This survey was undertaken to learn more about how often and under what circumstances police-public contact becomes problematic. The Bureau of Justice Statistics (BJS) initiated surveys of the public on their interactions with police in 1996 with the first Police-Public Contact Survey, a pretest among a nationally representative sample of 6,421 persons aged 12 or older. That initial version of the questionnaire revealed that about 20 percent of the public had direct, face-to-face contact with a police officer at least once during the year preceding the survey. At that time, the principal investigator estimated that about 1 in 500 residents, or about a half million people, who had an encounter with a police officer also experienced either a threat of force or the actual use of force by the officer. The current survey, an improved version of the 1996 Police-Public Contact Survey, was fielded as a supplement to the National Crime Victimization Survey (ICPSR 6406) during the last six months of 1999. A national sample nearly 15 times as large as the pretest sample in 1996 was used. The 1999 survey yielded nearly identical estimates of the prevalence and nature of contacts between the public and the police. This survey, because of its much larger sample size, permits more extensive analysis of demographic differences in police contacts than the 1996 pretest. In addition, it added a new and more detailed set of questions about traffic stops by police, the most frequent reason given for contact with police. Variables in the dataset cover type of contact with police, including whether it was face-to-face, initiated by the police or the citizen, whether an injury to the officer or the citizen resulted from the contact, crimes reported, and police use of force. Demographic variables supplied for the citizens include gender, race, and Hispanic origin.

This data contains information related to officer-initated stops by the City of Boulder Police Department. Information on the outcome of the stop (stop location, duration, search, and result) is included in this file. See the "Demographics of Police Stops" dataset for more details on the demographics of the person stopped (sex, race, ethnicity, year of birth, whether they are a Boulder resident). This demographic data is collected at the stop level, and no individual-level identifiers are recorded in the system during a stop.The data published are limited to stops where the officer initiated, or had discretion, in making a stop. Instances where an officer is responding to a community or police call are considered non-discretionary, and demographics information is not collected for those stops and not included here. There are some instances of non-discretion within a stop interaction as well. For example, there may be instances where there is an outstanding felony warrant for the person stopped, and by law the officer must arrest that person.Please read the methodology and data dictionary documents for more information. The fields for this demographics dataset are referred to as the "Results" file in the data dictionary.

NOTE: This is an archive version of NOPD Use of Force Incidents, and was last updated on April 27th, 2021. The data in this dataset are in the original format (one row per officer per subject interaction), and are no longer being updated. Please switch to the new format (one row per incident). This dataset represents use of force incidents by the New Orleans Police Department reported per NOPD Use of Force policy. This dataset includes initial reports that may be subject to change through the review process. This dataset reflects the most current status and information of these reports. This dataset includes one row of data for each combination of officer that used force and subject of force during the incident. For example, if during a use of force incident two officers used force and two people were the subject of force, there will be four rows associated with that incident in this dataset. The number of rows in this dataset does not represent the number of times force was used by NOPD officers. This dataset is updated nightly. Disclaimer: The New Orleans Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The New Orleans Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The New Orleans Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of New Orleans or New Orleans Police Department web page. The user specifically acknowledges that the New Orleans Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. Any use of the information for commercial purposes is strictly prohibited. The unauthorized use of the words "New Orleans Police Department," "NOPD," or any colorable imitation of these words or the unauthorized use of the New Orleans Police Department logo is unlawful. This web page does not, in any way, authorize such use.

This data contains information related to officer-initated stops by the City of Boulder Police Department. Information on the demographics of the person stopped (sex, race, ethnicity, year of birth, whether they are a Boulder resident) is included in this file. See the "Outcomes of Police Stops" dataset for more details on the outcome of the stop (stop location, duration, search, and result). This demographic data is collected at the stop level, and no individual-level identifiers are recorded in the system during a stop.The data published are limited to stops where the officer initiated, or had discretion, in making a stop. Instances where an officer is responding to a community or police call are considered non-discretionary, and demographics information is not collected for those stops and not included here. There are some instances of non-discretion within a stop interaction as well. For example, there may be instances where there is an outstanding felony warrant for the person stopped, and by law the officer must arrest that person.Please read the methodology and data dictionary documents for more information. The fields for this demographics dataset are referred to as the "Main" file in the data dictionary.

This data collection contains two different datasets. The first dates relates to a telephone survey of local residents in six areas that formed the test and control sites in a quasi-experiment that tested the effect of mounted police community patrols on public 'trust and confidence' in the police. The second dataset pertains to a programme of Systematic Social Observation of mounted and foot police patrols in the same areas and also other police districts. Files relating to the two datasets are included in different zip foldersMounted police are a feature of public policing around the world. Police on horseback are used primarily in public-order police work, particularly in the policing of large crowds, as well as for a number of other functions such as urban patrols. Mounted police represent a symbol of physical force as well as a connection to past eras of policing. They are thought to calm crowds and avert disorder in ways ‘standard’ police activity cannot. They are also among the most poorly-understood tools in modern public police work. In current conditions of austerity mounted police are, like many other police units or activities, under threat of retrenchment or elimination. Yet policy decisions are being undertaken without recourse to evidence, since virtually no focused research has ever been conducted with mounted police. There is no empirical basis on which to make claims regarding their utility. This project will investigate the ways in which mounted police work is experienced in the UK through a unique observational methodology, alongside focus groups with police officers and citizens. This research will build an evidence base for future strategies and priorities regarding mounted policing. The project is supported by the Association of Chief Police Officers (ACPO) and mounted police units across the UK. Two data collection methods were used. The telephone survey used standard survey methods to collect public opinion and experiential data. The systematic social observation used a bespoke mobile phone app to collect data on the quantity and quality of interactions between members of the public and police in community patrol settings.

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes 1.6 gb of stop data from North Carolina, covering all of 2010 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

The Police-Public Contact Survey (PPCS) provides detailed information on the nature and characteristics of face-to-face contacts between police and the public, including the reason for and outcome of the contact. The PPCS interviews a nationally representative sample of United States residents aged 16 years or older as a supplement to the National Crime Victimization Survey. To date, the PPCS has been conducted five times by the Bureau of Justice Statistics (BJS):

• The first survey -- described in the BJS publication Police Use of Force: Collection of National Data (NCJ 165040) -- documented levels of contacts with police during 1996.
• The second survey -- described in Contacts between Police and the Public: Findings from the 1999 National Survey (NCJ 184957) -- recorded police-citizen contacts in 1999. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 1999: [UNITED STATES] (ICPSR 3151).
• The third survey -- described in Contacts between Police and the Public: Findings from the 2002 National Survey (NCJ 207845) -- covered interactions between police and the public in 2002. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2002: [UNITED STATES] (ICPSR 4273).
• The fourth survey -- described in the BJS publication, Contacts between Police and the Public, 2005 (NCJ 215243) -- covered interactions between police and the public in 2005. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2005: [UNITED STATES] (ICPSR 20020).
• The fifth survey -- described in the BJS publication, Contacts between Police and the Public, 2008 (NCJ 234599) -- covered interactions between police and the public in 2008. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2008 (ICPSR 32022).

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.The purpose of the study was to implement a "platform-based" methodology for collecting data about police organizations and the communities they serve with the goals of generating in-depth standardized information about police organizations, personnel and practices and to help move policing in the direction of evidence-based "learning-organizations" by providing judicious feedback to police agencies and policy makers. The research team conducted three web-based Law Enforcement Organizations (LEO) surveys of sworn and civilian law enforcement employees (LEO Survey A Data, n=22,765; LEO Survey B Data, n=15,825; and LEO Survey C Data, n=16,483). The sample was drawn from the 2007 Law Enforcement Management and Administrative Statistics (LEMAS) database. Agencies with 100 to 3,000 sworn police personnel were eligible for participation. To collect data for the Police-Community Interaction (PCI) survey (PCI Data, n=16,659), each week department employees extracted names and addresses of persons who had recent contact with a police officer because of a reported crime incident, traffic accident or traffic stop. Typically, the surveys were completed within two to four weeks of the encounter.

Research shows that policies and institutions often maintain and reinforce social hierarchies, such as with law enforcement in the United States, keeping those belonging to historically dominant groups in favored positions, while pushing out those in historically marginalized communities. While this dynamic has oft been examined with respect to race, historically marginalized communities are more varied than solely a Black:White comparison. Using a unique data set of traffic stops made by large municipal police departments in California, I quantitatively examine whether and when disparities exist in who is searched and subsequently found with contraband during a traffic stop across a broader range of historically marginalized communities. For example, I find disparities in search and contraband hit rates between disabled and able-bodied drivers. A key implication of these results is that collecting information on a limited number of driver characteristics obscures important differences in public-police interactions.

The Toronto’s Police Service Annual Statistical Report (ASR) is a comprehensive overview of police related statistics including reported crimes, victims of crime, search of persons, firearms, traffic collisions, personnel, budget, communications, public complaints, regulated interactions and other administrative information. This dataset contains the following categories of information: Hate Crimes This portion of the dataset is provided by Intelligence Services to identify the count of Hate Crimes per year. Detailed Information on Hate/Bias crime can be found in the publication Hate Bias Statistical Report https://www.torontopolice.on.ca/publications/ Miscellaneous Budget This portion of the dataset is provided by Financial Services to identify the actual budget expenditures. R.I.D.E. This portion of the dataset is provided by Traffic Services to identify the number of vehicles stopped and charges laid as a result of R.I.D.E. This data is related to table (ASR-MISC-TBL-001) in The Annual Statistical Report Additional information can be found here.

This study's purpose was twofold: to investigate the nature of police patrol work in a community policing context and to field-test data collection instruments designed for systematic social observation. The project, conducted in Richmond, Virginia, where its police department was in the third year of a five-year plan to implement community policing, was designed as a case study of one police department's experience with community policing, focusing on officers in the patrol division. A team of eight researchers conducted observations with the police officers in the spring and summer of 1992. A total of 120 officers were observed during 125 observation sessions. Observers accompanied officers throughout their regular work shifts, taking brief field notes on officers' activities and encounters with the public. All of an observed officer's time during the shift was accounted for by either encounters or activities. Within 15 hours of the completion of the ridealong, the observer prepared a detailed narrative account of events that occurred during the ridealong and coded key items associated with these events. The study generated five nested quantitative datasets that can be linked by common variables. Part 1, Ridealong Data, provides information pertinent to the 125 observation sessions or "rides." Part 2, Activity Data, focuses on 5,576 activities conducted by officers when not engaged in encounters. Data in Part 3, Encounter Data, describe 1,098 encounters with citizens during the ridealongs. An encounter was defined as a communication between officers and citizens that took over one minute, involved more than three verbal exchanges between an officer and a citizen, or involved significant physical contact between the officer and citizen. Part 4, Citizen Data, provides data relevant to each of the 1,630 citizens engaged by police in the encounters. Some encounters involved more than one citizen. Part 5, Arrest Data, was constructed by merging Parts 1, 3, and 4, and provides information on 451 encounters that occurred during the ridealongs in which the citizen was suspected of some criminal mischief. All identification variables in this collection were created by the researchers for this project. Variables from Part 1 include date, start time, end time, unit, and beat assignment of the observation session, and the primary officer's and secondary officer's sex, race/ethnicity, years as an officer, months assigned to precinct and beat, hours of community policing training, and general orientation to community policing. Variables in Part 2 specify the time the activity began and ended, who initiated the activity, type, location, and visibility of the activity, involvement of the officer's supervisor during the activity, and if the activity involved problem-solving, or meeting with citizens or other community organizations. Part 3 variables include time encounter began and ended, who initiated the encounter, primary and secondary officer's energy level and mood before the encounter, problem as radioed by dispatcher, and problem as it appeared at the beginning of the encounter and at the end of the encounter. Information on the location of the encounter includes percent of time at initial location, visibility, officer's prior knowledge of the initial location, and if the officer anticipated violence at the scene. Additional variables focus on the presence of a supervisor, other police officers, service personnel, bystanders, and participants, if the officer filed or intended to file a report, if the officer engaged in problem-solving, and factors that influenced the officer's actions. Citizen information in Part 4 includes sex, age, and race/ethnicity of the citizen, role in the encounter, if the citizen appeared to be of low income, under the use of alcohol or drugs, or appeared to have a mental disorder or physical injury or illness, if the citizen was representing an establishment, if the citizen lived, worked, or owned property in the police beat, and if the citizen had a weapon. Also presented are various aspects of the police-citizen interaction, such as evidence considered by the officer, requests and responses to each other, and changes in actions during the encounter. Variables in Part 5 record the officer's orientation toward community policing, if the suspect was arrested or cited, if the offense was serious or drug-related, amount of evidence, if the victim requested that the suspect be arrested, if the victim was white, Black, and of low income, and if the suspect represented an organization. Information on the suspect includes gender, race, sobriety level, if of low income, if 19 years old or less, if actively resistant, if the officer knew the suspect adversarially, and if the suspect demonstrated conflict with others. Some items were recoded for the particular analyses for which the Arrest Data were constructed.

Political scientists have increasingly begun to study how citizen characteristics shape whether—and how—they interact with the police. Less is known about how officer characteristics shape these interactions. In this article, we examine how one officer characteristic—officer sex—shapes the nature of police-initiated contact with citizens. Drawing on literature from multiple fields, we develop and test a set of competing expectations. Using over four million traffic stops made by the Florida State Highway Patrol and Charlotte (North Carolina) Police Department, we find that women officers are less likely to search drivers than men on the force. Despite these lower search rates, when women officers do conduct a search, they are more likely to find contraband and they confiscate the same net amount of contraband as men. These results indicate that women officers are able to minimize the number of negative interactions with citizens without losses in effectiveness.

This research sought to evaluate the implementation of body worn cameras (BWCs) in the Los Angeles Police Department (LAPD). Researchers employed three strategies to evaluate the impact of BWCs in the department: 1) two-wave officer surveys about BWCs, 2) two-wave Systematic Social Observations (SSOs) of citizen interactions from officer ride-alongs, and 3) a time series analysis of existing LAPD data of use of force and complaint data. The officer surveys were conducted in the Mission and Newton divisions of the LAPD before and after BWCs were implemented. The survey instrument was designed to measure perceptions of BWCs across a variety of domains and took approximately 20 minutes to complete. Researchers attended roll calls for all shifts and units to request officer participation and administered the surveys on tablets using the Qualtrics software. The pre-deployment survey was administered in both divisions August and September 2015. The post-deployment surveys were conducted with a subset of officers who participated in the pre-deployment surveys during a two-week period in the summer of 2016, approximately nine months following the initial rollout of BWCs. The SSO data was collected in the Mission and Newton divisions prior to and following BWC implementation. The pre-administration SSOs were conducted in August and September 2015 and the post-administration SSOs were conducted in June and August, 2016. Trained observers spent 725 hours riding with and collecting observational data on the encounters between officers and citizens using tablets to perform field coding using Qualtrics software. A total of 124 rides (71 from Wave I and 53 from Wave II) were completed between both Newton and Mission Divisions. These observations included 514 encounters and involved coding the interactions of 1,022 citizens, 555 of which were deemed to be citizens who had full contact, which was defined as a minute or more of face-time or at least three verbal exchanges. Patrol officers (including special units) for ride-alongs were selected from a master list of officers scheduled to work each day and shift throughout the observation period. Up to five officers within each shift were randomly identified as potential participants for observation from this master list and observers would select the first available officer from this list. For each six-hour observation period, or approximately one-half of a shift, the research staff observed the interactions between the assigned officer, his or her partner, and any citizens he or she encountered. In Wave 2, SSOs were conducted with the same officers from Wave 1. The time series data were obtained from the LAPD use of force and complaint databases for each of the 21 separate patrol divisions, a metropolitan patrol division, and four traffic divisions of the LAPD. These data cover the time period where BWC were implemented throughout the LAPD on a staggered basis by division from 2015 to 2018. The LAPD operates using four-week deployment periods (DPs), and there are approximately 13 deployment periods per year. These data span the period of the beginning of 2012 through the 2017 DP 12. These data were aggregated to counts by deployment period based on the date of the originating incident. The LAPD collects detailed information about each application of force by an officer within an encounter. For this reason, separate use of force counts are based on incidents, officers, and use of force applications. Similarly, the LAPD also collects information on each allegation for each officer within a complaint and public complaint counts are based on incidents, officers, and allegations.

DATASET DESCRIPTION: This dataset provides the case report number, the date the incident occurred, subject race and gender at the time of the interaction and the lead charge. This dataset contains only instances where a warning was issued to the subject of the interaction for a violation.

GENERAL ORDERS RELATING TO TRAFFIC ENFORCEMENT: After stopping the violator, officers shall exercise good judgment in deciding what enforcement action should be taken (e.g., warning, citation, arrest). Additionally, field release citations and warnings shall be completed as outlined in General Order 308, which permits law enforcement agencies to use citation release procedures in lieu of arrest for specified Class A or B misdemeanor offenses, and all Class C misdemeanor offenses with certain exceptions.

AUSTIN POLICE DEPARTMENT DATA DISCLAIMER: 1. The data provided is for informational use only and may differ from official Austin Police Department crime data.

1. The Austin Police Department’s databases are continuously updated, and changes can be made due to a variety of investigative factors including but not limited to offense reclassification and dates.

2. Reports run at different times may produce different results. Care should be taken when comparing against other reports as different data collection methods and different systems of record may have been used.

4.The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided.

City of Austin Open Data Terms of Use - https://data.austintexas.gov/stories/s/ranj-cccq

The National Initiative for Building Community Trust and Justice (the National Initiative) is a joint project of the National Network for Safe Communities, the Center for Policing Equity, the Justice Collaboratory at Yale Law School, and the Urban Institute, designed to improve relationships and increase trust between communities and law enforcement. Funded by the Department of Justice, this mixed-methods evaluation aimed to assess outcomes and impacts in six cities that participated in the National Initiative, which include Birmingham, AL; Fort Worth, TX; Gary, IN; Minneapolis, MN; Pittsburgh, PA; and Stockton, CA. The data described herein represent two waves of surveys of residents living in the highest-crime, lowest-income residential street segments in the six National Initiative cities. The first wave was conducted between September 2015 and January 2016, and the second wave was conducted between July and October 2017. Survey items were designed to measure neighborhood residents' perceptions of their neighborhood conditions--with particular emphases on neighborhood safety, disorder, and victimization--and perceptions of the police as it relates to procedural justice, police legitimacy, officer trust, community-focused policing, police bias, willingness to partner with the police on solving crime, and the law. The data described herein are from pre- and post-training assessment surveys of officers who participated in three trainings: 1) procedural justice (PJ) conceptual training, which is the application of PJ in the context of law enforcement-civilian interactions, as well as its role in mitigating historical tensions between law enforcement and communities of color; 2) procedural justice tactical, which provided simulation and scenario-based exercises and techniques to operationalize PJ principles in officers' daily activities; and 3) implicit bias, which engaged officers in critical thought about racial bias, and prepared them to better identify and handle identity traps that enable implicit biases. Surveys for the procedural justice conceptual training were fielded between December 2015 and July 2016; procedural justice tactical between February 2016 and June 2017; and implicit bias between September 2016 and April 2018. Survey items were designed to measure officers' understanding of procedural justice and implicit bias concepts, as well as officers' levels of satisfaction with the trainings.

This study sought to identify the impact of the communication training program given to deputies in Alachua County, Florida, on the community's attitudes toward community law enforcement activities, particularly crime prevention and neighborhood patrols. To determine the success of the communication training for the Alachua deputies, researchers administered a survey to residents in the target neighborhood before the communication program was implemented (Part 1: Pretest Data) and again after the program had been established (Part 2: Post-Test Data). The survey instrument developed for use in this study was designed to assess neighborhood respondents' attitudes regarding (1) community law enforcement, defined as the assignment of deputies to neighborhoods on a longer term (not just patrol) basis with the goal of developing and implementing crime prevention programs, (2) the communication skills of deputies assigned to the community, and (3) the perceived importance of community law enforcement activities. For both parts, residents were asked how important it was to (1) have the same deputies assigned to their neighborhoods, (2) personally know the names of their deputies, and (3) work with the deputies on crime watch programs. Residents were asked if they agreed that the sheriff's office dealt with the neighborhood residents effectively, were good listeners, were easy to talk to, understood and were interested in what the residents had to say, were flexible, were trustworthy, were safe to deal with, and were straightforward, respectful, considerate, honest, reliable, friendly, polite, informed, smart, and helpful. Demographic variables include the gender, race, age, income, employment status, and educational level of each respondent.

The purpose of the Project on Policing Neighborhoods (POPN) was to provide an in-depth description of how the police and the community interact with each other in a community policing (CP) environment. Research was conducted in Indianapolis, Indiana, in 1996 and in St. Petersburg, Florida, in 1997. Several research methods were employed: systematic observation of patrol officers (Parts 1-4) and patrol supervisors (Parts 5-14), in-person interviews with patrol officers (Part 15) and supervisors (Parts 16-17), and telephone surveys of residents in selected neighborhoods (Part 18). Field researchers accompanied their assigned patrol or supervising officer during all activities and encounters with the public during the shift. Field researchers noted when various activities and encounters with the public occurred during these "ride-alongs," who was involved, and what happened. In the resulting data files coded observation data are provided at the ride level, the activity level (actions that did not involve interactions with citizens), the encounter level (events in which officers interacted with citizens), and the citizen level. In addition to encounters with citizens, supervisors also engaged in encounters with patrol officers. Patrol officers and patrol supervisors in both Indianapolis and St. Petersburg were interviewed one-on-one in a private interviewing room during their regular work shifts. Citizens in the POPN study beats were randomly selected for telephone surveys to determine their views about problems in their neighborhoods and other community issues. Administrative records were used to create site identification data (Part 19) and data on staffing (Part 20). This data collection also includes data compiled from census records, aggregated to the beat level for each site (Part 21). Census data were also used to produce district populations for both sites (Part 22). Citizen data were aggregated to the encounter level to produce counts of various citizen role categories and characteristics and characteristics of the encounter between the patrol officer and citizens in the various encounters (Part 23). Ride-level data (Parts 1, 5, and 10) contain information about characteristics of the ride, including start and end times, officer identification, type of unit, and beat assignment. Activity data (Parts 2, 6, and 11) include type of activity, where and when the activity took place, who was present, and how the officer was notified. Encounter data (Parts 3, 7, and 12) contain descriptive information on encounters similar to the activity data (i.e., location, initiation of encounter). Citizen data (Parts 4, 8, and 13) provide citizen characteristics, citizen behavior, and police behavior toward citizens. Similarly, officer data from the supervisor observations (Parts 9 and 14) include characteristics of the supervising officer and the nature of the interaction between the officers. Both the patrol officer and supervisor interview data (Parts 15-17) include the officers' demographics, training and knowledge, experience, perceptions of their beats and organizational environment, and beliefs about the police role. The patrol officer data also provide the officers' perceptions of their supervisors while the supervisor data describe supervisors' perceptions of their subordinates, as well as their views about their roles, power, and priorities as supervisors. Data from surveyed citizens (Part 18) provide information about their neighborhoods, including years in the neighborhood, distance to various places in the neighborhood, neighborhood problems and effectiveness of police response to those problems, citizen knowledge of, or interactions with, the police, satisfaction with police services, and friends and relatives in the neighborhood. Citizen demographics and geographic and weight variables are also included. Site identification variables (Part 19) include ride and encounter numbers, site beat (site, district, and beat or community policing areas [CPA]), and sector. Staffing variables (Part 20) include district, shift, and staffing levels for various shifts. Census data (Part 21) include neighborhood, index of socioeconomic distress, total population, and total white population. District population variables (Part 22) include district and population of district. The aggregated citizen data (Part 23) provide the ride and encounter numbers, number of citizens in the encounter, counts of citizens by their various roles, and by sex, age, race, wealth, if known by the police, under the influence of alcohol or drugs, physically injured, had a weapon, or assaulted the police, counts by type of encounter, and counts of police and citizen actions during the encounter.