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Curated

Automated Reporting System Pilot Project in Los Angeles, 1990 (ICPSR 9969)

Released/updated on: 2006-01-12
Geographic coverage: United States, Los Angeles
Time period: 1990-04-01--1990-12-01
The purpose of this pilot project was to determine if preliminary investigation report (PIR) data filed by patrol officers could be collected via laptop computers to allow the direct input of the data into the Los Angeles Police Department Crime and Arrest Database without adversely affecting the personnel taking or using the reports. This data collection addresses the following questions: (1) Did officers and supervisors prefer the automated reporting system (ARS) or the handwritten version of the PIR? (2) Did the ARS affect the job satisfaction or morale of officers and supervisors? (3) Did the ARS reduce the amount of time that patrol officers, supervisors, and clerks spent on paperwork? (4) Did the ARS affect the accuracy of information contained in the PIRs? (5) Did detectives and prosecuting attorneys find the ARS a more reliable source than handwritten PIRs? Officers and supervisors in two divisions of the Los Angeles Police Department, Wilshire and Hollywood, participated as control and experimental groups. The control group continued using handwritten ("existing") PIRs while the experimental group used the automated PIRs (ARS). The General Information Questionnaire collected information on each officer's rank, assignment, watch, gender, age, years with the Los Angeles Police Department, education, job morale, job demands, self-esteem, computer anxiety, and relationship with supervisor and other officers. The Job Performance Rating Form gathered data on work efforts, depth of job knowledge, work quality, oral and written skills, and capacity to learn. The Time Study Sheets collected data on investigation time, writing and editing time, travel time, approval and correction time, review time, errors by type, and data input time for both the handwritten and automated forms. The Evaluation of the Existing Form and the Evaluation of the Automated Form both queried respondents on ease of use, system satisfaction, and productivity loss. The ARS Use Questionnaire asked about ease of use, typing skills, computer skills, comfort with the system, satisfaction with training, and preference for the system. The Hollywood Detective Division ARS Use Questionnaire surveyed detectives on the system's ease of use, task improvement, support for continued use, and preference for the system. The PIR Content Evaluation Form collected data on quality of officers' observations, organization and writing skills, physical evidence, statements of victims, witnesses, and suspects, and offense classification. The Caplan Role Conflict and Role Ambiguity subscales were used in the design of the questionnaires.
Curated

CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations (Version 3.3), United States, 2010 (ICPSR 2824)

Released/updated on: 2023-03-30
Geographic coverage: United States

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:

Data Setup
  • 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 Description
  • 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.
Spatial Modeling
  • 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

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.
Options
  • 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.

CrimeStat Libraries

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.

Curated

CrimeStat III User Workbook and Data (ICPSR 23622)

Released/updated on: 2008-10-16
The Mapping and Analysis for Public Safety (MAPS) Program in conjunction with the National Law Enforcement, Corrections and Technology Center - Southeast (NLECTC-SE) in Charleston, South Carolina, announce the free download of a CrimeStat workbook designed specifically for crime analysts in the use of CrimeStat III. The data used in the workbook are also provided. Further, a PowerPoint file covering the workbook and all lessons is provided for download for those wanting to instruct a class. CrimeStat III is a Windows-based spatial statistics software package used for analyzing crime data from law enforcement and criminal justice agencies. Output produced from the software can be used with a geographic information system (GIS) to support and enhance the tactical and strategic analysis efforts of police departments. The workbook covers how to prepare data for CrimeStat, produce results and import them into ArcGIS 9.x for further analysis or presentation. It also covers entering data into CrimeStat III, basic descriptive statistics from Spatial Distribution, measures of clustering in Distance Analysis, several 'Hot Spot' techniques, and using both single and dual Kernel Density Interpolation. Upon completion of the workbook and exercises, users are able to immediately make use of CrimeStat at their own agencies in the analysis of crime patterns and trends.
Curated

National Archive of Criminal Justice Data (NACJD) Web Site (ICPSR 152)

Released/updated on: 2006-03-31
The National Archive of Criminal Justice Data (NACJD) preserves and distributes computerized crime and justice data from Federal agencies, state agencies, and investigator initiated research projects to users for secondary statistical analysis. Founded in 1978 as part of the Inter-university Consortium for Political and Social Research (ICPSR), NACJD is supported by the Bureau of Justice Statistics (BJS) and the National Institute of Justice (NIJ) in the United States Department of Justice. Twelve subject areas define the annual NACJD catalog: (1) attitude surveys, (2) community studies, (3) corrections, (4) court case processing, (5) courts, (6) criminal justice system, (7) crime and delinquency, (8) official statistics, (9) police, (10) victimization, (11) drugs, alcohol, and crime, and (12) computer programs and instructional packages. Among the many tools available at the NACJD Web site are online resource guides that provide detailed information about complex or frequently accessed data collections. The Web site also features an online data analysis system (DAS) that allows users to conduct analyses on selected datasets.
Curated

Prosecutor's Management and Information System (PROMIS), New Orleans, 1979 (ICPSR 8219)

Released/updated on: 1992-02-16
Geographic coverage: United States, Louisiana, New Orleans
The Prosecutor's Management and Information System (PROMIS) is a computer-based management information system for public prosecution agencies. PROMIS was initially developed with funds from the United States Law Enforcement Assistance Administration (LEAA) to cope with the problems of a large, urban prosecution agency where mass production operations had superseded the traditional practice of a single attorney preparing and prosecuting a given case from inception to final disposition. The combination of massive volumes of cases and the assembly-line fragmentation of responsibility and control had created a situation in which one case was indistinguishable from another and the effects of problems at various stages in the assembly line on ultimate case disposition went undetected and uncorrected. One unique feature of PROMIS that addresses these problems is the automated evaluation of cases. Through the application of a uniform set of criteria, PROMIS assigns two numerical ratings to each case: one signifying the gravity of the crime through a measurement of the amount of harm done to society, and the other signifying the gravity of the prior criminal record of the accused. These ratings make it possible to select the more important cases for intensive, pre-trial preparation and to assure even-handed treatment of cases of like gravity. A complementary feature of PROMIS is the automation of reasons for decisions made or actions taken along the assembly line. Reasons for dismissing cases prior to trial on their merits can be related to earlier cycles of postponements for various reasons and to the reasoning behind intake and screening decisions. The PROMIS data include information about the defendant, case characteristics and processes, charge, sentencing and continuance processes, and the witnesses/victims involved with a case. PROMIS was first used in 1971 in the United States Attorney's Office for the District of Columbia. To enhance the ability to transfer the PROMIS concepts and software to other communities, LEAA awarded a grant to the Institute for Law and Social Research (INSLAW) in Washington, DC. The New Orleans PROMIS data collection is a product of this grant.
Curated

Prosecutor's Management Information System (PROMIS), Rhode Island, 1979 (ICPSR 8288)

Released/updated on: 1992-02-16
Geographic coverage: Rhode Island, United States
The Prosecutor's Management Information System (PROMIS) is a computer-based information system for public prosecution agencies. PROMIS was initially developed with funds from the United States Law Enforcement Assistance Administration (LEAA) to cope with problems of a large, urban prosecution agency where mass production operations had superseded the traditional practice of a single attorney preparing and prosecuting a given case from inception to final disposition. The combination of massive volumes of cases and assembly-line fragmentation of responsibility and control had created a situation in which one case was indistinguishable from another and the effects of problems at various stages in the assembly line on ultimate case disposition went undetected and uncorrected. One unique feature of PROMIS that addresses these problems is the automated evaluation of cases. Through the application of a uniform set of criteria, PROMIS assigns two numerical ratings to each case: one signifying the gravity of the crimes through the measurement of the amount of harm done to society, and the other signifying the gravity of the prior record of the accused. These ratings make it possible to select the more important cases for intensive, pre-trial preparation and to assure even-handed treatment of cases with similar degrees of gravity. A complementary feature of PROMIS is the automation of reasons for decisions made or actions taken along the assembly line. Reasons for dismissing cases prior to trial on their merits can be related to earlier cycles of postponement for various reasons and the reasoning behind intake and screening decisions. The PROMIS data include information about the defendant, case characteristics and processes, charge, sentencing and continuance processes, and the witnesses/victims involved in the case. PROMIS was first used in 1971 in the United States Attorney's Office for the District of Columbia. To enhance the ability to transfer the concepts and software to other communities, LEAA awarded a grant to the Institute for Law xand Social Research (INSLAW) in Washington, DC. The Rhode Island PROMIS data collection is a product of this grant.
Curated

Prosecutor's Management Information System (PROMIS), St. Louis, 1979 (ICPSR 8225)

Released/updated on: 1992-02-16
Geographic coverage: United States, Missouri, St. Louis
The Prosecutor's Management Information System (PROMIS) is a computer-based information system for public prosecution agencies. PROMIS was initially developed with funds from the United States Law Enforcement Assistance Administration (LEAA) to cope with problems of a large, urban prosecution agency where mass production operations had superceded the traditional practice of a single attorney preparing and prosecuting a given case from inception to final disposition. The combination of massive volumes of cases and assembly-line fragmentation of responsibility and control had created a situation in which one case was indistinguishable from another and the effects of problems at various stages in the assembly line on ultimate case disposition went undetected and uncorrected. One unique feature of PROMIS that addresses these problems is the automated evaluation of cases. Through the application of a uniform set of criteria, PROMIS assigns two numerical ratings to each case: one signifying the gravity of the crimes through the measurement of the amount of harm done to society, and the other signifying the gravity of the prior record of the accused. These ratings make it possible to select the more important cases for intensive, pre-trial preparation and to assure even-handed treatment of cases with similar degrees of gravity. A complementary feature of PROMIS is the automation of reasons for decisions made or actions taken along the assembly line. Reasons for dismissing cases prior to trial on their merits can be related to earlier cycles of postponement for various reasons and the reasoning behind intake and screening decisions. The PROMIS data include information about the defendant, case characteristics and processes, charge, sentencing and continuance processes, and the witness/victims involved in the case. PROMIS was first used in 1971 in the United States Attorney's Office for the District of Columbia. To enhance the ability to transfer the concepts and software to other communities, LEAA awarded a grant to the Institute for Law and Social Research (INSLAW) in Washington, DC. The St. Louis PROMIS data collection is a product of this grant.
Curated

School Crime Operations Package (School COP Software) (ICPSR 23543)

Released/updated on: 2008-10-09
Geographic coverage: United States

The School Crime Operations Package (School COP) is a software application developed by Abt Associates Inc. with funding from the National Institute of Justice. School COP is a free software package that persons responsible for school safety can use to enter, analyze, and map criminal incidents and school rule violations that occur in and around K-12 schools. School COP organizes information according to the data model that the United States Department of Education's National Center for Education Statistics' Crime, Violence, and Discipline Reporting Task Force recommends. The School COP database includes data related to the incident (e.g., date, time, type, location) and to persons involved in the incident (e.g., name, grade, action taken). In other words, School COP is an incident-based system, rather than a student-based system. School COP offers a variety of techniques for analyzing school incidents, including tabular reports, bar graphs, pie charts, and maps. School COP can be installed on any Windows (95 or later) PC. It requires no other software to run, and is usable without formal training.

The origin of this project is an award to Abt Associates Inc. that was funded under the National Institute of Justice's (NIJ) June 1999 "Safe Schools Technology" solicitation, which requested proposals for innovative approaches to using technology to enhance the safety of our nation's elementary and secondary schools. School COP was initially released on CD-ROM in January 2001, and made available at the School COP Web site in June 2001. This Windows version of School COP was generally designed for individuals, for a single school, or for small offices within a school district. Abt Associates Inc. was subsequently awarded another grant in 2001 to enhance the School Crime Operations Package (School COP) and to conduct an evaluation of this software, which is used to enter and analyze incidents that occur on school campuses.

Two types of enhancements were made. First, an enhanced Windows version of School COP was developed that could run on a local- or wide-area network, thus allowing multiple users within a single school or across multiple schools to share a common School COP database. The enhanced Windows version also included two utilities: a Merge application (which enables a district-level School COP database to be constructed by merging several individual databases) and a Viewer application (which enables users to view -- but not add, edit, or delete -- incident information). Second, Web School COP was developed to meet the diverse information needs of persons charged with maintaining safe schools in large school districts, including persons at the school-level (e.g., principals, assistant principals, security officers, and school resource officers), the district-level (e.g., district-level administrators and security staff), as well as possibly parent organizations and state-level administrators. Web School COP was designed to run on either an Intranet (e.g., the school district's private Internet) or a secure third-party Web server, and was built to run on the current Microsoft Web platform.

The evaluation of School COP entailed case studies of six sites to address three main issues: (1) what decision process do sites go through when deciding whether to use School COP, (2) once the site decides to use School COP, what implementation obstacles exist, including those related to installation, customization, and training, and (3) what benefits do sites realize from using School COP.

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