The violent crime rate in Pennsylvania increased by **** percent from 2019 to 2020. Nevertheless, average violent crime rate in the United States in 2020 only increased by *** percent from the previous year.

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 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.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Beaver County, PA was 3.00000 Known Incidents in January of 2018, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Beaver County, PA reached a record high of 19.00000 in January of 2004 and a record low of 0.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Beaver County, PA - last updated from the United States Federal Reserve on June of 2025.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Washington County, PA was 5.00000 Known Incidents in January of 2020, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Washington County, PA reached a record high of 7.00000 in January of 2012 and a record low of 0.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Washington County, PA - last updated from the United States Federal Reserve on July of 2025.

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.

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This dataset is about Philadelphia, PA and includes average house sales price in a number of neighborhoods. The attributes of each neighborhood we have include the crime rate ('CrimeRate'), miles from Center City ('MilesPhila'), town name ('Name'), and county name ('County').

Combined Violent and Property Crime Offenses Known to Law Enforcement in Wyoming County, PA was 0.00000 Known Incidents in January of 2018, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Wyoming County, PA reached a record high of 0.00000 in January of 2016 and a record low of 0.00000 in January of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Wyoming County, PA - last updated from the United States Federal Reserve on June of 2025.

This dataset includes publicly available data published primarily by the Pennsylvania Department of Education and the Pennsylvania Office of Safe Schools. The dataset was created by combining several publications by the Pennsylvania Department of Education, including the 2017 School Fast Fact database, 2016-2017 Academic Performance database, and the 2017 Keystone Score database. The dataset includes institutional (school-wide) variables for every public high school in Pennslyvania (n = 407 ). The data includes information surrounding each institution's socio-economic status, racial composition, academic performance, and type of and total use of exclusionary discipline (in-school suspension, out-of-school suspension, and expulsion) for the school year 2016-2017. The dataset also includes neighborhood information for each school location. This data was collected from AreaVibes, a website known for its ability to guide individuals in their search for ideal residential areas in the United States and Canada. AreaVibes deploys a unique algorithm that evaluates multiple different data points for each location, including amenities, cost of living, crime rates, employment, housing, schools, and user ratings. This dataset deployed AreaVibes to input the physical addresses of each high school in order to retrieve the livability score for the surrounding neighborhoods of these educational institutions. Furthermore, the website was instrumental in collecting neighborhood crime scores, offering valuable insights into the levels of criminal activity within specific geographic zones. The crime score takes into account both violent crime and property crime. However, higher weights are given to violent crimes (65%) than property crime (35%) as they are more severe. Data for calculation by Areavibes is derived from FBI Uniform Crime Report.School discipline is crucial for ensuring safety, well-being, and academic success. However, the continued use of exclusionary discipline practices, such as suspension and expulsion, has raised concerns due to their ineffectiveness and harmful effects on students. Despite compelling evidence against these practices, many educational institutions persist in relying on them. This persistence has led to a troubling reality—a racial and socioeconomic discipline gap in schools. This data is used to explore the evident racial and socioeconomic disparities within high school discipline frameworks, shedding light on the complex web of factors that contribute to these disparities and exploring potential solutions. Drawing from social disorganization theory, the data explores the interplay between neighborhood and school characteristics, emphasizing the importance of considering the social context of schools.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Cumberland County, PA was 0.00000 Known Incidents in January of 2018, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Cumberland County, PA reached a record high of 1.00000 in January of 2005 and a record low of 0.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Cumberland County, PA - last updated from the United States Federal Reserve on July of 2025.

In 2023, law enforcement officers committed 303 justifiable homicides in the United States. A justifiable homicide is defined as the killing of a felon during the commission of a felony. What is homicide? Homicide occurs when one person kills another; however it is not exactly the same as murder. It may or may not be considered criminal. Legal examples include a person killing an intruder in their home or capital punishment. There are different types of homicide, which includes murder and manslaughter. Homicide trends in the United States As of 2023, California had the highest number of homicides, followed by Texas, Florida, Pennsylvania, and North Carolina. That same year, murders with one victim and one offender were the most common in the United States. Overall, the United States has had a much higher rate of homicide in the past years when compared to their neighbor, Canada.

We develop a panel count model with a latent spatio-temporal heterogeneous state process for monthly severe crimes at the census-tract level in Pittsburgh, Pennsylvania. Our dataset combines Uniform Crime Reporting data with socio-economic data. The likelihood is estimated by efficient importance sampling techniques for high-dimensional spatial models. Estimation results confirm the broken-windows hypothesis whereby less severe crimes are leading indicators for severe crimes. In addition to ML parameter estimates, we compute several other statistics of interest for law enforcement such as spatio-temporal elasticities of severe crimes with respect to less severe crimes, out-of-sample forecasts, predictive distributions and validation test statistics.

California reported the largest number of homicides to the FBI in 2023, at 1,929 for the year. Texas recorded the second-highest number of murders, with 1,845 for the year. Homicide victim demographics There were a total of 19,252 reported homicide cases in the U.S. in 2023. When looking at murder victims by gender and ethnicity, the vast majority were male, while just over half of the victims were Black or African American. In addition, homicide victims in the United States were found most likely to be between the ages of 20 and 34 years old, with the majority of victims aged between 17 to 54 years old. Are murders up? In short, no – since the 1990s the number of murders in the U.S. has decreased significantly. In 1990, the murder rate per 100,000 people stood at 9.4, and stood at 5.7 in 2023. It should be noted though that the number of homicides increased slightly from 2014 to 2017, although figures declined again in 2018 and 2019, before ticking up once more in 2020 and 2021. Despite this decline, when viewed in international comparison, the U.S. murder rate is still notably high. For example, the Canadian homicide rate stood at 1.94 in 2023, while the homicide rate in England and Wales was even lower.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Bucks County, PA was 1.00000 Known Incidents in January of 2019, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Bucks County, PA reached a record high of 1.00000 in January of 2017 and a record low of 0.00000 in January of 2015. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Bucks County, PA - last updated from the United States Federal Reserve on June of 2025.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Snyder County, PA was 0.00000 Known Incidents in January of 2019, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Snyder County, PA reached a record high of 3.00000 in January of 2008 and a record low of 0.00000 in January of 2005. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Snyder County, PA - last updated from the United States Federal Reserve on July of 2025.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Allegheny County, PA was 316.00000 Known Incidents in January of 2020, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Allegheny County, PA reached a record high of 692.00000 in January of 2004 and a record low of 3.00000 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Allegheny County, PA - last updated from the United States Federal Reserve on June of 2025.

Panama PA: Intentional Homicides: Male: per 100,000 Male data was reported at 17.103 Ratio in 2016. This records a decrease from the previous number of 19.992 Ratio for 2015. Panama PA: Intentional Homicides: Male: per 100,000 Male data is updated yearly, averaging 19.992 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 31.834 Ratio in 2012 and a record low of 11.838 Ratio in 2006. Panama PA: Intentional Homicides: Male: per 100,000 Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank: Health Statistics. Intentional homicides, male are estimates of unlawful male homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

Combined Violent and Property Crime Offenses Known to Law Enforcement in Clarion County, PA was 1.00000 Known Incidents in January of 2018, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Clarion County, PA reached a record high of 4.00000 in January of 2008 and a record low of 0.00000 in January of 2005. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Clarion County, PA - last updated from the United States Federal Reserve on June of 2025.

Panama PA: Intentional Homicides: Female: per 100,000 Female data was reported at 2.194 Ratio in 2016. This records a decrease from the previous number of 2.482 Ratio for 2015. Panama PA: Intentional Homicides: Female: per 100,000 Female data is updated yearly, averaging 1.927 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 2.614 Ratio in 2013 and a record low of 1.127 Ratio in 2006. Panama PA: Intentional Homicides: Female: per 100,000 Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank: Health Statistics. Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

Panama PA: Intentional Homicides: per 100,000 People data was reported at 11.400 Ratio in 2015. This records a decrease from the previous number of 14.700 Ratio for 2014. Panama PA: Intentional Homicides: per 100,000 People data is updated yearly, averaging 11.400 Ratio from Dec 1995 (Median) to 2015, with 21 observations. The data reached an all-time high of 23.000 Ratio in 2009 and a record low of 9.079 Ratio in 1996. Panama PA: Intentional Homicides: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank: Health Statistics. Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; Weighted average;