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This study followed the outcomes of the Bridge of Faith program. Bridge of Faith is an expansion project based on efforts of the Aim4Peace Violence Prevention Program, serving youth 13-24 years of age living in a prioritized area of Kansas City, Missouri. Bridge of Faith created goals and objectives that strategically address a continuum from response to violence exposure, intervention for violence survivors, and preventing of violence exposure. Activities were designed to target a reduction in risk factors and improvement in resiliency factors associated with the use of violence, as well as improve access to care and quality of services for those who are survivors of violence to reduce the probability of violence and exposure to others in the future. The overall purpose was to improve the health, social, and economic outcomes for youth and families who have been exposed to trauma and/or violence and prevent further violence from occurring. The project will facilitate these outcomes in specific goals and objectives to expand access to evidence-based programs and services for youth survivors through a new platform for collaborating agencies to link survivors of violence to additional wrap around services, and enhance the performance of service agencies through training, strengthening knowledge and skill development to ensure quality, trauma-informed, and culturally competent care.

This study on the Bridge of Faith Project was split into two datasets, Participant Survey Data and Police Data. Individuals were the unit of analysis measured in the Participant Survey Data, and criminal acts were the unit of analysis measured in the Police Data. Participant Survey Data contains 22 variables and 12 cases. Police Data contains 26 variables and 9 cases.

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models.

The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months.

A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study.

The statistical datasets consist of

• Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases
• Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases
• Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases
• Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases
• Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases
• Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases
• Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases
• Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases
• Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases
• Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases
• Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases
• Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases
• Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases
• Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases
• Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases.
• The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

The Detection of Crime, Resource Deployment, and Predictors of Success: A Multi-Level Analysis of Closed-Circuit Television (CCTV) in Newark, NJ collection represents the findings of a multi-level analysis of the Newark, New Jersey Police Department's video surveillance system. This collection contains multiple quantitative data files (Datasets 1-14) as well as spatial data files (Dataset 15 and Dataset 16). The overall project was separated into three components:

• Component 1 (Dataset 1, Individual CCTV Detections and Calls-For-Service Data and Dataset 2, Weekly CCTV Detections in Newark Data) evaluates CCTV's ability to increase the "certainty of punishment" in target areas;
• Component 2 (Dataset 3, Overall Crime Incidents Data; Dataset 4, Auto Theft Incidents Data; Dataset 5, Property Crime Incidents Data; Dataset 6, Robbery Incidents Data; Dataset 7, Theft From Auto Incidents Data; Dataset 8, Violent Crime Incidents Data; Dataset 9, Attributes of CCTV Catchment Zones Data; Dataset 10, Attributes of CCTV Camera Viewsheds Data; and Dataset 15, Impact of Micro-Level Features Spatial Data) analyzes the context under which CCTV cameras best deter crime. Micro-level factors were grouped into five categories: environmental features, line-of-sight, camera design and enforcement activity (including both crime and arrests); and
• Component 3 (Dataset 11, Calls-for-service Occurring Within CCTV Scheme Catchment Zones During the Experimental Period Data; Dataset 12, Calls-for-service Occurring Within CCTV Schemes During the Experimental Period Data; Dataset 13, Targeted Surveillances Conducted by the Experimental Operators Data; Dataset 14, Weekly Surveillance Activity Data; and Dataset 16, Randomized Controlled Trial Spatial Data) was a randomized, controlled trial measuring the effects of coupling proactive CCTV monitoring with directed patrol units.

Over 40 separate four-hour tours of duty, an additional camera operator was funded to monitor specific CCTV cameras in Newark. Two patrol units were dedicated solely to the operators and were tasked with exclusively responding to incidents of concern detected on the experimental cameras. Variables included throughout the datasets include police report and incident dates, crime type, disposition code, number of each type of incident that occurred in a viewshed precinct, number of CCTV detections that resulted in any police enforcement, and number of schools, retail stores, bars and public transit within the catchment zone.

Since 2015, the Bureau of Justice Assistance (BJA) has funded sites to engage in reforms intended to improve the national response to sexual assault cases. The goals of this initiative are to (1) create a coordinated community response that ensures just resolution to unsubmitted sexual assault kit (SAK) cases through a victim-centered approach and (2) build jurisdictions' capacity to prevent the development of conditions that lead to high numbers of unsubmitted sexual assault kits. Site efforts to address these issues include agencies such as law enforcement, prosecution, forensic laboratories, and victim advocacy service organizations. Westat was awarded a contract by the National Institute of Justice (NIJ) to assess components of BJA's Sexual Assault Kit Initiative (SAKI). The study includes (1) an evaluability assessment of 17 sites to determine their readiness for an evaluation, (2) a process evaluation and system reform assessment of the 17 sites, (3) a feasibility assessment of using case level data for an outcome evaluation, and analysis of a subset of unsubmitted SAK cases to identify how characteristics of incidents, offenders, and victims are associated with case processing decisions and outcomes, and (4) development of a long-term outcome evaluation plan.

Two sources of data are archived with NAJCD: (1) coded qualitative data from primarily on-site interviews the Westat Team conducted in 2018 with stakeholders from 17 of the fiscal year (FY) 2015 SAKI grantees and 2 private lab facilities and 2) quantitative case-level data from the 2 FY 2015 SAKI grantees on SAKI cases associated with previously unsubmitted sexual assault kits that were determined to contain foreign DNA or biological evidence through laboratory testing. The interview data file contains coded data from 172 interviews the research team conducted with one or more agency representatives regarding their organization's goals, strategies, and activities for processing sexual assault kits, and associated lessons learned, challenges, and expected outcomes. The quantitative case-level data file includes case-level information on 576 sexual assault kits determined to have DNA and associated cases included in the 2 sites' SAKI inventories. The case-level data captures information on case or offense-level information (e.g., date of offense, date offense reported to police, number of victims and suspects involved, investigation and prosecution activities), victim-level information (e.g., victim age, sex, race, participation in investigation), and suspect-level information (e.g., suspect's age, race, sex, criminal history).

In 2018 Justice and Security Strategies, Inc. (JSS) recieved funding from the National Institute of Justice (NIJ) to code and analyze detailed homicide data collected by the Los Angeles Police Department (LAPD). The data in this study were compiled from the LAPD's digitized homicide casefiles, or Murder Books, stored at the Homicide Library. These digitized casefiles included relevant information from the chronological log of detective activity, crime scene log, first responder statements and officer reports, incident history from the CAD and 911 tapes, Los Angeles Fire Department medical service report, death/autopsy report, suspect arrest report, evidence reports, follow-up reports, investigator final report, search warrants, crime scene sketches, crime scene notes, scientific evidence analysis, witness statements and notes, suspect interview notes, and case status information. A total of 4,111 Murder Books detailing homicides from four of 21 LAPD area stations in South Los Angeles between 1990 to 2010 were digitally scanned and preserved for posterity. Through a grant from the National Institute of Justice, JSS researchers 'tagged' the data to facilitate sharing these Murder Books among detectives in the South Bureau. These 'tags' also serve to encode features and outcomes from the Murder Book case files.

The data archived from this project consist of the digital database of the tagged Murder Books. There are two files included in this project. The victim file contains information about both the incident (obtainable from unique incident numbers) and information about the victim (victim number associated with each incident). The suspect file contains information about the suspects associated with each incident and can be linked to the original incident through the incident number.

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.

The Second International Self-Report Delinquency Study (ISRD-2) was a large international collaborative study of delinquency and victimization of 12 to 15 year-old students in seventh, eighth, and ninth grade classrooms. The study was a school-based study that drew on random samples from either city level or national level. In general, the cross-national description of the prevalence and incidence of delinquent behavior allowed for the assessment of national crime rates by comparison with the crime rates of other countries. The study was conducted in 31 mostly European countries, the United States, Caribbean and South American countries. The primary research questions explored included:

• Is juvenile delinquency normal, ubiquitous, and transitional?
• Is there a pattern of similarity in the offending behavior of juveniles across countries or are there any important differences? Descriptive comparisons of crime rates will call for explanations, especially if differences are observed.
• What are the national socio-economic or cultural differences, or the characteristics of legal or criminal policies that can explain such differences?

The purpose of this research was to further understanding of why crime occurs where it does by exploring the spatial etiology of homicides that occurred in Washington, DC, during the 13-year period 1990-2002.

The researchers accessed records from the case management system of the Metropolitan Police, District of Columbia (MPDC) Homicide Division to collect data regarding offenders and victims associated with the homicide cases. Using geographic information systems (GIS) software, the researchers geocoded the addresses of the incident location, the victim's residence, and offender's residence for each homicide case. They then calculated both Euclidean distance and shortest path distance along the streets between each address per case. Upon applying the concept of triad as developed by Block et al. (2004) in order to create a unit of analysis for studying the convergence of victims and offenders in space, the researchers categorized the triads according to the geometry of locations associated with each case. (Dots represented homicides in which the victim and offender both lived in the residence where the homicide occurred; lines represented homicides that occurred in the home of either the victim or the offender; and triangles represented three non-coincident locations: the separate residences of the victim and offender, as well as the location of the homicide incident.) The researchers then classified each triad according to two separate mobility triangle classification schemes: Traditional Mobility, based on shared or disparate social areas, and Distance Mobility, based on relative distance categories between locations. Finally, the researchers classified each triad by the neighborhood associated with the location of the homicide incident, the location of the victim's residence, and the location of the offender's residence.

A total of 3 statistical datasets and 7 geographic information systems (GIS) shapefiles resulted from this study. Note: All datasets exclude open homicide cases. The statistical datasets consist of Offender Characteristics (Dataset 1) with 2,966 cases; Victim Characteristics (Dataset 2) with 2,311 cases; and Triads Data (Dataset 3) with 2,510 cases. The GIS shapefiles have been grouped into a zip file (Dataset 4). Included are point data for homicide locations, offender residences, triads, and victim residences; line data for streets in the District of Columbia, Maryland, and Virginia; and polygon data for neighborhood clusters in the District of Columbia.

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.