Planning, Training, and Implementation
Data Collection Elements
Local jurisdictions will differ on the type of data that they want to collect and the methodologies they will employ to collect information. However, the following offers a guide to the different information that can be collected.
Data that might be collected by local jurisdictions can be categorized into two parts. The first set of elements in any traffic-stop study is data that are routinely collected during normal traffic-stop operations. The second set of elements are study-specific elements to be collected specifically to assess questions of racial profiling in stop-and-search practices. Agencies should decide how they will analyze the data before finalizing the elements they collect, as there are certain elements required for certain analyses.
It is recommended that jurisdictions assign a stop identification code to each dispatch or MDT communication for traffic-stops. The stop identification code should correspond to a unique identification number on new stop-and-search data collection forms or computer entries. This process allows jurisdictions to link information that they routinely collect on traffic-stops with additional information specifically recommended for stop-and-search data collection.
Routine Data Collection Elements
- Date, Time, and Location of the Stop
- License Number, State, and Description of Vehicle Stopped
- Length of Stop
- Name and ID Number of Officers
Study-Specific Data Collection Elements
- Date of Birth
- Race, Ethnicity, or National Origin
- Reason for the Stop
- Disposition or Outcome of the Stop
- Whether a Search was Conducted
Routine Data Collection Elements
Most jurisdictions already collect the following data during a routine dispatch or traffic stop and, under this data collection accountability model, would continue to do so.
Date, Time and Location of the Stop.
This information might include the police precinct, street address or intersection, mile marker or exit. Collecting these items is essential for analysis of traffic stop data because this information provides a context for the stops. A common concern of officers facing allegations of disparate traffic stop practices is that in order to fully understand why an officer chooses to stop a particular vehicle one must know the context of the situation, e.g. was the stop part of a particular operation; and was the officer in a neighborhood of predominantly one race or ethnicity? By collecting basic information on the date, time, and location of the stop, a department can begin to measure this context.
License Number, State, and Description of Vehicle Stopped.
Collection of this information can help to ensure the accuracy of data collection procedures. By implementing a systematic mechanism for cross-checking data, police managers can check the information provided by officers against Department of Motor Vehicle information. In addition, given that many people have expressed the belief than an officer's decision to stop a vehicle is sometimes due to the type of vehicle being driven (e.g., a rental or an out-of-state vehicle), or to an officer's perception that the type of vehicle does not match the person driving it. Collecting this information will help departments assess how often these kinds of stops occur.
Length of Stop.
Officers should, with the assistance of the dispatcher, record the times at which a stop commenced and ended. Anecdotal evidence of racial profiling includes stories about vehicle stops lasting for extended periods of time. This information will help jurisdictions determine whether or not this is a problem for them.
Name and Identification Number of Officers Participating in the Stop.
One of the most controversial aspects of stop and search data collection is whether to include the identity of the officer making the stop. The primary advantage to capturing this data is that it would enable police departments to identify potential problem officers who may be disproportionately stopping minorities. The data collection process could thereby function as an early warning system, alerting management to problems and giving them an opportunity to investigate and, if necessary, to intervene with counseling, training or other appropriate responses.
As New Jersey Attorney General Farmer stated, "It [data collection] is definitely supposed to be part of an early warning system that enables us to identify a potential problem, go in and fix it rather than waiting for it to fester. For it to be an early warning system, we didn't see any way to do it unless we had officer identification." An alternative to officer identification may be the use of unit or district information. This may provide a way to analyze the data within a meaningful unit of analysis, without requiring the identity of the individual officer.
Study-Specific Data Collection Elements
This data should be collected on all traffic stops, regardless of whether or not a citation, search or arrest was made. When possible, these elements could also be linked to pre-existing routine forms or data entry.
Date of Birth.
Officers should record the date of birth that appears on the driver's license of the individual he or she has stopped. Some jurisdictions currently use two general categories, "juvenile" and "adult," to record age-related information on people stopped. However, this practice may conceal age distinctions. For example, in San Jose where police began collecting data in 1999, the preliminary analysis from the first year of data collection indicated that 97 percent of persons stopped were adults, and 3 percent minors. In the face of survey data showing that young Black Americans disproportionately perceive that they are stopped by the police both because of their race and their age, San Jose's data provides little useful information. In order to address these concerns, data on the actual age of people stopped should be available for analysis.
Officers should also record the gender of those they stop. Many departments already collect this information, some in response to lawsuits by women alleging that they were sexually harassed by officers who stopped them. In terms of racial profiling, data on gender is important, particularly in light of national survey data that indicates that Black males believe they are stopped disproportionately to Black females and to whites of both genders. The ability to disaggregate gender, therefore, will be an important analytical and informational tool.
Race, Ethnicity, or National Origin.
Unlike age and gender, an individual's race or ethnicity will generally not appear on a driver's license. Therefore, in order to record this information, an officer will either have to: 1) make a verbal inquiry of the individual; or 2) use his or her own subjective determination. Verbal inquiries, while perhaps more accurate, risk exacerbating tensions during what may already be a tense encounter. However, using the officer's perception of race or ethnicity, may minimize inconvenience and maximize officer safety. Moreover, since it is an officer's perception of race and/or ethnicity in the first place that gives rise to the problem of racial profiling, this is probably the most appropriate means of ascertaining this information. Whether or not the officer correctly determines a driver's race or ethnicity is less important than how that person was treated based on the officer's perception.
For data collection purposes we recommend the following racial and/or ethnic categories:
- Asian, Pacific Islander
- Native American
- Middle Eastern, East Indian
Certainly, the categories may be altered to more appropriately reflect the racial and ethnic demographics of local jurisdictions.
Reason for the Stop.
The reason an officer gives for stopping a vehicle is one of the most important pieces of information to be collected. While there are a myriad of reasons to stop a driver, the key is to simplify this category sufficiently to allow manageable collection, and at the same time to gather enough specific information to accurately monitor discretion. As discussed earlier, discretion is at the core of a law enforcement officer's job, permitting innovative, flexible problem-solving. However, it also provides opportunities for conscious and unconscious racial discrimination to affect decision-making. While in both low-discretion and high-discretion stops, the driver has, in fact, violated the law, officers vary most often in their responses to high-discretion situations. Therefore, disparate treatment is more likely to occur in high-discretion rather than low-discretion situations. To effectively measure this, data collection systems need to enable analysts to disaggregate: (1) the level of discretion available to the officer at the time of the stop; and (2) whether the stop was based on external information such as an all-points bulletin or a 911 call.
The use of the following categories will facilitate analysis of officer discretion as it relates to racial profiling:
- Hazardous Moving Violation -- Stop light or sign violation; driving ten miles or more above the speed limit.
- Non-Hazardous Moving Violation -- Failure to signal when changing lanes; driving less than ten miles over the speed limit.
- Externally Generated Information Stop -- 911 call; all-points bulletin.
- Vehicle Equipment Violations/Defects -- Broken head or brake light; under-inflated tires.
- Investigatory Stop -- Belief of criminal activity based on observation.
- Seatbelt Violation
- Driving While Impaired
- Courtesy Stop / Citizen Assistance
- Other Motor Vehicle Violation
Each police department should review its own stop and citation practices to determine what categories would be most appropriate.
Disposition or Outcome of the Stop.
The disposition of each traffic stop should be collected. While there are a plethora of police dispositions relevant to traffic stops, for the purposes of data collection, departments can design a system that limits the complexity and volume of disposition codes. Officers should be able to designate more than one disposition code, if appropriate.
The following codes would provide enough data for meaningful analysis, while limiting the number to make collection more manageable:
- Oral Warning
- Written Warning
- Arrest Made
- Arrest by Warrant
- Criminal Citation
- Traffic Citation - Hazardous
- Traffic Citation - Non-hazardous
- Courtesy Service / Citizen Assist
- No Action Taken
Whether a Search was Conducted.
This is a complex but invaluable item to collect. While there are numerous legal and operational factors involved in collecting information about searches, much of the information already gathered indicates that search data can provide illuminating information on racial profiling practices. Since searching is a relatively rare activity, ordinarily, most officers will not have to complete the set of inquiries that follows.
The following list suggests the search information officers should collect:
- Was a search conducted? (Yes / No)
- What type of search was conducted? (vehicle / driver / passenger)
- What was the authority for the search? (visible contraband / odor / canine alert / inventory search / consent search)
- Was contraband found? (Yes / No)
- Was property seized? (Yes / No)
- Describe the nature and quality of the contraband seized or found.
- Comment Box (allows officers to put in any contextual information that appeared relevant to the search, such as strategic initiative).
When using a computerized data collection system, the categories can default to "no" in order to speed data collection when no search was conducted.
While it may seem easier to omit search information from the process altogether, such information serves two valuable functions. First, it provides jurisdictions with information on the productivity of searches. This refers to the number of searches that result in arrests or seizures, the nature of those arrests, and the quality of the seizures. Such information will enable local jurisdictions to allocate resources to more productive search techniques as necessary. Second, information about searches will allow departments to assess if officers are disproportionately targeting certain groups.