Abstract
Primary, secondary, and tertiary prevention programs that address opportunity or structural factors related to crime are usually delivered to entire cities, sections of cities or to specific neighborhoods, but our results indicate geographically targeting these programs to specific street segments may increase their efficacy. We link crime incidents to over 24,000 street segments (the two block faces on a street between two intersections) over a 16-year period, and identify distinct developmental patterns of crime at street segments using group-based trajectory analysis. One of these patterns, which we term chronic crime hot spots, includes just 1 % of street segments but is associated with 23 % of crime in the city during the study period. We then employ multinomial regression to identify the specific risk and protective factors that are associated with these crime hot spots. We find that both situational opportunities and social characteristics of places strongly distinguish chronic crime hot spots from areas with little crime. Our findings support recent efforts to decrease crime opportunities at crime hot spots through programs like hot spots policing, but they also suggest that social interventions directed at crime hot spots will be important if we are to do something about crime problems in the long run. We argue in concluding that micro level programs which focus crime prevention efforts on specific street segments have the potential to be less costly and more effective than those targeted at larger areas such as communities or neighborhoods.
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Notes
Intersections are excluded in our study in part because each intersection is attached to two or more street segments. In turn, incident reports at intersections differed dramatically from those at street segments. For example, traffic-related incidents accounted for only 3.77 % of reports at street segments, but for 45.3 % of reports at intersections. We also excluded the University of Washington campus from our analyses because crime data were not available for most of the study period.
Trajectory group 1 also represents an interesting hot spot pattern, with its steeply rising trajectory over the time period. Nonetheless, this group includes only 0.2 % of the street segments in the study, and accounts for only 1.5 % of crime overall. For a comparison of increasing and decreasing trajectories, see Weisburd et al. (2012).
Moreover, we have found that there is tremendous street-by-street variation not only in crime, but also in risk and protective factors related to crime (Weisburd et al. 2012) suggesting that data at higher level units like census blocks or census block groups would be inappropriate for our study.
Because census data are not available at the street segment level, we used data describing registered voters and public school students to develop an estimate of the number of residents on each street. To assess this relationship, we aggregated up our street segment estimates to census block groups for the year 2000. We then estimated a correlation between our data and the census estimates. We found a highly significant correlation of 0.70, indicating that there is a degree of error in our measure, but that overall it fits fairly well to the actual population of areas in Seattle.
We tried to use emergency crime call data, which lists the times when police are responding to calls as a way of tracking police presence, but we were able to gain data only for 4 years of our study period, and those data were extremely highly positively correlated with crime incident data. We concluded that the data overall reflected not police patrol at places, but police response to crimes at specific places, many of which were later identified as the locations of crime incidents.
Physical disorder indicators include the number of incidents of illegal dumping, litter, graffiti, overgrown weeds, inoperable cars on the street, junk storage, exterior abatement, substandard housing, and minor property damage.
The number of high-risk and truant juveniles on a street are strongly correlated (r = 0.91), because both measures include truancy as a factor. Number of high-risk juveniles, however, also takes into account school performance.
Using this approach rather than simply comparing the chronic and crime free patterns in an ordinary logistic regression we gain greater model stability and more accurate estimates of standard errors for the specific comparison.
The initial inspection of VIF shows some concerns of potential colinearity on both high-risk juveniles and truant teens, but in examining the corresponding condition indices and variance proportions of these two variables the diagnostics suggested their inclusion would not seriously impact the overall models (Tabachnick and Fidell 2001).
The standardized logistic regression coefficient is calculated by multiplying the parameter estimate times the standard deviation of the measure. While there is considerable controversy regarding the interpretation of these standardized coefficients (e.g. see Kaufman 1996), we think it provides a very general sense for comparing the strength of variable impacts across a model.
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Weisburd, D., Groff, E.R. & Yang, SM. Understanding and Controlling Hot Spots of Crime: The Importance of Formal and Informal Social Controls. Prev Sci 15, 31–43 (2014). https://doi.org/10.1007/s11121-012-0351-9
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DOI: https://doi.org/10.1007/s11121-012-0351-9