Search results

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study contains data from a project by the New York City Police Department (NYPD) involving GIS data on environmental risk factors that correlate with criminal behavior. The general goal of this project was to test whether risk terrain modeling (RTM) could accurately and effectively predict different crime types occurring across New York City. The ultimate aim was to build an enforcement prediction model to test strategies for effectiveness before deploying resources. Three separate phases were completed to assess the effectiveness and applicability of RTM to New York City and the NYPD. A total of four boroughs (Manhattan, Brooklyn, the Bronx, Queens), four patrol boroughs (Brooklyn North, Brooklyn South, Queens North, Queens South), and four precincts (24th, 44th, 73rd, 110th) were examined in 6-month time periods between 2014 and 2015. Across each time period, a total of three different crime types were analyzed: street robberies, felony assaults, and shootings.

The study includes three shapefiles relating to New York City Boundaries, four shapefiles relating to criminal offenses, and 40 shapefiles relating to risk factors.

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 used a place-based method of evaluation and spatial units of analysis to measure the extent to which allocating police patrols to high-risk areas effected the frequency and spatial distribution of new crime events in 5 U.S. cities. High-risk areas were defined using risk terrain modeling methods. Risk terrain modeling, or RTM, is a geospatial method of operationalizing the spatial influence of risk factors to common geographic units.

The collection contains 333 shape files, 8 SPSS files, and 9 Excel files. The shape files include both city level risk factor locations and crime data from police departments. SPSS and Excel files contain output from GIS data used for analysis.

This study contains data from the Northwestern Juvenile Project (NJP) series, a prospective longitudinal study of the mental health needs and outcomes of youth in detention.

This study examined the following goals: (1) firearm involvement (access, ownership, and use) during adolescence and young adulthood; (2) perpetration of firearm violence over time; and (3) patterns of firearm victimization (injury and mortality) over time. This study addressed the association between early involvement with firearms and firearm-firearm perpetration and victimization in adulthood.

The original sample included 1,829 randomly selected youth, 1,172 males and 657 females, then 10 to 18 years old, enrolled in the study as they entered the Cook County Juvenile Temporary Detention Center from 1995 to 1998. Among the sample were 1,005 African Americans, 524 Hispanics, and 296 non-Hispanic white respondents. Participants were tracked from the time they left detention. Re-interviews were conducted regardless of where respondents were living when their follow-up interview was due: in the community, correctional settings, or by telephone if they lived farther than two hours from Chicago.

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.

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.

In 2015, the National Institute of Justice funded the Urban Institute's Evaluation of Gunshot Detection Technology to Aid in the Reduction of Firearms Violence. This project was designed to investigate the degree to which gunshot detection technology (GDT) aids in the response, investigation, and prevention of firearms violence and related crimes. The goal of this study was to conduct a rigorous process and impact evaluation of GDT to inform policing researchers and practitioners about the impact GDT may have. To achieve this goal, the research team implemented a mixed-methods research design with police departments in Denver, Colorado; Milwaukee, Wisconsin; and Richmond, California.

Quantitative data collection included administrative data on calls for service (CFS), crime, and GDT alerts, as well as comprehensive case file reviews of 174 crimes involving a firearm. Quantitative analyses examined the impact of GDT by (1) comparing counts of gunshot notifications for GDT alerts to shooting-related CFS, (2) comparing response times of GDT alerts to shooting-related CFS, (3) examining the impact GDT has had on CFS and crimes, and (4) conducting a cost-benefit analysis of the GDT. Qualitative data collection included 46 interviews with criminal justice stakeholders to learn implementation processes and challenges associated with its GDT, and 6 focus groups with 49 community members to learn how residents feel about policing efforts to reduce firearm violence and its use of GDT.

Three types of files were uploaded for each site. They include quantitative data on crimes and CFS (DS1-DS3), gunshot notifications (DS4-DS6), and response times (DS7-DS9). The qualitative data are not currently available as part of this collection.

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 investigated the feasibility of implementing the Cardiff Model. The Cardiff Model is a unique violence surveillance system and intervention that involves data sharing and violence prevention planning between law enforcement and the medical field. Anonymized data on assaults from emergency and police departments (EDs; PDs) are combined to detail assault incidents and "hotspots." Data are discussed by a multidisciplinary consortium, which develops and implements a data-informed violence prevention action plan that includes behavioral, environmental, and policy changes to impact violence. Model actions led to decreases in injurious assaults and this model is now statutory in the United Kingdom.

The Cardiff Model has never been translated to the U.S. and would require an investigation within our health care system and in different geographical and population contexts. This study investigated the feasibility of essential Cardiff Model Components in order to refine study procedures and situate this community to request further funds for full model implementation.

As part of this study, researchers collected a number of feasibility measures from ED and study staff to evaluate the feasibility of translating included model components. Geospatial and statistical analyses investigated the added benefit of the combined ED, PD and Emergency Medical Services (EMS) data.

The study contains 1 SPSS data files (CHW Data_1.1.15 to 7.31.16.sav (n=748; 14 variables)), 1 STATA data file (nurse survey data.dta (n=43; 26 variables)), a text document (Nurse Survey_Qualitative data.txt), and 1 excel file (CHW Incidents_Block level data only.xlsx).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study was designed to investigate whether the presence of CCTV cameras can reduce crime by studying the cameras and crime statistics of a controlled area. The viewsheds of over 100 CCTV cameras within the city of Philadelphia, Pennsylvania were defined and grouped into 13 clusters, and camera locations were digitally mapped. Crime data from 2003-2013 was collected from areas that were visible to the selected cameras, as well as data from control and displacement areas using an incident reporting database that records the location of crime events. Demographic information was also collected from the mapped areas, such as population density, household information, and data on the specific camera(s) in the area. This study also investigated the perception of CCTV cameras, and interviewed members of the public regarding topics such as what they thought the camera could see, who was watching the camera feed, and if they were concerned about being filmed.

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.

This study examines municipal crime levels and changes over a nine year time frame, from 2000-2008, in the fifth largest primary Metropolitan Statistical Area (MSA) in the United States, the Philadelphia metropolitan region. Crime levels and crime changes are linked to demographic features of jurisdictions, policing arrangements and coverage levels, and street and public transit network features.

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.

This study was a systematic assessment of the impacts of foreclosures and crime levels on each other, using sophisticated spatial analysis methods, informed by qualitative research on the topic. Using data on foreclosures and crime in District of Columbia and Miami-Dade County, Florida from 2003 to 2011, this study considered the effects of the two phenomena on each other through a dynamic systems approach.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study contains Uniform Crime Report geocoded data obtained from St. Petersburg Police Department, Orlando Police Department, and Miami-Dade Police Department for the years between 2010 and 2014. The three primary goals of this study were:

1. to determine whether Florida law HB 7095 (signed into law on June 3, 2011) and related legislation reduced the number of pain clinics abusively dispensing opioid prescriptions in the State
2. to examine the spatial overlap between pain clinic locations and crime incidents
3. to assess the logistics of administering the law

The study includes:

• 3 Excel files: MDPD_Data.xlsx (336,672 cases; 6 variables), OPD_Data.xlsx (160,947 cases; 11 variables), SPPD_Data.xlsx (211,544 cases; 14 variables)
• 15 GIS Shape files (95 files total)

Data related to respondents' qualitative interviews and the Florida Department of Health are not available as part of this collection. For access to data from the Florida Department of Health, interested researchers should apply directory to the FDOH.

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 data collection represents an experimental micro-level geospatial crime prevention strategy that attempted to interrupt the near repeat (NR) pattern in residential burglary by creating a NR space-time high risk zone around residential burglaries as they occurred and then using uniformed volunteers to notify residents of their increased risk and provide burglary prevention tips. The research used a randomized controlled trial to test whether high risk zones that received the notification had fewer subsequent burglaries than those that did not. In addition, two surveys were administered to gauge the impact of the program, one of residents of the treatment areas and one of treatment providers.

The collection contains 6 Stata datasets:

1. BCo_FinalData_20180118_Archiving.dta(n = 484, 8 variables)
2. Red_FinalData_20180117_Archiving.dta (n = 268, 8 variables)
3. BCo_FinalDatasetOtherCrime_ForArchiving_v2.dta(n = 484, 8 variables)
4. Redlands_FinalDataSecondary_ForArchiving_v2.dta (n = 266, 8 variables)
5. ResidentSurvey_AllResponses_V1.4_ArchiveCleaned.dta (n = 457, 42 variables)
6. VolunteerSurvey_V1.2_ArchiveCleaned.dta (n = 38, 16 variables)

1. BaltimoreCounty_Bnd.zip
2. BC_NR_HRZs.zip
3. BurglaryAreaMinus800_NoApts.zip
4. Redlands_CityBnd.zip
5. RedlandsNR_HRZs.shp.zip

The Philadelphia Predictive Policing Experiment was a place-based, randomized control trial to study the impact of different patrol strategies on violent and property crime in predicted crime areas. The experiment's goal was to learn whether different operationally-realistic police responses to crime forecasts, estimated by a predictive policing software program, would reduce crime. Specifically, the study tested whether greater awareness among general duties patrol officers of the predicted crime areas would be sufficient to deter crime, whether a dedicated uniform patrol attendance in predictive areas would increase visible police presence sufficiently in the local area to deter crime, or if dedicated plain-clothes units performing surveillance and unmarked patrol would increase interdiction and offender incapacitation sufficiently to reduce crime. With support of the Philadelphia Police Department, the study took place over two, three-month periods between 2015 and 2016.

In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes.

The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.

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.

Researchers compiled datasets on prison admissions and releases that would be comparable across places and geocoded and mapped those data onto crime rates across those same places. The data used were panel data. The data were quarterly or annual data, depending on the location, from a mix of urban (Boston, Newark and Trenton) and rural communities in New Jersey covering various years between 2000 and 2010.

The crime, release, and admission data were individual level data that were then aggregated from the individual incident level to the census tract level by quarter (in Boston and Newark) or year (in Trenton). The analyses centered on the effects of rates of prison removals and returns on rates of crime in communities (defined as census tracts) in the cities of Boston, Massachusetts, Newark, New Jersey, and Trenton, New Jersey, and across rural municipalities in New Jersey.

There are 4 Stata data files. The Boston data file has 6,862 cases, and 44 variables. The Newark data file has 1,440 cases, and 45 variables. The Trenton data file has 66 cases, and 32 variables. The New Jersey Rural data file has 1,170 cases, and 32 variables.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study explores the victim level, perpetrator level and community level variables associated with Adult Protective Services Substantiated Financial Exploitation in Older Adults. The aims of the study were to identify factors that differentiate financial exploitation from other forms of elder abuse as well as differentiate pure financial exploitation from hybrid financial exploitation.

This project is concerned with understanding the determinants of racial bias in police traffic stops and in the city of Syracuse, New York. Using an officer-level panel of data on vehicle stops and vehicle searches by 512 officers from 2006 to 2009, the primary goal of this research is to better understand the effects of officer experience on their proclivities for racial bias in traffic stops, while controlling for officer, citizen, and neighborhood demographics.

Included in these data are variables for census tracts as well as their racial and ethnic makeup, times and dates when traffic stops occurred, sunrise and sunset data for the City of Syracuse, and the racial and ethnic makeup of citizens involved in stops.

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 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.

This project aimed to develop new insights into offender decision-making in hot spots in New York City, and to test whether these insights could inform interventions to reduce crime in hot spots. There were two phases to the project. In the first phase a set of hypotheses were developed about offender decision-making based on semi-structured interviews with individuals who were currently incarcerated, formerly incarcerated individuals, individuals currently on probation, and community members of high crime areas with no justice-involvement. These interviews suggested several factors worthy of further testing. For instance, offenders believed they were less likely to get away with a crime if they knew more about the officers in their community. That is, when police officers were less anonymous, offenders were less likely to go forward with a crime.

In the second phase a field intervention was developed and conducted to test whether reducing officer anonymity might deter crime. Through a randomized controlled trial (RCT) while working with NYPD neighborhood coordination officers, who work in New York City Housing Authority (NYCHA) developments, it was tested whether sending information about officers to residents in housing developments would deter crime in those developments.

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).

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.