Risky facilities can show up as hot spots on a city’s crime map. Indeed, specific hospitals, schools, and train stations are often well-known examples. But simply treating these facilities as hot spots misses an important analytical opportunity: comparing the risky facilities with other like facilities. Such a comparison can reveal important differences between facilities that can account for the differences in risk, thereby providing important pointers to preventive action.
In addition, risky facilities are sometimes treated as examples of repeat victimization. However, this can create confusion when it is not the facilities that are being victimized, but rather the people who are using them. Thus, a tavern that repeatedly requests police assistance in dealing with fights is not itself being repeatedly victimized, unless it routinely suffers damage in the course of these fights or if members of staff are regularly assaulted. Even those participating in the fights may not be repeat victims, as different patrons might be involved each time. Indeed, no one need be victimized at all, as would be the case if the calls were about drugs, prostitution, or stolen property sales. Calling the tavern a repeat victim can be more than just confusing, however, because it might also divert attention from the role mismanagement or poor design plays in causing the fights. By keeping the concepts of repeat victimization and risky facilities separate, it may be possible to determine whether or not repeat victimization is the cause of a risky facility and thereby to design responses accordingly.
The concept of risky facilities can be helpful in two types of policing projects. First, the concept can be useful in crime prevention projects that focus on a particular class of facilities, such as low rent apartment complexes or downtown parking lots. In the scanning stage, the objective is to list the facilities involved along with the corresponding number of problem incidents in order to see which facilities experience the most and which the fewest problems. This might immediately suggest some contributing factors. For example, a study of car break-ins and thefts in downtown parking facilities in Charlotte, North Carolina revealed that the number of offenses in each parking lot was not merely a function of size.14 Rather, it was discovered that some smaller facilities experienced a large numbers of thefts because of some fairly obvious security deficiencies. This finding was explored in more depth in the analysis stage by computing theft rates for each facility based on its number of parking spaces. The analysis found that the risk of theft was far greater in surface lots than in parking garages, a fact that had not been known previously. Subsequent analysis compared security features between the multilevel and surface lots and then within the members of each category in an effort to determine which aspects of security (e.g., attendants, lighting, security guards) explained the variation. This analysis guided the selection of measures that were to have been introduced at the response stage; and had these been implemented as planned (which was not the case), the assessment stage would have examined, not merely whether theft rates declined overall, but whether those at the previously riskiest facilities had declined most. Obviously, this type of analysis can be conducted within any group of facilities.
Second, risky facilities analysis can be helpful to crime prevention efforts that focus on a particular troublesome facility. In this sort of analysis, the scanning stage consists of comparing the problems at a particular facility with those at similar nearby facilities. For example, in a project that won the Herman Goldstein Award for Excellence in Problem-oriented Policing in 2003, 15 police in Oakland, California discovered that a particular motel experienced nearly 10 times as many criminal incidents as did any other comparable motel in the area. Although in this case the analysis convinced Oakland police to address the problems at the motel in question, in other cases analysis might reveal that some other facilities have far greater problems than the one which was the initial focus of the project. Comparing the facility being addressed in the project with other group members can also be useful in the analysis, response, and assessment stages described above.
Police reports and calls for service data are the most common sources of information about crime and disorder events. However, using these data can lead to errors if care is not taken to check for some of the following potential problems.†
† Many of these data problems are also encountered when studying hot spots and repeat victimization. For further information see Deborah Weisel (2005), Analyzing Repeat Victimization, Problem Solving Tools Series No. 4.
Incident reporting forms and police records can be revised to improve geographical information gathering; moreover, the increased use of geocoding for crime reports will gradually help resolve some of these difficulties.
A study in England in 1964 found that absconding rates for residents in 17 training schools for delinquent boys ranged from 10 percent to 75 percent. To determine whether this variation was random, researchers reexamined the absconding rates two years later (1966) to see if the variation was much the same. They found that by and large the variation was consistent between the two years. For example, School 1 had the lowest absconding rate and School 17 the highest rate in both years (see the table below). In fact, the correlation was 0.65 between the two years.† Because the variation was relatively stable and because very few boys would have been residents in both years, researchers determined that the variation was probably due to differences in management practices rather than to differences in the student populations.
† Correlation coefficients can be calculated quite simply from an Excel spreadsheet.
|Training School||Absconding Rate|
Adapted from: Clarke and Martin (1975).
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