Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in King County, WA (DISCONTINUED) (FBITC053033) from 2005 to 2021 about King County, WA; crime; violent crime; property crime; Seattle; WA; and USA.

The Seattle Police Department (SPD) replaced its Records Management System (RMS) in May 2019. To preserve data quality and continuity between systems (2008-Present), SPD relied on the National Incident-Based Reporting System (NIBRS). The standardization of crime classifications allows for comparison over time. For more information on definitions and classifications, please visit https://www.fbi.gov/services/cjis/ucr/nibrs.

Additional groupings are used to analyze crime in SPD’s Crime Dashboard. Violent and property crime categories align with best practices. For additional inquiries, we encourage the use of the underline data to align with the corresponding query.

Disclaimer: Only finalized (UCR approved) reports are released. Those in draft, awaiting approval, or completed after the update, will not appear until the subsequent day(s). Data is updated once every twenty-four hours. Records and classification changes will occur as a report makes its way through the approval and investigative process.

The objective of the Seattle Neighborhoods and Crime Survey (SNCS) was to test multilevel theories of neighborhood social organization and criminal violence. It was funded by the National Science Foundation (SES-0004324), and the National Consortium on Violence Research (SBR-9513040). Using the concept of differential neighborhood organization, the investigators posited that neighborhood crime is a function of informal social control against crime and informal organization in favor of crime. Informal neighborhood control against crime consists of neighborhood attachment, social capital, and collective efficacy. The study tested the hypothesis that individual social ties are explained by a rational choice model, which in turn produces neighborhood social capital that can be used to achieve collective goals. It also tested the hypothesis that neighborhoods rich in social capital had greater collective efficacy, which in turn, helped produce safe neighborhoods. Organization in favor of crime consists of violent codes of the street. The study tested the hypothesis that residents from disadvantaged neighborhoods tend to distrust police and other agents of conventional institutions, and consequently are more likely to participate in street culture, in which violence is a way of obtaining street credibility and status, as well as resolving disputes. The project has also examined dimensions of neighboring, and the causes and consequences of fear of crime. The study used a telephone survey of households within all 123 census tracts in the city of Seattle, WA, conducted in 2002-2003. The sampling frame was designed by investigators at the University of Washington, with three objectives in mind: (a) to gain a random sample of households within each of 123 census tracts; (b) to obtain a disproportionate number of racial and ethnic minorities using an ethnic oversample; and (c) to obtain a replication sample of Terrance Miethe's 1990 victimization survey in 100 Seattle neighborhoods [Testing Theories of Criminality and Victimization in Seattle, 1960-1990]. Specific samples were drawn by Genesys, a sampling firm in Philadelphia, PA, using a constantly-updated compilation of white pages. Telephone interviews were conducted by the Social and Behavioral Research Institute at California State University, San Marcos, using computer-assisted telephone interviewing (CATI) technology. Respondents were asked about household demographics, such as race, gender, residential mobility, age distribution of the household, and income, their perceptions and assessments of their neighborhoods (including safety, disorder, and crime), neighbors, and relations with police. A variety of questions about neighboring were asked, including social capital (intergenerational closure, reciprocated exchange, and participation in neighborhood associations), attachment to their neighborhood, and collective efficacy (child-centered social control). Respondents were asked about routine activities including taking steps to protect their homes, spending time in bars and nightclubs, and leaving their home unattended. Questions about fear of crime included personal fear as well as altruistic fear for other members of the household, and questions about racial attitudes included residential preferences by race composition of the neighborhood. A victimization inventory modeled after the National Crime Victimization Survey was used for burglary, vandalism, stolen property, violence, and robbery. Demographic information includes age, race, sex, education, martial status, household income, whether respondent was a student, employment status, religious affiliation, approximate value of home, monthly rent including utilities, residence history in the last five years, whether respondent was born in the Unites States, and number of people currently living in the respondent's household.

In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.

This study extends a prior National Institute (NIJ) funded study on mirco level places that examined the concentration of crime at places over time. The current study links longitudinal crime data to a series of other databases. The purpose of the study was to examine the possible correlates of variability in crime trends over time. The focus was on how crime distributes across very small units of geography. Specifically, this study investigated the geographic distribution of crime and the specific correlates of crime at the micro level of geography. The study reported on a large empirical study that investigated the "criminology of place." The study linked 16 years of official crime data on street segments (a street block between two intersections) in Seattle, Washington, to a series of datasets examining social and physical characteristics of micro places over time, and examined not only the geography of developmental patterns of crime at place but also the specific factors that are related to different trajectories of crime. The study used two key criminological perspectives, social disorganization theories and opportunity theories, to inform their identification of risk factors in the study and then contrast the impacts of these perspectives in the context of multivariate statistical models.

This data represents crime reported to the Seattle Police Department (SPD). Each row contains the record of a unique event where at least one criminal offense was reported by a member of the community or detected by an officer in the field. This data is the same data used in meetings such as SeaStat (https://www.seattle.gov/police/information-and-data/seastat) for strategic planning, accountability and performance management.

These data contain offenses and offense categorization coded to simulate the standard reported to the FBI under the National Incident Based Reporting System (NIBRS) and used to generate Uniform Crime Report (UCR) summary statistics. As these records evolve, daily and are continually refreshed, they will not match official UCR statistics. They represent a more accurate state of the record.

Previous versions of this data set have withheld approximately 40% of crimes. This updated process includes all records of crime reports logged in the Departments Records Management System (RMS) since 2008, which are tracked as part of the SeaStat process. In an effort to safeguard the privacy of our community, offense reports will only be located to the “beat” level. Location specific coordinates will no longer be provided.

Beats are the most granular unit of management used for patrol deployment. To learn more about patrol deployment, please visit: https://www.seattle.gov/police/about-us/about-policing/precinct-and-patrol-boundaries. In addition to the Departments patrol deployment areas, these data contain the “Neighborhood” where the crime occurred, if available. This coding is used to align crime data with the Micro Community Policing Plan (MCPP). For more information see: https://www.seattle.gov/police/community-policing/about-mcpp.

As with any data, certain condition and qualifications apply: 1) These data are refreshed, daily and represent the most accurate, evolved state of the record.

2) Due to quality control processes, these data will lag between 2 and 6 weeks. Most changes will occur within that record and reports logged in the last 2 weeks should be treated as volatile. Analysts may wish to remove these records from their analysis.

3) Not all offenses are reported here, only the primary offense as determined by the “Hierarchy Rule.” For more information on NIBRS and UCR, see the FBI (https://ucr.fbi.gov/nibrs-overview).

4) This dataset contains records of offenses that occurred prior to “go-live” of the existing RMS. Records are queried based on the full population of data and are not constrained by “Occurred Date.”

We invite you to engage these data, ask questions and explore.

The King County Sheriff’s Office (KCSO) is providing offense report data captured in it's Records Management System (RMS) from 2020 to present. KCSO replaced its RMS in late 2018 and at the same time transitioned to the FBI’s National Incident-Based Reporting System (NIBRS). The NIBRS standardization of crime classifications allows for comparison over time and between agencies. For official KCSO NIBRS reporting, please visit the WASPC Crime in Washington Report: https://www.waspc.org/cjis-statistics---reports.

Disclaimer: Only finalized (supervisor approved) reports are released. Those in draft, awaiting supervisor approval, or completed after the daily update of data, will not appear until the subsequent day(s). Data updates once every twenty-four hours. Records and classification changes will occur as a report makes its way through the approval and investigative process, thus reports might appear in the data set one day, but be removed the next day if there is a change in the approval status. This mirrors the fluidity of an investigation. Once a report is re-approved, it will show back up in the data set. Other than approval status, the report case status is factored into what can be released in the daily data set. As soon as a report case status matches the criteria for release, it will be included in the data set. For a list of offenses that are included in the data set, please see the attached pdf.

Resources: - KCSO's 2019 crime data: https://data.kingcounty.gov/Law-Enforcement-Safety/King-County-Sheriff-s-Office-Incident-Dataset/rzfs-wyvy - Police District GIS shapefile: https://gis-kingcounty.opendata.arcgis.com/datasets/king-county-sheriff-patrol-districts-patrol-districts-area/explore - Police District key: https://data.kingcounty.gov/Law-Enforcement-Safety/KCSO-Patrol-Districts/ptrt-hdax/data - For more information on definitions and classifications, please visit https://www.fbi.gov/services/cjis/ucr/nibrs - SPD's Crime Data: https://data.seattle.gov/Public-Safety/SPD-Crime-Data-2008-Present/tazs-3rd5

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.

There has been little research on United States homicide rates from a long-term perspective, primarily because there has been no consistent data series on a particular place preceding the Uniform Crime Reports (UCR), which began its first full year in 1931. To fill this research gap, this project created a data series on homicides per capita for New York City that spans two centuries. The goal was to create a site-specific, individual-based data series that could be used to examine major social shifts related to homicide, such as mass immigration, urban growth, war, demographic changes, and changes in laws. Data were also gathered on various other sites, particularly in England, to allow for comparisons on important issues, such as the post-World War II wave of violence. The basic approach to the data collection was to obtain the best possible estimate of annual counts and the most complete information on individual homicides. The annual count data (Parts 1 and 3) were derived from multiple sources, including the Federal Bureau of Investigation's Uniform Crime Reports and Supplementary Homicide Reports, as well as other official counts from the New York City Police Department and the City Inspector in the early 19th century. The data include a combined count of murder and manslaughter because charge bargaining often blurs this legal distinction. The individual-level data (Part 2) were drawn from coroners' indictments held by the New York City Municipal Archives, and from daily newspapers. Duplication was avoided by keeping a record for each victim. The estimation technique known as "capture-recapture" was used to estimate homicides not listed in either source. Part 1 variables include counts of New York City homicides, arrests, and convictions, as well as the homicide rate, race or ethnicity and gender of victims, type of weapon used, and source of data. Part 2 includes the date of the murder, the age, sex, and race of the offender and victim, and whether the case led to an arrest, trial, conviction, execution, or pardon. Part 3 contains annual homicide counts and rates for various comparison sites including Liverpool, London, Kent, Canada, Baltimore, Los Angeles, Seattle, and San Francisco.

The primary objective of this study was to test criminal opportunity theories of victimization and the collective benefits or harm resulting from citizen-based crime control activities. Other areas of investigation included crime displacement, "free-rider" effects (i.e., crimes occurring in conjunction with other crimes), and a multilevel analysis of victimization risks. Two types of data were gathered for this collection. First, census tract data were used to identify tracts that had not changed their physical boundaries since 1960. In addition, statistics were gathered from police reports for the same years. Variables for the census tract data (Part 1) include median family income in constant 1980 dollars, average number of persons per occupied housing unit, percent of labor force taking public transportation to work, percent of children under 18 living with both parents, and percent of civilian labor force that was female. Police report variables in Part 1 include rates per 100,000 population for homicide, rape, robbery, assault, residential burglary, and automobile theft. Secondly, during a telephone survey of Seattle residents conducted in 1990, respondents were asked a variety of questions about their experiences with crime and victimization. These data, presented in Part 2, cover burglaries, stolen property, physical assaults by strangers, vandalism, car thefts, type of neighborhood, type of home, security measures taken, and sociodemographic conditions. The unit of analysis for this data collection is housing units.