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Gang Graffiti, Group Process, and Gang Violence

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Abstract

Objectives

Examines the neighborhood-level relationship between gang graffiti and gang violence in a large city in the western region of the US during a peak period of local gang feuds in 2014–15.

Methods

Bayesian Poisson log-linear mixed regression models with a spatio-temporal autoregressive process are estimated using a combination of data for N = 42,276 space–time units.

Results

Consistent with the view of graffiti an important means of street-level communication between gangs and an integral part of group processes associated with violence escalation and contagion, results reveal a roughly 40 to 60% increase in the expected rate of gang homicides, gang assaults, and gang firearm offenses (but not gang robberies) for each unit increase in neighborhood density of gang graffiti. Somewhat unexpectedly, the relationship with both gang homicide and gang assault was stronger for non-threatening gang graffiti than gang graffiti involving explicit threats or disrespect.

Conclusions

Findings suggest gang graffiti provides clear clues about local “staging grounds,” where gang status is on the line and violence is expected and easily provoked. Thus, while gangs increasingly are dissing rivals and airing beefs through music (e.g., “diss tracks”) and in cyberspace, many still occupy and defend turf and write graffiti that communicates threats to other gangs and feeds into group processes associated with violence escalation and contagion.

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Notes

  1. According to Castleman (1982), the origins of the word graffiti can be traced to the Italian word graffiare, which means “to scratch”.

  2. We do not identify the city by name due to confidentiality agreements between Graffiti Tracker and contracted clients.

  3. Densities are calculated in relation to an underlying symmetric grid system that provides a uniform distribution typically required for kernel smoothing.

  4. In contrast to census block groups, which are subdivisions of census tracts consisting of between 600 and 3000 people (mean population = 1500), 1000 × 1000 foot grid cells in the city contain between 51 and 866 people (mean population = 182).

  5. In most cases, we used a Bessel K model and adjusted parameters to visually match the model with the data.

  6. Comparisons could not be made with census blocks due to the lack of available city-wide data for most metrics at this level of aggregation.

  7. A single case is omitted due to indeterminable gang graffiti type.

  8. However, whereas our approach, which falls into what Hipp and Boessen (2013, p. 294, emphasis in original) refer to as the “individual social environment strategy,” assumes “the environment is acting on a single block or person at the center of the environment,” their “egohoods approach posits that the entire environment is capturing the social process (and therefore the outcome measure is constructed at the geographic unit of the entire egohood).” While they note that the two overlapping boundary approaches (parameterized with half-mile radius buffers) frequently yield similar results and that either one “improves on the nonoverlapping boundary approach” (p. 311), important differences emerge with respect to the estimated effects of inequality, population density, and household crowding on crime.

  9. No comparable data were available from any other publicly available source.

  10. It is important to note that place features do not necessarily have a uniform effect on different types of crime, including gang violence (Valasik 2018), and can exhibit different effects on the same crime occurring in different environments (Barnum et al. 2017).

  11. We suspect liquor establishments loaded highly with yoga studios, coffee shops, and fast-food restaurants because of the strong craft beer culture in the city and laws prohibiting purchase of full-strength alcohol and beers in grocery stores.

  12. Central to this approach are data partitioned into a fixed number of high-level units. For a recent application of the method in criminology, see Ridgeway and colleagues’ (2019) analysis of the relationship between civil gang injunctions and crime in Los Angeles, 1988–2014.

  13. Using the Incremental Spatial Autocorrelation tool in ArcGIS, we first identified peaks in spatial autocorrelation for each outcome measure, which corresponded loosely to a radius of a quarter mile, half mile, or one full mile. Models estimated at a half-mile radius distance exhibited the greatest stability in coefficients, as evidenced by Geweke scores and small DIC and relatively large LMPL fit values. These models were selected for computing inverse distance weights using a distance-decay function in which the weighted influence of events and predictors in surrounding neighborhood grid cells decreases as the distance from each target grid cell increases.

  14. In Los Angeles, Valasik et al. (2016) found a significant drop in collection of gang and gang member intelligence by officers after a number of gang units were temporarily dismantled in January 2011. We are unaware of any comparable change in city police and policing during the time period under investigation in this study.

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Authors and Affiliations

  1. School of Public Affairs, University of Colorado Denver, Denver, USA

    Lorine A. Hughes & Lonnie M. Schaible

  2. Graffiti Tracker, Long Beach, USA

    Timothy Kephart

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  1. Lorine A. Hughes
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  2. Lonnie M. Schaible
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  3. Timothy Kephart
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Correspondence to Lorine A. Hughes.

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Hughes, L.A., Schaible, L.M. & Kephart, T. Gang Graffiti, Group Process, and Gang Violence. J Quant Criminol 38, 365–384 (2022). https://doi.org/10.1007/s10940-021-09507-8

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