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.

Part I Crime - Last 14 Days**First time checking out this data set? Start Here! Data provided in the Crime Map/Viewer are preliminary General Offense Reports extracted from the Rochester Police Department’s Law Enforcement Records Management System. The data is grouped into the Federal Bureau of Investigations (FBI) Uniform Crime Reporting (UCR) Part I Crime classifications, with the exception of Rape, which has been excluded in accordance with privacy regulations.

Part I Crime - Last 28 Days**First time checking out this data set? Start Here! Data provided in the Crime Map/Viewer are preliminary General Offense Reports extracted from the Rochester Police Department’s Law Enforcement Records Management System. The data is grouped into the Federal Bureau of Investigations (FBI) Uniform Crime Reporting (UCR) Part I Crime classifications, with the exception of Rape, which has been excluded in accordance with privacy regulations.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Ontario County, NY (DISCONTINUED) (FBITC036069) from 2004 to 2020 about Ontario County, NY; Rochester; crime; violent crime; property crime; NY; and USA.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Monroe County, NY (DISCONTINUED) (FBITC036055) from 2004 to 2021 about Monroe County, NY; Rochester; crime; violent crime; property crime; NY; and USA.

Clearance rates for most crimes have remained low and stable for decades (Braga et al., 2011), despite advances in police technologies (Koper et al., 2015). Many police agencies have developed triaging practices for criminal investigations using solvability factors to guess which cases are most likely to be solved and to allocate investigative resources to those crimes (Eck, 1983; 1992). This practice partially stems from a persistent belief that resolving crimes and the resulting clearance rates are due to circumstances of the crime and community context, and are beyond the control of police. However, a growing body of research has challenged this belief, demonstrating that enhanced investigative efforts can improve crime clearance rates beyond solvability factors (Braga and Dusseault, 2018; Lum and Wellford, 2023). In this study, the research team sought to determine if investigative follow-ups could increase clearance rates for robbery and burglary cases (frequently occurring crime types with traditionally low clearance rates) and increase victim satisfaction with police services. Agencies selected for the study were the Seattle Police Department (SPD) in Seattle, Washington, and the Rochester Police Department (RPD) in Rochester, New York. Both agencies triaged a large proportion of robbery cases and would have a large enough sample size to successfully carry out an experiment. The original study design was a randomized controlled trial. In both sites, robbery cases would be allocated to either the intervention condition--an investigative follow-up conducted by an officer during their daily patrol assignment--or the control condition with no follow-up. Challenges to personnel and agency funding from the COVID-19 pandemic, the murder of George Floyd, and other officer-involved deaths in 2020 led to difficulties implementing the study as initially designed. The experiment was not initiated in Rochester, and initiated but not completed in Seattle. Therefore, the team transitioned to a natural quasi-experiment design in Rochester and added a case analysis of robberies in Seattle. This collection contains three datasets: victim satisfaction surveys from Seattle (DS1, n=39) and Rochester (DS2, n=37), and supplemental reports on follow-ups made during the Seattle experiment implementation (DS3, n=82).

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Dodge County, MN (DISCONTINUED) (FBITC027039) from 2004 to 2021 about Dodge County, MN; crime; violent crime; property crime; Rochester; MN; and USA.

This data collection examines the needs of burglary, robbery, and assault victims and the responses of local victim assistance programs to those needs in four metropolitan areas: Evanston, Illinois, Rochester, New York, Pima County, Arizona, and Fayette County, Kentucky. Four issues were explored in detail: the needs of victims, where they seek help, the kinds of help they receive, and which of their problems do and do not get resolved. Variables include (1) demographic information such as city of residence, length of residence, birth date, marital status, race, work status, education, and income, (2) information on the crime itself, such as type of crime, when the crime happened, and details of the attack and attacker, and (3) consequences of the crime, such as problems encountered as a result of the crime, emotional responses to the crime, and reactions to the crime on a practical level.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Wabasha County, MN (DISCONTINUED) (FBITC027157) from 2004 to 2021 about Wabasha County, MN; Rochester; crime; violent crime; property crime; MN; and USA.

The data for this study were collected in order to examine the delivery of police services in selected neighborhoods. Performances of police agencies organized in different ways were compared as they delivered services to different sets of comparable neighborhoods. For Part 1, Citizen Debriefing Data, data were drawn from telephone interviews conducted with citizens who were involved in police-citizen encounters or who requested police services during the observed shifts. The file contains data on the citizens involved in observed encounters, their satisfaction with the delivered services, and neighborhood characteristics. This file includes variables such as the type of incident, estimated property loss, police response time, type of action taken by police, citizen satisfaction with the handling of the problem by police, reasons for dissatisfaction, the emotional state of the citizen during the encounter, whom the officers referred the citizen to for help, the citizen's prior contacts with police, and the citizen's education, age, sex, and total family income. Part 2, General Shift Information, contains data describing the shift (i.e., the eight-hour tour of duty to which the officers were assigned), the officers, and the events occurring during an observed shift. This file includes such variables as the total number of encounters, a breakdown of dispatched runs by type, the number of contacts with other officers, the number of contacts with non-police support units, officer discretion in taking legal action, and officer attitudes on patrol styles and activities. Part 3, Police Encounters Data, describes police encounters observed by the research team during selected shifts. It consists of information describing the officers' role in encounters with citizens observed during a shift and their demeanor toward the citizens involved. The file includes variables such as the type of encounter, how the encounter began, whether the citizens involved possessed a weapon, the encounter location, what other agencies were present during the encounter and when they arrived, police actions during the encounter, the role of citizens involved in the encounter, the demeanor of the officer toward the citizens during the encounter, actions taken by the citizens, which services were requested by the citizens, and how the observer affected the encounter. Part 4, Victimization Survey Data, examined citizen attitudes about the police and crime in their neighborhoods. The data were obtained through telephone interviews conducted by trained interviewers. These interviews followed a standard questionnaire designed by the project leaders. Variables include perceived risk of victimization, evaluations of the delivery of police services, household victimization occurring in the previous year, actions taken by citizens in response to crime, and demographic characteristics of the neighborhood.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Livingston County, NY (DISCONTINUED) (FBITC036051) from 2004 to 2021 about Livingston County, NY; crime; violent crime; property crime; Rochester; NY; and USA.

Part I Crime - 2011 to Present**First time checking out this data set? Start Here!Data provided in the Crime Map/Viewer are preliminary General Offense Reports extracted from the Rochester Police Department’s Law Enforcement Records Management System. The data is grouped into the Federal Bureau of Investigations (FBI) Uniform Crime Reporting (UCR) Part I Crime classifications, with the exception of Rape, which has been excluded in accordance with privacy regulations.

The counts of arrests are derived from information transmitted from law enforcement agencies to the Division of Criminal Justice Services Computerized Criminal History database for fingerprintable offenses.An adult arrest is defined as an arrest of a person 16 years old or older or a juvenile offender prosecuted in adult court. Fingerprintable offenses (defined in Criminal Procedure Law §160.10) include any felony, a misdemeanor defined in the penal law, a misdemeanor defined outside the penal law which would constitute a felony if such a person had a previous judgment of conviction for a crime, or loitering for the purpose of engaging in prostitution as defined in subdivision two of Penal Law §240.37.