This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

This study was designed to develop crime forecasting as an application area for police in support of tactical deployment of resources. Data on crime offense reports and computer aided dispatch (CAD) drug calls and shots fired calls were collected from the Pittsburgh, Pennsylvania Bureau of Police for the years 1990 through 2001. Data on crime offense reports were collected from the Rochester, New York Police Department from January 1991 through December 2001. The Rochester CAD drug calls and shots fired calls were collected from January 1993 through May 2001. A total of 1,643,828 records (769,293 crime offense and 874,535 CAD) were collected from Pittsburgh, while 538,893 records (530,050 crime offense and 8,843 CAD) were collected from Rochester. ArcView 3.3 and GDT Dynamap 2000 Street centerline maps were used to address match the data, with some of the Pittsburgh data being cleaned to fix obvious errors and increase address match percentages. A SAS program was used to eliminate duplicate CAD calls based on time and location of the calls. For the 1990 through 1999 Pittsburgh crime offense data, the address match rate was 91 percent. The match rate for the 2000 through 2001 Pittsburgh crime offense data was 72 percent. The Pittsburgh CAD data address match rate for 1990 through 1999 was 85 percent, while for 2000 through 2001 the match rate was 100 percent because the new CAD system supplied incident coordinates. The address match rates for the Rochester crime offenses data was 96 percent, and 95 percent for the CAD data. Spatial overlay in ArcView was used to add geographic area identifiers for each data point: precinct, car beat, car beat plus, and 1990 Census tract. The crimes included for both Pittsburgh and Rochester were aggravated assault, arson, burglary, criminal mischief, misconduct, family violence, gambling, larceny, liquor law violations, motor vehicle theft, murder/manslaughter, prostitution, public drunkenness, rape, robbery, simple assaults, trespassing, vandalism, weapons, CAD drugs, and CAD shots fired.

Comprehensive dataset of 52 Police stations in Pennsylvania, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 23 Police academies in Pennsylvania, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Allegheny County 911 center answers and dispatches 911 calls for 111 out of 130 municipalities in Allegheny County. Agencies are dispatched via a computer aided dispatch (CAD) system. This dataset contains dispatched EMS and Fire events from the CAD and includes details about the nature of the emergency.

To protect the privacy of callers and prevent sensitive health or other identifying information being revealed, the following steps were taken:

• Aggregated event location to census block groups.
• Aggregated call date/time to quarter and year.
• Shortened call types to remove potentially identifying information.
• Flagged certain call types as containing sensitive health information, such as mental health issues, overdose, etc.
• Checked frequency of calls with sensitive health information:
  • If the number of calls (in a particular category related to sensitive health information) for a certain quarter/year and census block group was greater than or equal to 5, the call description was included.
  • If less than 5, the call description was redacted.

Events requiring EMS and Fire services will appear in both datasets with a different Call ID. Events requiring two agencies of the same service (e.g. two or more different fire companies responded to a major fire) will only list the primary responder.

The call descriptions are based on information provided by the caller. The calls are not later updated with a disposition or correction if the original description was inaccurate. For example, if EMS is dispatched to the scene of a stroke, but the person actually had a heart attack, that record would not be updated later with the correct description.

A small subset of the CAD data had no call type recorded. These records are preserved with a "null" in the Description_Short field. Redacted call types are listed as "Removed".

The 19 municipalities that dispatch their own EMS, Fire, and/or Police services are called "ringdown municipalities". These are subject to change. The list can be found in the Ringdown Municipalities 2019 resource.

Due to the size of these tables, you may experience 504 Gateway Timeout errors when trying to download the first two resources below. Use the following links instead.

To download the 911 EMS Dispatches table, click on this link: https://tools.wprdc.org/downstream/ff33ca18-2e0c-4cb5-bdcd-60a5dc3c0418

To download the 911 Fire Dispatches table, click on this link: https://tools.wprdc.org/downstream/b6340d98-69a0-4965-a9b4-3480cea1182b

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

The dashboard displays aggregated State Police incident data. It contains no personally identifiable information. Users can refine the data with custom date ranges, locations, and categories.
The CAID currently contains crash and enforcement data for both commercial and non-commercial vehicles, including Incident maps. Data regarding various crimes (including violent crimes where a firearm was involved in the commission) is now on the dashboard, with mapping for counties and municipalities. A map of PSP's coverage area throughout the commonwealth is also available.

PSP is interested in your feedback. Use the Contact Us button located at the top of the dashboard to:
• Submit suggestions for dashboard enhancements
• Submit ideas on how we can provide better service and improve the quality of life in your neighborhood (such as sobriety checkpoints, radar details, community presentations, etc.)

Feedback is reviewed by the CAID Development Team and the Office of Community Engagement. At all times, PSP welcomes your comments on how we are meeting our Core Values of Honor, Service, Integrity, Respect, Trust, Courage, and Duty.

This study examined spatial and temporal features of crime guns in Pittsburgh, Pennsylvania, in order to ascertain how gun availability affected criminal behavior among youth, whether the effects differed between young adults and juveniles, and whether that relationship changed over time. Rather than investigating the general prevalence of guns, this study focused only on those firearms used in the commission of crimes. Crime guns were defined specifically as those used in murders, assaults, robberies, weapons offenses, and drug offenses. The emphasis of the project was on the attributes of crime guns and those who possess them, the geographic sources of those guns, the distribution of crime guns over neighborhoods in a city, and the relationship between the prevalence of crime guns and the incidence of homicide. Data for Part 1, Traced Guns Data, came from the City of Pittsburgh Bureau of Police. Gun trace data provided a detailed view of crime guns recovered by police during a two-year period, from 1995 to 1997. These data identified the original source of each crime gun (first sale to a non-FFL, i.e., a person not holding a Federal Firearms License) as well as attributes of the gun and the person possessing the gun at the time of the precipitating crime, and the ZIP-code location where the gun was recovered. For Part 2, Crime Laboratory Data, data were gathered from the local county crime laboratory on guns submitted by Pittsburgh police for forensic testing. These data were from 1993 to 1998 and provided a longer time series for examining changes in crime guns over time than the data in Part 1. In Parts 3 and 4, Stolen Guns by ZIP-Code Data and Stolen Guns by Census Tract Data, data on stolen guns came from the local police. These data included the attributes of the guns and residential neighborhoods of owners. Part 3 contains data from 1987 to 1996 organized by ZIP code, whereas Part 4 contains data from 1993 to 1996 organized by census tract. Part 5, Shots Fired Data, contains the final indicator of crime gun prevalence for this study, which was 911 calls of incidents involving shots fired. These data provided vital information on both the geographic location and timing of these incidents. Shots-fired incidents not only captured varying levels of access to crime guns, but also variations in the willingness to actually use crime guns in a criminal manner. Part 6, Homicide Data, contains homicide data for the city of Pittsburgh from 1990 to 1995. These data were used to examine the relationship between varying levels of crime gun prevalence and levels of homicide, especially youth homicide, in the same city. Part 7, Pilot Mapping Application, is a pilot application illustrating the potential uses of mapping tools in police investigations of crime guns traced back to original point of sale. NTC. It consists of two ArcView 3.1 project files and 90 supporting data and mapping files. Variables in Part 1 include date of manufacture and sale of the crime gun, weapon type, gun model, caliber, firing mechanism, dealer location (ZIP code and state), recovery date and location (ZIP code and state), age and state of residence of purchaser and possessor, and possessor role. Part 2 also contains gun type and model, as well as gun make, precipitating offense, police zone submitting the gun, and year the gun was submitted to the crime lab. Variables in Parts 3 and 4 include month and year the gun was stolen, gun type, make, and caliber, and owner residence. Residence locations are limited to owner ZIP code in Part 3, and 1990 Census tract number and neighborhood name in Part 4. Part 5 contains the date, time, census tract and police zone of 911 calls relating to shots fired. Part 6 contains the date and census tract of the homicide incident, drug involvement, gang involvement, weapon, and victim and offender ages. Data in Part 7 include state, county, and ZIP code of traced guns, population figures, and counts of crime guns recovered at various geographic locations (states, counties, and ZIP codes) where the traced guns first originated in sales by an FFL to a non-FFL individual. Data for individual guns are not provided in Part 7.

Comprehensive dataset of 45 Police supply stores in Pennsylvania, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This project was an 18-month long research-practitioner partnership to conduct a process evaluation of the State College Police Department's implementation of a grant to encourage arrest policies for domestic violence. The general goals of the process evaluation were to assess how and to what extent the State College Police Department's proposed activities were implemented as planned, based on the rationale that such activities would enhance the potential for increasing victim safety and perpetrator accountability systemically. As part of the grant, the police department sought to improve case tracking and services to victims by developing new specialized positions for domestic violence, including: (1) a domestic violence arrest coordinator from within the State College Police Department who was responsible for monitoring case outcomes through the courts and updating domestic violence policies and training (Part 1, Victim Tracking Data from Domestic Violence Coordinator), (2) a victims service attorney from Legal Services who was responsible for handling civil law issues for domestic violence victims, including support, child custody, employment, financial, consumer, public benefits, and housing issues (Part 2, Victim Tracking Data From Victim Services Attorney), and (3) an intensive domestic violence probation officer from the Centre County Probation and Parole Department who was responsible for providing close supervision and follow-up of batterers (Part 3, Offender Tracking Data). Researchers worked with practitioners to develop databases suitable for monitoring service provision by the three newly-created positions for domestic violence cases. Major categories of data collected on the victim tracking form (Parts 1 and 2) included location of initial contact, type of initial contact, referral source, reason for initial contact, service/consultation provided at initial contact, meetings, and referrals out. Types of services provided include reporting abuse, filing a Protection from Abuse order, legal representation, and assistance with court procedures. Major categories of data collected on the offender tracking form (Part 3) included location of initial contact, type of initial contact, referral source, reason for initial contact, service/consultation provided, charges, sentence received, relationship between the victim and perpetrator, marital status, children in the home, referrals out, presentencing investigation completed, prior criminal history, and reason for termination. Types of services provided include pre-sentence investigation, placement on supervision, and assessment and evaluation. In addition to developing these new positions, the police department also sought to improve how officers handled domestic violence cases through a two-day training program. The evaluation conducted pre- and post-training assessments of all personnel training in 1999 and conducted follow-up surveys to assess the long-term impact of training. For Part 4, Police Training Survey Data, surveys were administered to law enforcement personnel participating in a two-day domestic violence training program. Surveys were administered both before and after the training program and focused on knowledge about domestic violence policies and protocols, attitudes and beliefs about domestic violence, and the background and experience of the officers. Within six months after the training, the same participants were contacted to complete a follow-up survey. Variables in Part 4 measure how well officers knew domestic violence arrest policies, their attitudes toward abused women and how to handle domestic violence cases, and their opinions about training. Demographic variables in Part 4 include age, sex, race, education, and years in law enforcement.

Comprehensive dataset containing 16 verified State police businesses in Pennsylvania, United States with complete contact information, ratings, reviews, and location data.

The Pittsburgh Youth Study (PYS) is part of the larger "Program of Research on the Causes and Correlates of Delinquency" initiated by the Office of Juvenile Justice and Delinquency Prevention in 1986. PYS aims to document the development of antisocial and delinquent behavior from childhood to early adulthood, the risk factors that impinge on that development, and help seeking and service provision of boys' behavior problems. The study also focuses on boys' development of alcohol and drug use, and internalizing problems.

PYS consists of three samples of boys who were in the first, fourth, and seventh grades in Pittsburgh, Pennsylvania public schools during the 1987-1988 academic year (called the youngest, middle, and oldest sample, respectively). Using a screening risk score that measured each boy's antisocial behavior, boys identified at the top 30 percent within each grade sample on the screening risk measure (n=~250), as well as an equal number of boys randomly selected from the remainder (n=~250), were selected for follow-up. Consequently, the final sample for the study consisted of 1,517 total students selected for follow-up. 506 of these students were in the oldest sample, 508 were in the middle sample, and 503 were in the youngest sample.

Assessments were conducted semiannually and then annually using multiple informants (i.e., boys, parents, teachers) between 1987 and 2010. The youngest sample was assessed from ages 6-19 and again at ages 25 and 28. The middle sample was assessed from ages 9-13 and again at age 23. The oldest sample was assessed from ages 13-25, with an additional assessment at age 35. Information has been collected on a broad range of risk and protective factors across multiple domains (e.g., individual, family, peer, school, neighborhood). Measures of conduct problems, substance use/abuse, criminal behavior, mental health problems have been collected.

This collection contains data and syntax files for delinquency constructs. The datasets include constructs on the frequency and level of criminal and delinquent activities, including theft, violence, weapons used, delinquency, drug-selling, white collar crime, as well as police contacts and past incarceration. Additionally, the collection includes data on delinquency risk (high vs. low) and the associated weight.

The delinquency constructs were created by using the PYS raw data. The raw data are available at ICPSR in the following studies: Pittsburgh Youth Study Youngest Sample (1987 - 2001) [Pittsburgh, Pennsylvania], Pittsburgh Youth Study Middle Sample (1987 - 1991) [Pittsburgh, Pennsylvania] , and Pittsburgh Youth Study Oldest Sample (1987 - 2000) [Pittsburgh, Pennsylvania].

Abstract (en): The purpose of this data collection was to follow a birth cohort born in Philadelphia during 1958 with a special focus on delinquent activities as children and as adults. The respondents were first interviewed in DELINQUENCY IN A BIRTH COHORT IN PHILADELPHIA, PENNSYLVANIA, 1945-1963 (ICPSR 7729). Part 1 offers basic demographic information, such as sex, race, date of birth, church membership, age, and socioeconomic status, on each cohort member. Two files supply offense data: Part 2 pertains to offenses committed while a juvenile and Part 3 details offenses as an adult. Offense-related variables include most serious offense, police disposition, location of crime, reason for police response, complainant's sex, age, and race, type of victimization, date of offense, number of victims, average age of victims, number of victims killed or hospitalized, property loss, weapon involvement, and final court disposition. Part 4, containing follow-up survey interview data collected in 1988, was designed to investigate differences in the experiences and attitudes of individuals with varying degrees of involvement with the juvenile justice system. Variables include individual histories of delinquency, health, household composition, marriage, parent and respondent employment and education, parental contacts with the legal system, and other social and demographic variables. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Checked for undocumented or out-of-range codes.. All children born in Philadelphia during 1958. 2006-01-12 All files were removed from dataset 5 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 5 and flagged as study-level files, so that they will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Office of Juvenile Justice and Delinquency Prevention. When using the Juvenile Offense file (Part 2), users should exclude from analyses any records of offenses committed when the offender was over 17 years of age. All records included in this file represent police contacts. Only a subset of these cases represent true offenses or violations of the Pennsylvania Crime Code. The variable EVENTYPE distinguishes between true offenses and cases that are police contacts only. The crime code fields can also be used to distinguish true offense charges from charges that represent police contacts only. Police contacts are those designated in the crime code value labels by an asterisk directly following the equal sign. For example, "1001 = COUNTERFEIT" represents a true offense, while "2624 = *RUNAWAY" represents a police contact only. To link the interview data from the survey file with either the juvenile delinquency history or adult criminal history databases, the user should utilize the LINKAGE DATABASE, provided in the Follow-Up Interview machine-readable codebook. A data collection instrument is available only for Part 4, the Follow-Up Interview data.Producers: Sellin Center for Studies in Criminology and Criminal Law and National Analysts, Division of Booz-Allen and Hamilton, Inc., Philadelphia, PA, 1990.

The Department of Justice launched Operation Weed and Seed in 1991 as a means of mobilizing a large and varied array of resources in a comprehensive, coordinated effort to control crime and drug problems and improve the quality of life in targeted high-crime neighborhoods. In the long term, Weed and Seed programs are intended to reduce levels of crime, violence, drug trafficking, and fear of crime, and to create new jobs, improve housing, enhance the quality of neighborhood life, and reduce alcohol and drug use. This baseline data collection effort is the initial step toward assessing the achievement of the long-term objectives. The evaluation was conducted using a quasi-experimental design, matching households in comparison neighborhoods with the Weed and Seed target neighborhoods. Comparison neighborhoods were chosen to match Weed and Seed target neighborhoods on the basis of crime rates, population demographics, housing characteristics, and size and density. Neighborhoods in eight sites were selected: Akron, OH, Bradenton (North Manatee), FL, Hartford, CT, Las Vegas, NV, Pittsburgh, PA, Salt Lake City, UT, Seattle, WA, and Shreveport, LA. The "neighborhood" in Hartford, CT, was actually a public housing development, which is part of the reason for the smaller number of interviews at this site. Baseline data collection tasks included the completion of in-person surveys with residents in the target and matched comparison neighborhoods, and the provision of guidance to the sites in the collection of important process data on a routine uniform basis. The survey questions can be broadly divided into these areas: (1) respondent demographics, (2) household size and income, (3) perceptions of the neighborhood, and (4) perceptions of city services. Questions addressed in the course of gathering the baseline data include: Are the target and comparison areas sufficiently well-matched that analytic contrasts between the areas over time are valid? Is there evidence that the survey measures are accurate and valid measures of the dependent variables of interest -- fear of crime, victimization, etc.? Are the sample sizes and response rates sufficient to provide ample statistical power for later analyses? Variables cover respondents' perceptions of the neighborhood, safety and observed security measures, police effectiveness, and city services, as well as their ratings of neighborhood crime, disorder, and other problems. Other items included respondents' experiences with victimization, calls/contacts with police and satisfaction with police response, and involvement in community meetings and events. Demographic information on respondents includes year of birth, gender, ethnicity, household income, and employment status.