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Crime Generators in Context: Examining ‘Place in Neighborhood’ Propositions

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Abstract

Objectives

The present study tests hypotheses regarding the moderating influence of neighborhood-level criminal opportunity on the relationship between crime generators and block-level crime.

Methods

We first estimated multilevel negative binomial regression models for violent, property, and drug crimes to identify crime-type specific crime generators on each block. We then estimated a series of crime-type specific models to examine whether the effects of violent, property, and drug crime generators are moderated by three census block group-level indicators of neighborhood criminal opportunity—concentrated disadvantage, vehicular traffic activity, and civic engagement.

Results

The positive relationship between crime generators and crime on blocks was exacerbated in census block groups with high levels of concentrated disadvantage and high levels of traffic activity for all three crime types. The effects of crime generators on block-level crime were significantly tempered in census block groups with high levels of civic engagement.

Conclusions

Particular place types do not generate crime similarly across varying neighborhood contexts. Rather, the criminogenic effects of micro-places appear to be exacerbated in neighborhoods with extensive criminal opportunity and tempered in neighborhoods with less criminal opportunity.

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Notes

  1. For the purposes of this paper, “neighborhood” and “community” are used interchangeably.

  2. Similar to several recent studies (e.g., Bernasco and Block 2011; Boessen and Hipp 2018; Contreras 2017; Contreras and Hipp 2019), blocks are the level 1 units of analysis. The decision to use blocks rather than street segments was guided by the methodology used to collect the crime data. San Antonio Police Department (SAPD) has moved away from practices that rely on street centerlines and now has protocols in place to geo-validate addresses and locate crime incidents on parcels using rooftop geocoding. Therefore, few incidents are geocoded to the street centerline. A common method for assigning incidents to street segments is to use the nearest street; this can lead to arbitrary decisions upon assignment when incidents are near intersections.

  3. This data is modeled from a collection of consumer surveys produced by GfK MRI, a company focused on media and consumer research in the United States (Esri 2017). GfK MRI uses address-based sampling, a probability-based sampling methodology aimed at improving population coverage. More information on the methodology can be found at https://www.gfk.com/fileadmin/user_upload/dyna_content/US/documents/KnowledgePanel_-_A_Methodological_Overview.pdf and https://mri.gfk.com/fileadmin/user_upload/dyna_content/US/documents/MRI_Documents/tech_guide_methodology.pdf. Codebooks can be found at http://codebook.mriplusonline.com/codebooks.aspx. Additional information on the consumer survey data used by ESRI’s Civic Activities and Political Affiliation Market Potential dataset can be found at https://mri.gfk.com/gfk-mri-technical-guides/fall-2017-technical-guide/.

  4. Similar to other studies measuring crime on blocks (e.g., Boessen and Hipp 2015), we do not include sexual assaults due to low reporting rates (see Baumer and Lauritsen 2010).

  5. The FDR Correction is a powerful alternative to the traditional Bonferroni approach and controls for the expected proportion of falsely rejected hypotheses due to multiple testing (Benjamini and Hochberg 1995; Benjamini and Hochberg 2000).

  6. We chose to identify nearby crime generators using queen contiguity over measuring places as “exposures” (i.e., distance decay effects) because our level 1 units of analysis are polygons. When working with polygons, using a boundary method to identify neighbors is preferable. With lines (e.g., street segments) and points, neighbors are not identified naturally and therefore some type of threshold has to be determined to establish neighbors. Using distance decay with polygons requires one to define how far away neighbors are and what weight should be assigned when established neighbors already exist. This method is much more problematic for polygons than street segments because polygons vary in size, so the correct distance and weighting method will likely vary across units.

  7. The maximum bivariate Pearson’s correlation coefficient was 0.74 (between percent Hispanic and percent of residents without a high school diploma, GED, or a higher educational degree), followed by 0.563 (between percent Hispanic and percent of residents receiving food stamps).

  8. Items included attended a public meeting on town or school affairs; served on a committee for a local organization; engaged in fundraising; volunteered for a charitable organization; made a speech; wrote something that was published; wrote a letter to a newspaper or magazine editor or called a news radio or TV show; wrote or called a politician; voted in federal, state, or local election; attended a political rally, speech, or organized protest; signed a petition; participated in an environmental group or cause; made contributions to NPR; made contributions to PBS; contributed to arts or cultural organization; contributed to educational organization; contributed to environmental organization; contributed to health organization; contributed to political organization; contributed to religious organization; and contributed to social services organization.

  9. Collinearity diagnostics for the multilevel negative binomial regression models presented in Table 2 indicated a mean variance inflation factor (VIF) of 1.77, with a maximum VIF of 3.67 for banks and credit unions on adjacent blocks. Though some researches argue that VIFs greater than 2.5 may cause concern, Allison (2012) explains that high VIFs can be safely ignored when the variables with high VIFs are control variables, as the coefficients of the variables will not be affected and the performance of the control variable is not jeopardized. Note that the purpose of the analyses reported in Table 2 is to identify which place types on focal blocks operate as crime generators. Among these variables of interest, the maximum VIF is 2.43. The maximum VIF for the models using counts of crime generators was 2.01.

  10. As a sensitivity analysis, we computed alternative measures of violent, property, and drug crime generators that account for the differential effect sizes of the place types. Specifically, we recalculated the number of crime generators per block, weighted by the place type effect sizes. We then re-estimated the 18 cross-level interactions. Of the eighteen significant cross-level interactions reported in Tables 5, 6, and 7, sixteen remained significant in the hypothesized direction. The positive interaction between concentrated disadvantage and drug crime generators on focal blocks was no longer significant, nor was the positive interaction between traffic activity and drug crime generators on adjacent blocks.

  11. On average, the inclusion of each cross-level interaction was associated with a 5.60% decrease in the slope variances reported in Table 4. The mean percent decrease was slightly lower for the focal block crime generator slope variances (M = 5.21%) compared to the percent decrease observed for adjacent block crime generator slope variances (M = 5.99%). The largest decrease in slope variance observed for focal block crime generator slopes was associated with the inclusion of an interaction between concentrated disadvantage and property crime generators (8.74%). For adjacent blocks, the largest decrease was associated with the inclusion of an interaction between civic engagement and violent crime generators (12.64%).

  12. This limitation is not unique to our PIN study, but rather one that is also present in individual level tests of opportunity theories that rely on measures that seem to represent multiple concepts within the theory (see Madero-Hernandez and Fisher 2013).

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Authors and Affiliations

  1. Department of Criminal Justice, University of Texas at San Antonio, 501 W. Cesar E. Chavez Blvd., San Antonio, TX, 78207, USA

    Marie Skubak Tillyer

  2. Department of Sociology and Criminology, Pennsylvania State University, University Park, PA, USA

    Pamela Wilcox

  3. College of Built Environments, University of Washington, Seattle, WA, USA

    Rebecca J. Walter

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  1. Marie Skubak Tillyer
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Table 8 Bivariate correlations
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Tillyer, M.S., Wilcox, P. & Walter, R.J. Crime Generators in Context: Examining ‘Place in Neighborhood’ Propositions. J Quant Criminol 37, 517–546 (2021). https://doi.org/10.1007/s10940-019-09446-5

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