Deryol
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  2. Pamela Wilcox
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  3. Matthew Logan
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  4. John Wooldredge
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Corresponding author

Correspondence to Rustu Deryol.

Appendices

Appendix 1: Bivariate Correlations Among Level-1 Variables

 

Crime count

Carry-out

On premise

Bus stop

Interaction

Zero order correlations among place level variables

 Crime count

1

    

 Carry-out

−0.064**

1

   

 On premise

−0.049**

0.432**

1

  

 Bus routes

−0.073**

0.257**

0.305**

1

 

 Interaction

−0.076**

0.761**

0.810**

0.656**

1

  1. ** Correlation is significant at the 0.01 level (2-tailed)

Appendix 2: Bivariate Correlations Among Level-2 Variables

 

Commercial

Disadvantage

Instability

Felon residence

Spatial lag

Commercial

1

    

Disadvantage

0.175**

1

   

Instability

0.298**

0.240**

1

  

Felon residence

0.203**

0.744**

0.313**

1

 

Spatial lag

0.340**

0.080

0.175**

0.106

1

  1. ** Correlation is significant at the 0.01 level (2-tailed)

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Deryol, R., Wilcox, P., Logan, M. et al. Crime Places in Context: An Illustration of the Multilevel Nature of Hot Spot Development. J Quant Criminol 32, 305–325 (2016). https://doi.org/10.1007/s10940-015-9278-1

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