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 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.

The 30-Day Police Blotter contains the most recent initial crime incident data, updated on a nightly basis. All data is reported at the block/intersection level, with the exception of sex crimes, which are reported at the police zone level. The information is "semi-refined" meaning a police report was taken, but it has not made its way through the court system. This data is subject to change once it is processed and republished using Uniform Crime Reporting (UCR) standards. The UCR coding process creates a necessary delay before processed data is available for publication. Therefore, the 30-Day blotter will provide information for users seeking the most current information available. This dataset will be continually overwritten and any records older than thirty days will be removed. Validated incidents will be moved to the Police Blotter Archive dataset. Data in the archived file is of a higher quality and is the file most appropriate for reporting crime statistics. 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.

A map used to represent crimes in Pittsburgh, PA that will be used in crime analysis for training exercise.

This data ceased updating with the transition to a new records management system on 11/14/2023. Access to the updated data source will be provided in the future.

The 30-Day Police Blotter contains the most recent initial crime incident data, updated on a nightly basis. All data is reported at the block/intersection level, with the exception of sex crimes, which are reported at the police zone level. The information is "semi-refined" meaning a police report was taken, but it has not made its way through the court system. This data is subject to change once it is processed and republished using Uniform Crime Reporting (UCR) standards. The UCR coding process creates a necessary delay before processed data is available for publication. Therefore, the 30-Day blotter will provide information for users seeking the most current information available.

This dataset will be continually overwritten and any records older than thirty days will be removed. Validated incidents will be moved to the Police Blotter Archive dataset. Data in the archived file is of a higher quality and is the file most appropriate for reporting crime statistics.

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.

Graph and download economic data for Unemployment Rate in Pittsburgh, PA (MSA) (PITT342UR) from Jan 1990 to Jun 2025 about Pittsburgh, PA, unemployment, rate, and USA.

About the G.M. Hopkins Maps History and Background of the Maps Maps produced by the G.M. Hopkins Company have made a lasting impression on the boundaries of many American cities. Between 1870 and 1940, the company produced over 175 atlases and real estate plat maps that primarily covered the Eastern sea board, including cities, counties, and townships in 18 different states and the District of Columbia. In the early years, the company produced county atlases, but gradually focused on city plans and atlases. They were among the first publishers to create a cadastral atlas, a cross between a fire insurance plat and a county atlas prevalent in the 1860s-1870s. These real estate or land ownership maps (also known as plat maps) not only depict property owners, but show lot and block numbers, dimensions, street widths, and other buildings and landmarks, including churches, cemeteries, mills, schools, roads, railroads, lakes, ponds, rivers, and streams. Originally named the G.M. Hopkins and Company, the map-making business was jointly founded in 1865 in Philadelphia, Pa., by the Hopkins brothers, G.M. and Henry. The true identity of G.M. Hopkins remains somewhat of a mystery even today. “G.M.” either stands for Griffith Morgan or George Morgan. There are three different possibilities for the confusion over his identity. “Either the compilers of the earlier [city] directories were negligent; G.M. Hopkins changed his first name; or there were two G.M. Hopkins (father and son) working for the same firm” (Moak, Jefferson M. Philadelphia Mapmakers. Philadelphia: Shackamaxon Society, 1976, p. 258).

http://digital.library.pitt.edu/abouthp/#hopkins

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.

Unemployment Rate in Pittsburgh, PA (MSA) was 3.60% in May of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Pittsburgh, PA (MSA) reached a record high of 15.70 in April of 2020 and a record low of 3.10 in April of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Pittsburgh, PA (MSA) - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Civilian Labor Force in Pittsburgh, PA (MSA) (PITT342LF) from Jan 1990 to Jun 2025 about Pittsburgh, PA, civilian, labor force, labor, and USA.

Graph and download economic data for Unemployment Rate in Westmoreland County, PA (PAWEST5URN) from Jan 1990 to May 2025 about Westmoreland County, PA; Pittsburgh; PA; unemployment; rate; and USA.

Graph and download economic data for Unemployment Rate in Beaver County, PA (PABEAV7URN) from Jan 1990 to Jun 2025 about Beaver County, PA; Pittsburgh; PA; unemployment; rate; and USA.