Enhancing the Research Partnership Between the Albany Police Department and the Finn Institute, 2005-2016

Version
v1
Resource Type
Dataset : administrative records data, survey data
Creator
  • Worden, Robert E.
  • McLean, Sarah J.
Other Title
  • Version 1 (Subtitle)
Publication Date
2020-12-16
Publication Place
Ann Arbor, Michigan
Publisher
  • Inter-University Consortium for Political and Social Research
Funding Reference
  • United States Department of Justice. Office of Justice Programs. National Institute of Justice
Language
English
Free Keywords
Schema: ICPSR
crime control; crime reduction; police community relations; police effectiveness; police patrol; police reports; police training
Description
  • Abstract

    The Finn Institute is an independent, not-for-profit corporation that conducts research on matters of public safety and security. The project provided for steps that would strengthen and enhance an existing police-researcher partnership, focused around analyses of proactive policing. As part of a research partnership with the Albany Police Department (APD) and the Finn Institute, this study was oriented around a basic research question: can proactive policing be conducted more efficiently, in the sense that a better ratio of high-value to lower-value stops is achieved, such that the trade-off between crime reduction and police community relations is mitigated. Albany Resident Survey Dataset (DS1) unit of analysis was individuals. Variables include neighborhood crime and disorder, legitimacy and satisfaction with police service, and direct and vicarious experience with stop and perceptions of stops as a problem. Demographic variables include age, race, education, employment, marital status, and household count. Management of "Smart Stops" Dataset (DS2) unit of analysis was investigatory stops; variables include records of individual stops, the month and year of the stop, whether the location of the stop was a high-crime location, whether the person stopped (or any of the persons stopped, if multiple people were stopped at one time) were high-risk, and whether the stop resulted in an arrest. Trends in Proactive Policing Dataset (DS3) unit of analysis was APD officers. Variables include number of stops per quarter; variables include demographics such as officer characteristics such as their assignments, length of service, and gender.
  • Abstract

    The Albany (New York) Police Department (APD) and the John F. Finn Institute partnered to conduct empirical research designed to increase the efficiency of proactive policing, by emphasizing stops that are most likely to be instrumental in crime reduction, i.e., "high-value" stops--stops with the greatest potential crime-reduction benefits - to all stops. Insofar as proactive policing can be conducted more efficiently, then it may be possible to side-step the trade-off between crime control and legitimacy. By differentiating among stops in terms of their potential for crime reduction and managing them accordingly, it may be possible to realize the crime control value of proactive policing and at the same time reduce the strain that it can place on police-community relations. In addition, researchers identified officers who were skilled in proactive policing and sought to learn from them how they performed this task. Researchers anticipated that if they could distinguish what practices set skilled officers apart from more ordinary officers, then this knowledge could be translated for use by police practitioners to shape training curricula. Albany Resident Survey (DS1): One outcome of smarter stops might be an improvement in public trust and confidence in the police - police legitimacy. The survey was planned as the first of a two-wave panel survey, in terms of which to assess the impact of feedback on stops. Management of "Smart Stops" (DS2): Researchers posit that stops have greater presumptive value for crime reduction when they are conducted in high-crime places, involve high-risk people, or produce enforcement outcomes. Such stops are "high-value" stops. Analysis of stops showed that, though stops and crime were correlated spatially, only 40 percent of the stops were high-value stops, leaving room for strategic improvement. Management of "Smart Stops" (DS2): Stops by Albany police followed a generally downward trend after 2006. The annual numbers of stops edged modestly downward through 2008, and declined much more steeply after 2009, which coincided with a change in administration and a very different emphasis, from what might be characterized as intelligence-led policing to community policing. Other components of our project made it clear that individual officers vary widely in their levels of investigatory stops.
  • Methods

    The project relied on a mixed method approach, including quantitative analyses of department data, quantitative analyses of survey data, and qualitative analyses of interview data. Researchers (a) collected and analyzed data on stops, levels of crime in the immediate vicinity of stops, and the risk factors for crime among the people who were stopped, with a view toward differentiating high-value and lower-value stops; (b) developed analytical protocols and products intended to provide regular feedback on the ratio of high- to lower-value stops for operational commanders; (c) and identified and studied officers who are especially successful in making high-value stops, to better understand the practices that make them successful. Albany Resident Survey (DS1): From September to October 2014, a phone survey was administered to assess Albany residents' attitudes toward Albany police. Respondents were asked several questions about their perceptions of the Albany police. The survey was planned as the first of a two-wave panel survey, in terms of which to assess the impact of feedback on stops. When it became clear that the feedback was not feasible for APD to regularly produce and deliver, the second wave of the survey was cancelled. The survey instrument was based largely on the instrument that we used in a previous project (Worden & McLean, 2017), which was based on previously fielded surveys, including those that Skogan administered in Chicago in the 1990s, and surveys of procedural justice and legitimacy conducted by Tyler and others. It included several items in terms of which to measure residents' trust and confidence in the Albany police, as well as their direct and vicarious experiences with police. Since the survey was administered in September-October, it captures citizens' attitudes in the immediate aftermath of high-profile incidents of police use of deadly force, in Ferguson, Missouri, and New York City. Management of "Smart Stops" (DS2): Researchers established three criteria for high-value stops: stops in high-crime places; stops of high-risk people; and stops with successful enforcement outcomes. Researchers applied these criteria to stops occurring from 2005 through the end of 2014. Data on stops came from Albany Police Department's (APD) computer-aided dispatch (CAD) and record management systems. Additional information about stops was drawn from arrest reports, field interview cards, and traffic tickets. APD personnel were consulted in the selection of incident types among officer-initiated incidents that should be treated as investigatory stops. In addition, researchers excluded stops conducted by traffic units on the premise that they are predominantly for the more limited purpose of traffic enforcement. The locations of stops are identified only in terms of a generic identifier for the "street unit" (an intersection or street block), 106 of which were designated high-crime based on an analysis of crime trajectories (Wheeler, et al., 2016) and the knowledge of the working group members. The persons stopped are identified only in terms of their assessed risk for offending, based on (1) their arrest histories for Part I crimes, weapon offenses, or sex offenses within the past 5 years, (2) prior juvenile arrest histories, and (3) their status as an identified gang member; no individual characteristics are included in the data. Trends in Proactive Policing (DS3): Researchers examined CAD data on individual officers' stops cross-sectionally, focusing on 2016, and longitudinally, from 2007 through 2016. Focusing on officers assigned to patrol (including both uniformed motor patrol and community policing specialists), the effects of officers' characteristics were analyzed.
  • Methods

    Albany Resident Survey (DS1) variables include demographics; neighborhood characteristics such as crime and disorder; opinions on dimensions of police service such as legitimacy and satisfaction; and direct and vicarious experiences with stops and perceptions of stops as a problem. Management of "Smart Stops" (DS2) variables describe stops in terms of date and street unit as well as three criteria to determine high-value stops: stops in high-crime places; stops of high-risk people; and stops with successful enforcement outcomes Trends in Proactive Policing (DS3) variable includes officer characteristics (year of experience, gender, division assignment, etc.) and quarterly counts for stops by individual officers from 2007 through 2016.
  • Methods

    Presence of Common Scales: None
  • Methods

    Response Rates: Not available.
  • Abstract

    Datasets:

    • DS0: Study-Level Files
    • DS1: Albany Resident Survey Dataset
    • DS2: Management of "Smart Stops" Dataset
    • DS3: Trends in Proactive Policing Dataset
Temporal Coverage
  • Time period: 2005--2016
  • 2005 / 2016
  • Collection date: 2014-09--2014-10
  • 2014-09 / 2014-10
  • Collection date: 2005--2014
  • 2005 / 2014
  • Collection date: 2007--2016
  • 2007 / 2016
Geographic Coverage
  • Albany (New York)
  • New York (state)
  • United States
Sampled Universe
Albany Resident Survey (DS1): Albany Residents of Albany, NY Management of "Smart Stops" (DS2): Investigatory stops by Albany (NY) police Trends in Proactive Policing (DS3): Albany (NY) patrol officers Smallest Geographic Unit: Zip code
Sampling
Albany resident survey (DS1): the survey sample was a stratified probability sample of 800 residents, with households over sampled from among four high-crime zip codes (12202, 12206, 12207, and 12210) relative to households in three medium-crime zip codes (12203, 12204, and 12205) and two low-crime zip codes (12208 and 12209).The survey rests on a probability sample of 800 residents, with oversampling from higher-crime zip codes and statistical weighting to represent the entire city. Management of "Smart Stops" (DS2): the sample came out of a population of recorded stops came from the Albany Police Department's computer-aided dispatch (CAD) and record management systems. Additional information about stops was drawn from arrest reports, field interview cards, and traffic tickets. Researchers endeavored to differentiate investigatory stops from officer-initiated events that either involve no contact with a person (such as building checks) or are not of an enforcement nature, and they sought to distinguish investigatory stops from stops made only for the purpose of traffic law enforcement. Trends in Proactive Policing (DS3): for cross-sectional analysis of 2016 stops, researchers concentrated on all 145 officers assigned to either of the two patrol divisions or to the NEU. For longitudinal analysis of stop trajectories between 2007 and 2016, researchers concentrated on 131 officers who in 2016 had at least 4 years of service as an Albany police officer.
Collection Mode
  • face-to-face interview
  • telephone interview
Note
Funding institution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (2013-MU-CX-0012).
Availability
Delivery
One or more files in this study are not available for download due to special restrictions; consult the study documentation to learn more on how to obtain the data.
Alternative Identifiers
  • 37820 (Type: ICPSR Study Number)

Update Metadata: 2020-12-16 | Issue Number: 2 | Registration Date: 2020-12-16