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This study investigated the complex relationship between unauthorized immigration and crime at the local level. Through a mix of data fusion, synthetic population modeling, and detailed crime reporting from selected jurisdictions, the study sought to produce nuanced insights to challenge prevailing assumptions about immigration and crime, ultimately aiding in informed policy-making and resource allocation.

This study employed crime and crime reporting data from ten jurisdictions across the United States paired with synthetic data which estimated the unauthorized immigrant population. This research aimed to provide an in-depth analysis at the census tract level. Analyses focused on unauthorized immigration and its correlation with drug, property, and violent crime rates, while accounting for crime reporting in traditional and emerging immigrant destinations along with sites with low foreign populations.

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 integrated neighborhood-level data on robbery and burglary gathered from local police agencies across the United States, foreclosure data from RealtyTrac (a real estate information company), and a wide variety of social, economic, and demographic control variables from multiple sources. Using census tracts to approximate neighborhoods, the study regressed 2009 neighborhood robbery and burglary rates on foreclosure rates measured for 2007-2008 (a period during which foreclosure spiked dramatically in the nation), while accounting for 2007 robbery and burglary rates and other control variables that captured differences in social, economic, and demographic context across American neighborhoods and cities for this period. The analysis was based on more than 7,200 census tracts in over 60 large cities spread across 29 states. Core research questions were addressed with a series of multivariate multilevel and single-level regression models that accounted for the skewed nature of neighborhood crime patterns and the well-documented spatial dependence of crime.

The study contains one data file with 8,198 cases and 99 variables.

Over the last several years, the topic of immigration has gained increased attention from politicians, policymakers, and the media. This attention has centered on the prevalence of undocumented immigrants entering and residing within the United States, concern over increasing crime rates involving undocumented immigrants, and the appropriateness of the various policies aimed at controlling the influx of undocumented immigrants into the country. The recent wave of immigration from Latin America has led to a renewed public outcry and overall concerns regarding the relationship between immigration, crime and gang involvement, and the safety of the American public.

Thus, the goal of this project was to conduct a multi-methodological study to examine immigrants' involvement in crime, gang membership, and experiences with violent victimization. In addition, this project examined alcohol and drug use among immigrants. This project relied on data collected in Maricopa County, Arizona. Specifically, this project relied on

1. analyses of previously collected quantitative self-report data from a sample of recently booked arrestees,
2. analyses of quantitative self-report data collected from a community sample of immigrants (of different immigration statuses) and US-born citizens, and
3. analysis of qualitative data collected from a community sample of immigrants (of different immigration statuses) and US-born citizens.

The results provide a more comprehensive understanding of the relationship between immigration status and crime, gang involvement, and victimization as well as an understanding of immigrants' alcohol and drug use, relative to US-born citizens.

The effective treatment of youth with co-occurring mental health and substance use disorders placed in juvenile justice residential facilities aims to effect positive change among youth in the system's care and promote public safety. This study aimed to examine the prevalence of co-occurring disorders among a multiyear, statewide sample of youth completing residential placement within the juvenile justice system in the state of Florida.

• Determine the prevalence of mental health and substance use disorders, and their co-occurrence among youth placed in long-term juvenile justice facilities across the state of Florida
• Assess the impact of service matching to assessed dynamic risk factors, dosage of intervention services actually provided to each youth, and treatment quality/fidelity of those interventions on both changes in risk and protective factors during placement and post-release recidivism outcomes
• provide policy recommendations related to the efficacy of best practices through the combination of service matching/dosage/treatment quality of treatment within residential facilities among youth presenting with co-occurring disorders

Despite public attention to the problem of human trafficking, it has proven difficult to measure the problem. Improving the quality of information about human trafficking is critical to developing sound anti-trafficking policy. In support of this effort, in 2013 the Federal Bureau of Investigation incorporated human trafficking offenses in the Uniform Crime Reporting (UCR) program. Despite this achievement, there are many reasons to expect the UCR program to underreport human trafficking. Law enforcement agencies struggle to identify human trafficking and distinguishing it from other crimes. Additionally, human trafficking investigations may not be accurately classified in official data sources. Finally, human trafficking presents unique challenges to summary and incident-based crime reporting methods. For these reasons, it is important to understand how agencies identify and report human trafficking cases within the UCR program and what part of the population of human trafficking victims in a community are represented by UCR data. This study provides critical information to improve law enforcement identification and reporting of human trafficking.

Coding criminal incidents investigated as human trafficking offenses in three US cities, supplemented by interviews with law and social service stakeholders in these locations, this study answers the following research questions:

• How are human trafficking cases identified and reported by the police?
• What sources of information about human trafficking exist outside of law enforcement data?
• What is the estimated disparity between actual instances of human trafficking and the number of human trafficking offenses reported to the UCR?

This study tests the efficacy of an intervention--Safe Public Spaces (SPS) -- focused on improving the safety of public spaces in schools, such as hallways, cafeterias, and stairwells. Twenty-four schools with middle grades in a large urban area were recruited for participation and were pair-matched and then assigned to either treatment or control. The study comprises four components: an implementation evaluation, a cost study, an impact study, and a community crime study.

Community-crime-study: The community crime study used the arrest of juveniles from the NYPD (New York Police Department) data. The data can be found at (https://data.cityofnewyork.us/Public-Safety/NYPD-Arrests-Data-Historic-/8h9b-rp9u). Data include all arrest for the juvenile crime during the life of the intervention. The 12 matched schools were identified and geo-mapped using Quantum GIS (QGIS) 3.8 software. Block groups in the 2010 US Census in which the schools reside and neighboring block groups were mapped into micro-areas. This resulted in twelve experimental school blocks and 11 control blocks which the schools reside (two of the control schools existed in the same census block group). Additionally, neighboring blocks using were geo-mapped into 70 experimental and 77 control adjacent block groups (see map). Finally, juvenile arrests were mapped into experimental and control areas. Using the ARIMA time-series method in Stata 15 statistical software package, arrest data were analyzed to compare the change in juvenile arrests in the experimental and control sites.

Cost-study: For the cost study, information from the implementing organization (Engaging Schools) was combined with data from phone conversations and follow-up communications with staff in school sites to populate a Resource Cost Model. The Resource Cost Model Excel file will be provided for archiving. This file contains details on the staff time and materials allocated to the intervention, as well as the NYC prices in 2018 US dollars associated with each element. Prices were gathered from multiple sources, including actual NYC DOE data on salaries for position types for which these data were available and district salary schedules for the other staff types. Census data were used to calculate benefits.

Impact-evaluation: The impact evaluation was conducted using data from the Research Alliance for New York City Schools. Among the core functions of the Research Alliance is maintaining a unique archive of longitudinal data on NYC schools to support ongoing research. The Research Alliance builds and maintains an archive of longitudinal data about NYC schools. Their agreement with the New York City Department of Education (NYC DOE) outlines the data they receive, the process they use to obtain it, and the security measures to keep it safe.

Implementation-study: The implementation study comprises the baseline survey and observation data. Interview transcripts are not archived.

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 current study sought to expand the current understanding of the psychometric characteristics of the collective efficacy scale at the individual level and the role of collective efficacy in promoting safe, healthy community conditions. A team of interviewers consisting of residents of the targeted neighborhoods were selected and trained to administer the field surveys (NIJ Neighborhoods Resident Survey Data, 108 variables, n=649). In order to ensure accuracy of the responses, the field supervisor conducted telephone validation for approximately ten to fifteen percent of the surveys. In addition to resident surveys, trained research staff conducted systematic social observations (SSOs) of street segments in selected neighborhoods noting physical and social indictors.

The use of common operational picture (COP) technology can give law enforcement and its public safety response partners the capacity to develop a shared situational awareness to support effective and timely decision-making. These technologies collate and display information relevant for situational awareness (e.g., the location and what is known about a crime incident, the location and operational status of an agency's patrol units, the duty status of officers).

CNA conducted a mixed-methods study including a technical review of COP technologies and their capacities and a set of case studies intended to produce narratives of the COP technology adoption process as well as lessons learned and best practices regarding implementation and use of COP technologies.

This study involved four phases over two years: (1) preparation and technology review, (2) qualitative case studies, (3) analysis, and (4) development and dissemination of results. This study produced a market review report describing the results from the technical review, including common technical characteristics and logistical requirements associated with COP technologies and a case study report of law enforcement agencies' adoption and use of COP technologies. This study provides guidance and lessons learned to agencies interested in implementing or revising their use of COP technology. Agencies will be able to identify how they can improve their information sharing and situational awareness capabilities using COP technology, and will be able to refer to the processes used by other, model agencies when undertaking the implementation of COP technology.

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).

This study collected and combined data from a large number of sources (e.g. crime data, land use data, parolee data, business and employment data, etc.) to study crime and crime trends across two counties in Southern California: Los Angeles and Orange counties. The crime data comes from a number of police agencies for the years 2005-12. Crime data is not available for all cities for all years. The variables from other sources are for the year 2010. All data is aggregated to the common geographic unit of census tracts. The data come from the following sources: 1) crime data from police agencies; 2) socio-demographic data from the American Community Survey (ACS) obtained from ICPSR; 3) business data from Mint data; 4) land use data from the Southern California Association of Governments; 5) voluntary organization data from the National Center for Charitable Statistics.

This wide array of information allows accounting for the multi-dimensional and inter-related sources of crime and crime trends in Southern California in neighborhoods (census tracts). Using these data, the project: 1) built a model to predict crime in small geographic areas; 2) assessed the effect of neighborhood organizations and institutions on crime rates; 3) determined the effect of the spatial distribution of poverty (at both small and large scales) on crime rates; 4) assessed how the clustering of social problems in a neighborhood affects neighborhood crime over time. This project built on prior work done by the Metropolitan Futures Initiative (MFI) team to locate various data sources in Southern California.

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 includes data collected with the purpose of creating an integrated dataset that would allow researchers to address significant, policy-relevant gaps in the literature--those that are best answered with cross-jurisdictional data representing a wide array of economic and social factors. The research addressed five research questions:

1. What is the impact of gentrification and suburban diversification on crime within and across jurisdictional boundaries?
2. How does crime cluster along and around transportation networks and hubs in relation to other characteristics of the social and physical environment?
3. What is the distribution of criminal justice-supervised populations in relation to services they must access to fulfill their conditions of supervision?
4. What are the relationships among offenders, victims, and crimes across jurisdictional boundaries?
5. What is the increased predictive power of simulation models that employ cross-jurisdictional data?

The Denver Youth Survey (DYS) is part of the larger "Program of Research on the Causes and Correlates of Delinquency" initiated by the Office of Juvenile Justice and Delinquency Prevention in 1986. The DYS is a longitudinal study of problem and successful behavior over the life course that focuses on delinquency, drug use, victimization, and mental health. The DYS is based on a probability sample of households in "high-risk" neighborhoods of Denver, Colorado. These neighborhoods were selected on the basis of their social ecology in terms of population and housing characteristics. Only socially disorganized neighborhoods with high official crime rates (top one-third) were included. The survey respondents include 1,528 children and youth who were 7, 9, 11, 13, or 15 years old in 1987, and one of their parents, who lived in one of the more than 20,000 randomly selected households.

The survey respondents include 807 boys and 721 girls and include White (10 percent), Latino (45 percent), and African American (33 percent) youth, as well as 12 percent from other racial/ethnic backgrounds. The child and youth respondents, along with one caretaker, were interviewed annually from 1988 until 1992, and annually from 1995 until 1999. The age range covered by the study is from age 7 through age 26.

The dataset contains 1,528 cases and 22,081 variables.

The Denver Youth Survey (DYS) is part of the larger "Program of Research on the Causes and Correlates of Delinquency" initiated by the Office of Juvenile Justice and Delinquency Prevention in 1986. It is a longitudinal study of problem and successful behavior over the life course that focuses on delinquency, drug use, victimization, and mental health. DYS variables also address family demographics, neighborhood characteristics, parenting, and involvement in social roles.

The DYS is based on a probability sample of households in "high-risk" neighborhoods of Denver, Colorado. These neighborhoods were selected on the basis of their social ecology in terms of population and housing characteristics. Only socially disorganized neighborhoods with high (top one-third) official crime rates were included. The survey respondents include 1,528 children and youth who were 7, 9, 11, 13, or 15 years old in 1987, and one of their parents, who lived in one of the more than 20,000 randomly selected households.

The survey respondents include 807 boys and 721 girls and include White (10%), Latino (45%), and African American (33%) youth, as well as 12% from other racial/ethnic backgrounds. The child and youth respondents, along with one caretaker, were interviewed annually from 1988 until 1992 (waves 1-5), annually from 1995 until 1999 (waves 6-10), and in 2003 (wave 11). The study covers an age range of 7 through 26.

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.

Fingermark identification remains one of the most important and unambiguous approaches to place perpetrators at crime scenes. While a great number of forensic techniques for the visualization of latent marks already exist, they all suffer from one or more shortcomings such as: limited applicability with regard to the age of a mark or the nature of the surface it was deposited on ("substrate"); the requirement of expensive laboratory equipment and special training; and the potential to alter or even destroy evidence, or at least leave a visible record of their application.

The goal of this project was to develop and validate novel fingermark lifters, which allow instantaneous, on-the-spot visualization of marks. The underlying detection principle used with these lifters is based on the reaction of either pH-sensitive or amine-reactive substances - immobilized on suitable solid supports such as membranes - with chemicals contained in fingermark residues (e.g., lactic acid, amino acids, proteins, and amino sugars). The exposure of appropriate reagents to such an environment causes a change in their spectroscopic properties, which can be seen, depending on the type of reagent, either under ambient or luminescent light conditions.