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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.

This study comprises two parts: (1) an experimental analysis of whether reminders about payment and reasons why payment should be made are effective in getting offenders to pay their court-ordered economic sanctions; and (2) a survey of probationers who were ordered to pay economic sanctions that investigated five reasons why offenders may not pay their court-ordered restitution: inability to pay, not knowing what they owe, disagreeing with the purpose and fairness of restitution, perceived unfair treatment by the probation officer, and problems with the mechanics of paying. A sample of 771 probationers was selected from Centre County, Pennsylvania. All individuals included in the sample had an outstanding restitution balance. The experiment addressed two reasons offenders give for why they do not pay their court-ordered restitution: (a) lack of understanding of how much they owe and where their payments are directed and (b) a belief that the sanctions are unfair. A total of 771 offenders were randomly assigned to one of four conditions in a 2 x 2 between-subjects design in which, over a 6-month period in 2012, three quarters of the offenders received monthly letters that contained (a) information or no information about the economic sanctions they had paid and what they still owed (Information manipulation) and (b) a statement or no statement about reasons for paying restitution (Rationale manipulation). The remaining offenders did not receive a letter. Payments were tracked monthly for 6 months after the first letter was sent, and at 9, 12, and 18 months after the first letter, using the Pennsylvania Unified Judicial System website. The survey of the same probationers was administered approximately 14 months after the first letter was sent (8 months after the final letter). Of the 771 offenders, 149 returned a completed survey.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The study gathered information from police officers and residents of four different community areas that had undergone some form of police consolidation or contracting. The communities were the city of Pontiac in Michigan; the cities of Chisago and Lindstrom in Minnesota; York and Windsor Townships and the boroughs of Felton, Jacobus, Yoe, Red Lion, and Windsor in Pennsylvania; and the city of Compton in California. Surveys were administered to gauge the implementation and effectiveness of three models of police consolidation: merger of agencies, regionalization under which two or more agencies join to provide services in a broader area, and contracting by municipalities with other organizations for police services. The collection includes 5 SPSS files: ComptonFinal_Masked-by-ICPSR.sav (176 cases / 99 variables) MinnesotaFinal_Masked-by-ICPSR.sav (228 cases / 99 variables) PontiacFinal_Masked-by-ICPSR.sav (230 cases / 99 variables) YorkFinal_Masked-by-ICPSR.sav (219 cases / 99 variables) OfficerWebFINALrecodesaug2015revised_Masked-by-ICPSR.sav (139 cases / 88 variables)

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined the impact of technology on social, organizational, and behavioral aspects of policing. The present data represents an officer-level survey of four law enforcement agencies, designed to answer the following questions: (1) how are technologies used in police agencies across ranks and organizational sub-units? (2) how does technology influence organizational and personal aspects of police including - operations, culture, behavior, and satisfaction? (3) how do organizational and individual aspects of policing concurrently shape the use and effectiveness of technology? (4) how does technology affect crime control efforts and police-community relationships? (5) what organizational practices help to optimize the use of technology with an emphasis on enhance effectiveness and legitimacy?

This study was undertaken as a process evaluation of the Grants to Combat Violence Against Women on Campus Program (Campus Program), which was conducted by the Institute for Law and Justice under a grant from the National Institute of Justice (NIJ) and funding from the Violence Against Women Office (VAWO). The Campus Program was comprised of 38 colleges or universities, which received funding in 1999 and 2000. Part 1 data consist of basic demographic information about each campus and the violence against women programs and services available at each site. Data for Part 2, collected from questionnaires administered to grant project staff, documented perceptions about the Campus Program project and participation and collaboration from those involved in the partnership with each college or university (i.e., non-profit, non-governmental victim service providers).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This data collection represents an experimental micro-level geospatial crime prevention strategy that attempted to interrupt the near repeat (NR) pattern in residential burglary by creating a NR space-time high risk zone around residential burglaries as they occurred and then using uniformed volunteers to notify residents of their increased risk and provide burglary prevention tips. The research used a randomized controlled trial to test whether high risk zones that received the notification had fewer subsequent burglaries than those that did not. In addition, two surveys were administered to gauge the impact of the program, one of residents of the treatment areas and one of treatment providers. The collection contains 6 Stata datasets: BCo_FinalData_20180118_Archiving.dta(n = 484, 8 variables) Red_FinalData_20180117_Archiving.dta (n = 268, 8 variables) BCo_FinalDatasetOtherCrime_ForArchiving_v2.dta(n = 484, 8 variables) Redlands_FinalDataSecondary_ForArchiving_v2.dta (n = 266, 8 variables) ResidentSurvey_AllResponses_V1.4_ArchiveCleaned.dta (n = 457, 42 variables) VolunteerSurvey_V1.2_ArchiveCleaned.dta (n = 38, 16 variables) The collection also includes 5 sets of geographic information system (GIS) data: BaltimoreCounty_Bnd.zip BC_NR_HRZs.zip BurglaryAreaMinus800_NoApts.zip Redlands_CityBnd.zip RedlandsNR_HRZs.shp.zip

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This research expanded on offenders' decisions whether or not to offend by having explored a range of alternatives within the "not offending" category, using a framework derived from the concept of crime displacement. Decision trees were employed to analyze the multi-staged decision-making processes of criminals who are blocked from offending due to a situational crime control or prevention measure. The researchers were interested in determining how offenders evaluated displacement options as available alternatives. The data were collected through face-to-face interviews with 200 adult offenders, either in jail or on probation under the authority of the Texas Department of Criminal Justice, from 14 counties. Qualitative data collected as part of this study's methodology are not included as part of the data collection at this time. Three datasets are included as part of this collection: NIJ-2013-3454__Part1_Participants.sav (200 cases, 9 variables) NIJ-2013-3454__Part2_MeasuresSurvey.sav (2415 cases, 6 variables) NIJ-2013-3454__Part3_Vignettes.sav (1248 cases, 10 variables) Demographic variables included: age, gender, race, and ethnicity.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was designed to investigate whether the presence of CCTV cameras can reduce crime by studying the cameras and crime statistics of a controlled area. The viewsheds of over 100 CCTV cameras within the city of Philadelphia, Pennsylvania were defined and grouped into 13 clusters, and camera locations were digitally mapped. Crime data from 2003-2013 was collected from areas that were visible to the selected cameras, as well as data from control and displacement areas using an incident reporting database that records the location of crime events. Demographic information was also collected from the mapped areas, such as population density, household information, and data on the specific camera(s) in the area. This study also investigated the perception of CCTV cameras, and interviewed members of the public regarding topics such as what they thought the camera could see, who was watching the camera feed, and if they were concerned about being filmed.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Within the past fifteen years, policymakers across the country have increasingly supported criminal justice reforms designed to reduce the scope of mass incarceration in favor of less costly, more evidence-based approaches to preventing and responding to crime. One of the primary reform efforts is the Justice Reinvestment Initiative (JRI), a public-private partnership through which state governments work to diagnose the primary drivers of their state incarceration rates, reform their sentencing policies to send fewer nonviolent offenders to prison, and reinvest the saved money that used to go into prisons into alternatives to incarceration, instead. This mixed-methods study sought to assess public opinion about the justice reinvestment paradigm of reform and to determine whether exposure to racialized and race-neutral cues affects people's willingness to allocate money into criminal justice institutions versus community-based social services in order to reduce and prevent crime.

Recognizing that violence can be an intractable problem in many communities and that there are numerous approaches to both an immediate violence problem and the range of root causes behind violence, the National Institute of Justice funded an investigation into what factors underlie violence and efforts being implemented to address those factors and potentially reduce violence at the community level. In this mixed methods study, the RAND Corporation drew on data from key informant interviews, community surveys, administrative data, and geographic data to examine specific factors that contribute to violence, as well as a range of anti-violence efforts that have been used to address violence levels in two U.S. communities: the Bullseye area of Durham, North Carolina, and the Northside (North Minneapolis) neighborhood of Minneapolis, Minnesota. Specifically, the research project aimed to answer the following questions: What are community level factors that can contribute to persistent violence? What are the key factors in both cities that distinguish high violent crime areas compared to low violent crime areas? This collection contains final analytic datasets for Durham (DS1) and Minneapolis (DS2), violent crime rate data (DS3), community survey data for Durham (DS4) and Minneapolis (DS5), and multiple datasets containing community-level contextual factors from the American Community Survey (ACS) and geographical data from the U.S. Census Bureau (2009-2018) that were used to build the final analytic datasets (DS6-DS11). Qualitative data from key informant interviews and GIS data are not available for download at this time. Access to Durham and Minneapolis community survey data is restricted.