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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.

CrimeStat III is a spatial statistics program for the analysis of crime incident locations, developed by Ned Levine and Associates under the direction of Ned Levine, PhD, that was funded by grants from the National Institute of Justice (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037). The program is Windows-based and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. CrimeStat is being used by many police departments around the country as well as by criminal justice and other researchers.

The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant formats using either spherical or projected coordinates. It calculates various spatial statistics and writes graphical objects to ArcGIS, MapInfo, Surfer for Windows, and other GIS packages.

CrimeStat is organized into five sections:

• Primary file - this is a file of incident or point locations with X and Y coordinates. The coordinate system can be either spherical (lat/lon) or projected. Intensity and weight values are allowed. Each incident can have an associated time value.
• Secondary file - this is an associated file of incident or point locations with X and Y coordinates. The coordinate system has to be the same as the primary file. Intensity and weight values are allowed. The secondary file is used for comparison with the primary file in the risk-adjusted nearest neighbor clustering routine and the duel kernel interpolation.
• Reference file - this is a grid file that overlays the study area. Normally, it is a regular grid though irregular ones can be imported. CrimeStat can generate the grid if given the X and Y coordinates for the lower-left and upper-right corners.
• Measurement parameters - This page identifies the type of distance measurement (direct, indirect or network) to be used and specifies parameters for the area of the study region and the length of the street network. CrimeStat III has the ability to utilize a network for linking points. Each segment can be weighted by travel time, travel speed, travel cost or simple distance. This allows the interaction between points to be estimated more realistically.

• Spatial distribution - statistics for describing the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, the convex hull, or directional mean.
• Spatial autocorrelation - statistics for describing the amount of spatial autocorrelation between zones, including general spatial autocorrelation indices - Moran's I , Geary's C, and the Getis-Ord General G, and correlograms that calculate spatial autocorrelation for different distance separations - the Moran, Geary, Getis-Ord correlograms. Several of these routines can simulate confidence intervals with a Monte Carlo simulation.
• Distance analysis I - statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis, and Ripley's K statistic. There is also a routine that assigns the primary points to the secondary points, either on the basis of nearest neighbor or point-in-polygon, and then sums the results by the secondary point values.
• Distance analysis II - calculates matrices representing the distance between points for the primary file, for the distance between the primary and secondary points, and for the distance between either the primary or secondary file and the grid.
• 'Hot spot' analysis I - routines for conducting 'hot spot' analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, and risk-adjusted nearest neighbor hierarchical clustering. The hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls.
• 'Hot spot' analysis II - more routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC), K-means clustering, Anselin's local Moran, and the Getis-Ord local G statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation.

• Interpolation I - a single-variable kernel density estimation routine for producing a surface or contour estimate of the density of incidents (e.g., burglaries) and a dual-variable kernel density estimation routine for comparing the density of incidents to the density of an underlying baseline (e.g., burglaries relative to the number of households).
• Interpolation II - a Head Bang routine for smoothing zonal data that can be applied to events (volumes), rates or can be used to create rates. In addition, there is an interpolated Head Bang routine for interpolating the smoothed Head Bang result to grid cells.
• Space-time analysis - a set of tools for analyzing clustering in time and in space. These include the Knox and Mantel indices, which look for the relationship between time and space, and the Correlated Walk Analysis module, which analyzes and predicts the behavior of a serial offender and a spatial-temporal moving average.
• Journey to crime analysis - a simple criminal justice method for estimating the likely location of a serial offender given the distribution of incidents and a model of travel distance. The routine allows the user to estimate a travel model with a calibration file and apply it to the serial events. It can be used to identify a likely location given the distribution of 'points' and assumptions about travel behavior. There is a routine for drawing lines between origins and destinations (crime trips).
• Bayesian journey to crime analysis - an advanced criminal justice method for estimating the likely location of a serial offender given the distribution of incidents, a model of travel distance, and an origin-destination matrix showing the relationship between where crimes were committed and where offenders lived. A diagnostics routine analyzes serial offenders for whom their residence is known and estimates which of several journey to crime estimates is most accurate. A selected method can be applied to identify a likely residence location of a single serial offender given the distribution of incidents, assumptions about travel behavior, and the origin of offenders who committed crimes in the same locations.
• Regression modeling - a module for analyzing a relationship between a dependent variable and one or more independent variables. The CrimeStat regression module includes both Ordinary Least Squares and Poisson-based regression models, estimated from Maximum Likelihood (MLE) or Markov Chain Monte Carlo (MCMC) algorithms. The current version includes six different models including OLS, Poisson with Linear Dispersion Correction, Poisson-Gamma and a Poisson-Gamma-Conditional Autoregressive (CAR) spatial regression model. The module can handle very large datasets through a Block Sampling approach. There is also a module for applying estimated coefficients to a new dataset to make predictions.

Crime travel demand modeling is a new module in CrimeStat III. It is an application of travel demand modeling, widely used in transportation planning, to crime analysis. The analysis is done by zones. First, crime 'trips' are defined as a link between an offender residence/origin location and a crime location. The number of crimes originating in each zone is counted as is the number of crimes ending in each zone. Second, the model is run sequentially in four separate stages with multiple routine in each stage:

• Trip Generation - Separate models are produced that predict the number of crimes originating in each zone (origins) and the number of crimes ending in each zone (destinations). CrimeStat III uses a multivariate Poisson regression model, with stepwise options, to create the prediction. Trips from outside the study area (external trips) can be added to the origin model to account for travel from outside the region. Once the models are created, a balancing procedure ensures that the number of origins equals the number of destinations.
• Trip Distribution - Using the predicted number of crime trips originating in each zone and the predicted number of trips occurring in each zone, the second stage distributes trips from each zone to every other zone using a gravity model. There are routines for calculating the actual (observed) distribution from individual data, for estimating the prediction coefficients, and for applying the predicted coefficients to the predicted origins and destinations. Another routine allows a comparison of the predicted trip distribution with the observed trip distribution.
• Mode Split - The predicted number of trips for each zone-to-zone pair can be split into likely travel modes using an accessibility function that approximates the utility of one mode relative to the others.
• Network Assignment - Finally, the predicted trips from each zone to every other zone by travel mode are assigned to a likely route based on the shortest path algorithm. The output includes the likely routes taken for each origin-destination zone pair and the total volume of trips on network links. This step requires a travel network, one for each travel mode. There are additional utilities for calculating transit networks from station/stop locations and for testing for one-way streets.

• Parameters can be saved and re-loaded.
• Tab colors can be changed.
• Monte Carlo simulation data can be output.

CrimeStat is accompanied by sample datasets and a manual that gives the background behind the statistics and examples. The manual also discusses applications of CrimeStat developed by other analysts and researchers. The program and sample data sets are in Windows-based zipped files that can be downloaded. The manual is a set of individual chapters in PDF files. They can be viewed online or downloaded. If downloading the PDF chapters separately, they should be saved into the same directory as the CrimeStat program. If the PDF file names are not renamed, they can be accessed directly from the program's help menu.

The CrimeStat Libraries (version 1.0) are component objects that allow for the functions of CrimeStat to be programmed directly into custom software or systems. The CrimeStat Libraries include all of the routines that were developed through version 2.0 of the regular CrimeStat program, including spatial description, hot spot analysis, and kernel density interpolation routines. Additional spatial autocorrelation routines have been included. The libraries can input dbf, shape, and Ascii text files and can output to shape file, MIF/MID files, ASCII text files, and KML files.

This project sought to gather and analyze data on the effects of marijuana legalization from primary and secondary data sources that are both local and national in scope, and at both the individual and aggregate level. Since 1996, 37 states have passed statutes legalizing marijuana for medical and/or recreational use, while it has remained illegal under federal law. Jurisdictional and temporal variation in law creates a complex environment and substantial challenges for police and prosecutors charged with enforcement, and little is known about the justice system processing, public safety, and public health outcomes of evolving laws and policies.

Secondary criminal justice and public health data were gathered from federal, state, and local sources. Each source has a sufficiently long time series to provide statistical power and to allow for sometimes gradual implementation. The design exploits geographic and temporal variation in the implementation of marijuana law, using a difference-in-differences design that compares outcomes in states which implemented the policies with states that did not, before and after implementation.

This study sought to examine the effects of cannabis legalization on crime and law enforcement in Washington State. In 2012 citizens voted to legalize possession of small amounts of cannabis, with the first licensed retail outlets opening on July 1, 2014. Researchers crafted their analysis around two questions. First, how are law enforcement agencies handling crime and offenders, particularly involving marijuana, before and after legalization? Second, what are the effects of marijuana legalization on crime, crime clearance, and other policing activities statewide, as well as in urban, rural, tribal, and border areas?

Research participants and crime data were collected from 14 police organizations across Washington, as well as Idaho police organizations situated by the Washington-Idaho border where marijuana possession is illegal. Additional subjects were recruited from other police agencies across Washington, prosecutors, and officials from the Washington State Department of Fish and Wildlife, Washington State Liquor and Cannabis Board, and the National Association of State Boating Law Administrators for focus groups and individual interviews. Variables included dates of calls for service from 2004 through 2018, circumstances surrounding calls for service, geographic beats, agency, whether calls were dispatch or officer initiated, and whether the agency was in a jurisdiction with legal cannabis.

This study evaluated CeaseFire, a program of the Chicago Project for Violence Prevention. The evaluation had both outcome and process components.

The outcome evaluation assessed the program's impact on shootings and killings in selected CeaseFire sites. Two types of crime data were compiled by the research team: Time Series Data (Dataset 1) and Shooting Incident Data (Dataset 2). Dataset 1 is comprised of aggregate month/year data on all shooting, gun murder, and persons shot incidents reported to Chicago police for CeaseFire's target beats and matched sets of comparison beats between January 1991 and December 2006, resulting in 1,332 observations. Dataset 2 consists of data on 4,828 shootings that were reported in CeaseFire's targeted police beats and in a matched set of comparison beats for two-year periods before and after the implementation of the program (February 1998 to April 2006).

The process evaluation involved assessing the program's operations and effectiveness. Researchers surveyed three groups of CeaseFire program stakeholders: employees, representatives of collaborating organizations, and clients.

The three sets of employee survey data examine such topics as their level of involvement with clients and CeaseFire activities, their assessments of their clients' problems, and their satisfaction with training and management practices. A total of 154 employees were surveyed: 23 outreach supervisors (Dataset 3), 78 outreach workers (Dataset 4), and 53 violence interrupters (Dataset 5).

The six sets of collaborating organization representatives data examine such topics as their level of familiarity and contact with the CeaseFire program, their opinions of CeaseFire clients, and their assessments of the costs and benefits of being involved with CeaseFire. A total of 230 representatives were surveyed: 20 business representatives (Dataset 6), 45 clergy representatives (Dataset 7), 26 community representatives (Dataset 8), 35 police representatives (Dataset 9), 36 school representatives (Dataset 10), and 68 service organization representatives (Dataset 11).

The Client Survey Data (Dataset 12) examine such topics as clients' involvement in the CeaseFire program, their satisfaction with aspects of life, and their opinions regarding the role of guns in neighborhood life. A total of 297 clients were interviewed.

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 California correctional system underwent a dramatic transformation under California's Public Safety Realignment Act (AB 109) in 2011, a law that shifted from the state to the counties the responsibility for monitoring, tracking, and incarcerating lower level offenders previously bound for state prison. Realignment, therefore, presents the opportunity to witness 58 natural experiments in the downsizing of prisons. Counties faced different types of offenders, implemented different programs in different community and jail environments, and adopted differing sanctioning policies. This study examines the California's Public Safety Realignment Act's effect on counties' criminal justice institutions, including the disparities that result in charging, sentencing, and resource decisions.

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 project explored the use and impact of injury evidence and biological evidence through a study of the role of these forms of evidence in prosecuting sexual assault in an urban district attorney's office in a metropolitan area in the eastern United States. The research questions addressed in this summary overview were as follows:

• How frequent were different forms of injury evidence and biological evidence in the sample?
• Is the presence of injury evidence and biological evidence correlated with the presence of other forms of evidence?
• Which types of cases and case circumstances are more likely to yield injury evidence and biological evidence?
• Do the presence of injury evidence and biological evidence predict criminal justice outcomes, taking into account the effects of other predictors?
• In what ways do prosecutors use injury evidence and biological evidence and what is their appraisal of their impact on case outcomes?

The collection contains 1 SPSS data file, DataArchiveFile_InjuryEvidenceForensicEvidenceandthe ProsecutionofSexualAssault4-7-17.sav (n=257; 417 variables).

The qualitative data files were excluded from deposit with ICPSR and are not available as part of this data collection at this time.

The Bureau of Justice Statistics (BJS) entered into a cooperative agreement with Westat to develop and evaluate a lower-cost, subnational companion survey of victimization as one piece of the subnational estimates program. The Local-Area Crime Survey (LACS) was fielded in 2015 and 2016 and is intended for use by states, municipalities, or other jurisdictions and entities to assess levels and trends in public safety. The LACS is modeled in part after the National Crime Victimization Survey (NCVS), conducted for BJS by the U.S. Census Bureau. One of the two major statistical programs on crime produced by the U.S. Department of Justice, the NCVS is the nation's primary source of information about criminal victimization, whether reported or not reported to police. The core NCVS methodology includes a mix of in-person and telephone interviews with household members age 12 and older selected from an area probability sample to produce reliable national-level estimates. As another part of the subnational estimates program, BJS worked with the Census Bureau to enhance and reallocate the NCVS sample to support subnational estimates for the 22 most populous states and potentially substate areas within those states. For the most part, this direct estimation component of the program will not support estimates at the local level. See the NCVS Subnational Estimates webpage on the BJS website for more information.

The goals of this research were to (1) develop and test a relatively inexpensive survey design (2) that could be administered by local jurisdictions or their vendors (3) to produce cross-jurisdiction estimates and estimates of change over time within jurisdictions that may be compared with similar estimates using NCVS data. In addition to questions about victimization experiences, the LACS included questions about perceptions of community safety and police efficacy. The rationale for including these items was that they were relevant to all households, not just victims. The hope was that these items would increase survey response rates as non-victims would have important questions to answer. The LACS served as a platform for assessing the value of these questions for the planned NCVS instrument redesign. For more information, see the NCVS Instrument Redesign webpage on the BJS website.

This study was an evaluation of multiple imputation strategies to address missing data using the New Approach to Evaluating Supplementary Homicide Report (SHR) Data Imputation, 1990-1995 (ICPSR 20060) dataset.

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations.

This dataset represents the concatenated version of the NCVS on a collection year basis for 1992-2013. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred.

For additional information, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 35164.

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations.

This dataset represents the concatenated version of the NCVS on a collection year basis for 1992-2014. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred.

For additional information, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 36142.

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations.

This dataset represents the concatenated version of the NCVS on a collection year basis for 1992-2015. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred.

For additional information, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 36142.