Search Results

In this project, the research team developed and evaluated an artificial intelligence (AI) tool using agent-based modeling methods for crime analysis and risk evaluation (CARE): CAREsim. The purpose of this tool was to improve the effectiveness of person-based patrol strategies, where police take preemptive actions upon selected high-risk individuals (determined based on factors known to police such as violent crime history) when predicted risks of committing crimes are high. CARESim was developed and tested with a simulated randomized controlled experiment within the jurisdiction of Hampton, Virginia. 240 high-risk individuals (120 in each group) were followed for a 12-month period, with the simulation lasting 23 months. The treatment group received additional crime analyses using the AI tool and more focused patrols, while the control group received analyses as usual and random patrols in the simulated environment. The tool was evaluated on a series of outcomes (e.g., number of crimes and arrests) comparing the control and treatment groups. This collection contains the simulated high-risk individual data (DS1) and the simulated crimes data (DS2) used for the experiment.

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 reports the findings of a randomized controlled trial (RCT) involving more than 400 police officers and the use of body-worn cameras (BWC) in the Las Vegas Metropolitan Police Department (LVMPD). Officers were surveyed before and after the trial, and a random sample was interviewed to assess their level of comfort with technology, perceptions of self, civilians, other officers, and the use of BWCs. Information was gathered during ride-alongs with BWC officers and from a review of BWC videos. The collection includes 2 SPSS data files, 4 Excel data files, and 2 files containing aggregated treatment groups and rank-and-treatment groups, in Stata, Excel, and CSV format: SPSS: officer-survey---pretest.sav (n=422; 30 variables) SPSS: officer-survey---posttest2.sav (n=95; 33 variables) Excel: officer-interviews---form-a.xlsx (n=23; 52 variables) Excel: officer-interviews---form-b.xlsx (n=27; 52 variables) Excel: ride-along-observations.xlsx (n=72; 20 variables) Excel: video-review-data.xlsx (n=53; 21 variables) Stata: hours-and-compensation-rollup-to-treatment-group.dta (n=4; 42 variables) Excel: hours-and-compensation-rollup-to-treatment-group.xls (n=4; 42 variables) CSV: hours-and-compensation-rollup-to-treatment-group.csv (n=4; 42 variables) Stata: hours-and-compensation-rollup-to-rank-and-treatment-group.dta (n=12; 43 variables) Excel: hours-and-compensation-rollup-to-rank-and-treatment-group.xls (n=12; 43 variables) CSV: hours-and-compensation-rollup-to-rank-and-treatment-group.csv (n=12; 43 variables)

The Drug Market Intervention (DMI) has been identified as a promising practice for disrupting overt-drug markets, reducing the crime and disorder associated with drug sales, and improving police-community relations. Montgomery County, Maryland; Flint, Michigan; Guntersville, Alabama; Lake County, Indiana; Jacksonville, Florida; New Orleans, Louisiana; and Roanoke, Virginia applied for and received DMI training and technical assistance from Michigan State University in 2010 and 2011. This study followed the seven sites that were trained in the program to determine how the program was implemented, how the DMI affected the targeted drug market, whether the program affected crime and disorder, whether the program improved police-community relations, and how much the program cost.

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.

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 collection was part of a larger two-phase project funded by the National Institute of Justice (NIJ). Phase I focused on the development and estimation of predictive crime models in Shreveport, Louisiana and Chicago, Illinois. Phase II involved the implementation of a prevention model using the predictive model. To evaluate the two predictive policing pilot programs funded by NIJ, RAND evaluated the predictive and preventative models employed by the Shreveport Police Department titled Predictive Intelligence Led Operational Targeting (PILOT). RAND evaluated whether PILOT was associated with a measurable reduction in crime. The data were used to determine whether or not there was a statistically significant reduction in property crime counts in treated districts versus control districts in Shreveport. The collection includes 1 Excel file (Shreveport_Predictve_Policing_Evaluation_Experiment_Data.xlsx (n=91; 8 variables)) related only to the property crime aspect of the study. Neither data used to perform the outcomes evaluation for the Chicago Police Department experiment nor qualitative data used to help perform the prediction and prevention model evaluations are available.

Clearance rates for most crimes have remained low and stable for decades (Braga et al., 2011), despite advances in police technologies (Koper et al., 2015). Many police agencies have developed triaging practices for criminal investigations using solvability factors to guess which cases are most likely to be solved and to allocate investigative resources to those crimes (Eck, 1983; 1992). This practice partially stems from a persistent belief that resolving crimes and the resulting clearance rates are due to circumstances of the crime and community context, and are beyond the control of police. However, a growing body of research has challenged this belief, demonstrating that enhanced investigative efforts can improve crime clearance rates beyond solvability factors (Braga and Dusseault, 2018; Lum and Wellford, 2023). In this study, the research team sought to determine if investigative follow-ups could increase clearance rates for robbery and burglary cases (frequently occurring crime types with traditionally low clearance rates) and increase victim satisfaction with police services. Agencies selected for the study were the Seattle Police Department (SPD) in Seattle, Washington, and the Rochester Police Department (RPD) in Rochester, New York. Both agencies triaged a large proportion of robbery cases and would have a large enough sample size to successfully carry out an experiment. The original study design was a randomized controlled trial. In both sites, robbery cases would be allocated to either the intervention condition--an investigative follow-up conducted by an officer during their daily patrol assignment--or the control condition with no follow-up. Challenges to personnel and agency funding from the COVID-19 pandemic, the murder of George Floyd, and other officer-involved deaths in 2020 led to difficulties implementing the study as initially designed. The experiment was not initiated in Rochester, and initiated but not completed in Seattle. Therefore, the team transitioned to a natural quasi-experiment design in Rochester and added a case analysis of robberies in Seattle. This collection contains three datasets: victim satisfaction surveys from Seattle (DS1, n=39) and Rochester (DS2, n=37), and supplemental reports on follow-ups made during the Seattle experiment implementation (DS3, n=82).

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 was an evaluation of the Los Angeles Police Department's (LAPD) Training Evaluation and Management System II (TEAMS II) Early Intervention System conducted by Justice and Security Strategies, Inc. TEAMS II was designed to identify officers at-risk for engaging in future problematic behavior. This system was mandated as part of the Consent Decree (Section II) that was formally entered into on June 15, 2001 between the U.S. Department of Justice and the LAPD. Justice and Security Strategies, Inc. research staff worked with the Information Technology Bureau to obtain and analyze TEAMS II data, conducted informal interviews with officers, sergeants, civilians, command staff, and technologists involved with TEAMS II, and worked with the TEAMS II contractors to examine and provide recommendations. The data collection includes 3 Stata data files. The concentration analysis dataset (TEAMS-Concentration-Analysis-FINAL-v2.dta) with 143 variables for 15,710 cases, the regression-discontinuity dataset (TEAMS-Regression-Discontinuity-FINAL.dta) with 98 variables for 297,779 cases, and the time series dataset (TEAMS-Time-Series-FINAL.dta) with 43 variables for 192 cases. Demographic variables included as part of this data collection include officer age, gender, ethnicity, education level, and total number of officers employed by demographics.

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 represents the first comprehensive national assessment of law enforcement uses of and perspectives on sex offender registration and notification (SORN) systems. The two-year, mixed-method study featured collection and analysis of interview data from over two-dozen jurisdictions, and administration of a nationwide survey of law enforcement professionals. The study examined ways in which law enforcement leaders, uniformed staff, and civilian staff engaged in SORN-related duties perceive SORN's roles and functions, general effectiveness, and informational utility. Additionally, the study elicited law enforcement perspectives related to promising SORN and related sex offender management practices, perceived barriers and challenges to effectiveness, and policy reform priorities. This collection includes two SPSS data files and one SPSS syntax file: "LE Qualitative Data.sav" with 55 variables and 101 cases, "LE Quantitative Data-ICPSR.sav" with 201 variables and 1402 cases and "LE Quantitative Data Syntax.sps". Qualitative data from interviews conducted with law enorcement professionals are not available at this time.

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)

This study made use of existing data to examine the effect of implementing school resource officers (SROs) on exclusionary discipline (e.g., arrests, suspensions) as well as perceptions of school climate in a school district in the Midwestern United States. The data used in this study were from district administrative records from the 1999-2000 through 2015-2016 school years. Among the schools in district that have implemented SROs, this study estimated the rates of exclusionary discipline and perceptions of school climate over the years before SROs were implemented, and compared those outcomes to parallel measures from after SROs were implemented. Schools that have not implemented SROs acted as a comparison group. Analytic techniques included latent growth curve modeling with multiple group piecewise models to examine differences between (a) schools with and without SROs (b) before and after SRO implementation. This study also incorporated interviews and surveys with SROs for the purpose of understanding how their roles and responsibilities in schools as well as their subjective experiences of stress may play a role in the relationships examined in the study.