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.

This map shows a comparable measure of crime in the United States. The crime index compares the average local crime level to that of the United States as a whole. An index of 100 is average. A crime index of 120 indicates that crime in that area is 20 percent above the national average.The crime data is provided by Applied Geographic Solutions, Inc. (AGS). AGS created models using the FBI Uniform Crime Report databases as the primary data source and using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. The crimes included in the models include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. The total crime index incorporates all crimes and provides a useful measure of the relative “overall” crime rate in an area. However, these are unweighted indexes, meaning that a murder is weighted no more heavily than a purse snatching in the computations. The geography depicts states, counties, Census tracts and Census block groups. An urban/rural "mask" layer helps you identify crime patterns in rural and urban settings. The Census tracts and block groups help identify neighborhood-level variation in the crime data.------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

This data ceased updating with the transition to a new records management system on 11/14/2023. Access to the updated data set has been added as of April 11, 2025 here: Crime Data Guide.

This study assessed the implementation and impact of the One Vision One Life (OVOL) violence-prevention strategy in Pittsburgh, Pennsylvania. In 2003, the rise in violence in Pittsburgh prompted community leaders to form the Allegheny County Violence Prevention Imitative, which became the OVOL program. The OVOL program sought to prevent violence using a problem-solving, data-driven model to inform how community organizations and outreach teams respond to homicide incidents. The research team examined the impact of the OVOL program on violence using a quasi-experimental design to compare violence trends in the program's target areas before and after implementation to (1) trends in Pittsburgh neighborhoods where One Vision was not implemented, and (2) trends in specific nontarget neighborhoods whose violence and neighborhood dynamics One Vision staff contended were most similar to those of target neighborhoods. The Pittsburgh Bureau of Police provided the violent-crime data, which the research team aggregated into monthly counts. The Pittsburgh Department of City Planning provided neighborhood characteristics data, which were extracted from the 2000 Census. Monthly data were collected on 90 neighborhoods in Pittsburgh, Pennsylvania from 1996 to 2007, resulting in 12,960 neighborhood-by-month observations.

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.

This compilation includes five historical datasets that are part of the University of Pittsburgh Library collection. The datasets were transcribed from The Pittsburgh Neighborhood Atlas, published in 1977. The atlas was prepared by the Pittsburgh Neighborhood Alliance. The information provides an insight into the neighborhoods conditions and the direction in which they were moving at the time of preparation. Much of the material describing neighborhood characteristics came from figures compiled for smaller areas: voting districts or census blocks. The five datasets in this collection provide data about overall neighborhood satisfaction and satisfaction with public services, based on a city-wide citizen survey. Also included are statistics about public assistance, the crime rate and the changes in real estate and mortgage loans transactions.

Tree canopy and crime in Pittsburgh, PA. Tree canopy data were from the 2010 Tree Canopy Assessment carried out by the University of Vermont Spatial Analysis Laboratory in collaboration with Tree Pittsburgh. Crime data was obtained from 2010 police reporting compiled by the Pittsburgh Neighborhood & Community Information System (PNCIS).

Highland Park. Crime = 3%, White = 66%, Poverty = 9%, Tree Canopy = 49% Larimer. Crime = 13%, White = 9%, Poverty = 22%, Tree Canopy = 22%

Graph and download economic data for Unemployment Rate in Pittsburgh, PA (MSA) (PITT342URN) from Jan 1990 to May 2025 about Pittsburgh, PA, unemployment, rate, and USA.

This data ceased updating with the transition to a new records management system on 11/14/2023. Access to the updated data set has been added as of April 11, 2025 here: https://data.wprdc.org/dataset/monthly-criminal-activity-dashboard.

The Police Blotter Archive contains crime incident data after it has been validated and processed to meet Uniform Crime Reporting (UCR) standards, published on a nightly basis. This data validation process creates a data publishing delay of approximately thirty days. Users who require the most recent incident data should use the 30 Day Police Blotter. The 30 Day Police Blotter dataset contains more recent data, but has not yet been run through quality control and standardization procedures by the Police Bureau. All data is reported at the block/intersection level, with the exception of sex crimes, which are reported at the police zone level.

This dataset only contains information reported by City of Pittsburgh Police, and does not contain incidents that solely involve other police departments operating within the city (campus police, Port Authority, etc.)

More documentation is available in our Crime Data Guide.

Abstract

The Police Blotter Archive contains crime incident data for the City of Pittsburgh. This dataset consists of two data tables: (i) historical (FY2005 through 2015) and (ii) current (FY2061-2023). For both tables, x- and y-geocodes representing the incident location are provided for each incident report.

These open government data are available online (https://data.wprdc.org/dataset/uniform-crime-reporting-data) under a Creative Commons License that allows for re-distribution and reuse with proper attribution to the creator (City of Pittsburgh). They have been uploaded here for use within the Redivis platform.

About this dataset

Context

The dataset tabulates the population of Pittsburgh by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pittsburgh. The dataset can be utilized to understand the population distribution of Pittsburgh by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pittsburgh. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Pittsburgh.

Key observations

Largest age group (population): Male # 25-29 years (16,615) | Female # 20-24 years (18,291). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Pittsburgh population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pittsburgh is shown in the following column.
• Population (Female): The female population in the Pittsburgh is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Pittsburgh for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pittsburgh Population by Gender. You can refer the same here

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Westmoreland County, PA (DISCONTINUED) (FBITC042129) from 2011 to 2018 about Westmoreland County, PA; Pittsburgh; crime; violent crime; property crime; PA; and USA.

About this dataset

Context

The dataset tabulates the population of Pittsburgh by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Pittsburgh across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.85% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Pittsburgh is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Pittsburgh total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pittsburgh Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Pittsburgh population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Pittsburgh across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Pittsburgh was 303,255, a 0.15% increase year-by-year from 2022. Previously, in 2022, Pittsburgh population was 302,799, a decline of 0.85% compared to a population of 305,400 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Pittsburgh decreased by 30,340. In this period, the peak population was 333,595 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Pittsburgh is shown in this column.
• Year on Year Change: This column displays the change in Pittsburgh population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pittsburgh Population by Year. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Mcknight Road cross streets in Pittsburgh, PA.

Graph and download economic data for Unemployed Persons in Pittsburgh, PA (MSA) (LAUMT423830000000004) from Jan 1990 to May 2025 about Pittsburgh, PA, household survey, unemployment, persons, and USA.

This dataset provides information about the number of properties, residents, and average property values for Pennsylvania Boulevard cross streets in Pittsburgh, PA.

Graph and download economic data for All Employees: Educational Services in Pittsburgh, PA (MSA) (SMU42383006561000001A) from 1990 to 2024 about Pittsburgh, PA, education, services, employment, and USA.

Graph and download economic data for Total Quarterly Wages in Pittsburgh, PA (MSA) (ENUC383030010SA) from Q1 1990 to Q4 2024 about Pittsburgh, PA, wages, and USA.