The Geography of Drug Activity and Violence: Analyzing Spatial Relationships of Non-Homogenous Crime Event Types

Abstract

The pervasiveness of interest regarding the theme of a relationship between street-level drug activity and violence has been reflected throughout criminal justice research, policy, and practice as well as in public opinion. Most research has focused on the connection between the two at the individual level. This study extends previous research by examining the place-based relationship between drugs and violence. To do so, this project employs three spatial statistical approaches—measures of spatial intensity/density, measures of spatial dependence for drugs and violence separately, and a modified spatial dependence approach for non-homogenous populations to explore the relationship between drug activity and violence. The findings indicate that while drugs and violence often exhibit overlapping spatial patterns, important variations exist in the spatial relationship between the two.

Keywords:

• drugs
• violence
• spatial analysis
• geography
• place-based
• drug markets

Notes

Notes

1. The geographic information system used was ARCGIS 8.2, which is a product of the Environmental Systems Research Institute (www.esri.com).

2. The database manipulation software used was Visual FOXPRO, which is a product of the Microsoft Corporation (www.microsoft.com).

3. The spatial analytic software used were S-PLUS and S+ SpatialStats Extension, which are products of the Insightful Corporation (www.insightful.com).

4. Formally, the kernel density estimation function is represented by the following equation (see Bailey & Gatrell, 1995, p. 85):Here, the mean estimated intensity of a particular location is denoted by λ_τ (s). k() is a probability density function, the radius of the kernel being the bandwidth, or τ and the center of the kernel, s. One common functional form of the kernel is a “quartic” function (used in this analysis), a hill-like, three-dimensional cap. Using a quartic function for k(), the measure of intensity λ_τ (s) then becomes (see Bailey and Gatrell, 1995, p. 85):where h_i is the distance between the point s and the observed event location s_i.

5. Please contact the author for all illustrations alluded to in this study.

6. The K function is mathematically represented by the following equation (see Bailey & Gatrell, 1995, p. 92):where λ²R is the intensity times the area of the region of interest and d_ij is the distance between events i and j. I_h(d_ij) equals 1 if d_ij ≤ h and 0 otherwise; w_ij is an edge correction factor.

7. To do this more easily, one plots the “LHAT” function, which is derivative of the K function. The LHAT function is denoted by (see Bailey and Gatrell, 1995, p. 104):

8. Please contact the author for all illustrations alluded to in this study.

9. Map units are standardized measures used in S-Plus. For example, a block approximately measures between 200 and 400 map units in length.

10. The differences in the distance of which clustering was detected for drugs and violence, when tested against a null hypothesis of no difference was statistically significant (t = 8.944, df = 123, p <. 001).

11. This was suggested by a previous anonymous reviewer of this article.