This indicator is based on location of residence. Mortality rate has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify homicides are X85-Y09, Y87.1, and U01-U02. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Violence is a public health crisis in the US, with gun violence being a major driver. Almost three quarters of homicides involve firearms. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults ages 15-34 years, males, and Black individuals most disproportionately impacted. Comprehensive prevention strategies should work to address the underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. Law enforcement agencies contribute reports either directly or through their state reporting programs. Each year, summary data are reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Offenses Known and Clearances by Arrest data files include monthly data on the number of Crime Index offenses reported and the number of offenses cleared by arrest or other means. The counts include all reports of Index crimes (excluding arson) received from victims, officers who discovered infractions, or other sources.

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). The extract files version of NIBRS was created to simplify working with NIBRS data. Data management issues with NIBRS are significant, especially when two or more segment levels are being merged. These issues require skills separate from data analysis. NIBRS data as formatted by the FBI are stored in a single file. These data are organized by various segment levels (record types). There are six main segment levels: administrative, offense, property, victim, offender, and arrestee. Each segment level has a different length and layout. There are other segment levels that occur with less frequency than the six main levels. Significant computing resources are necessary to work with the data in its single-file format. In addition, the user must be sophisticated in working with data in complex file types. For these reasons and the desire to facilitate the use of NIBRS data, ICPSR created the extract files. The data are not a representative sample of crime in the United States.

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.

Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on. self-enumerated questionnaireStarting with the year 1998, each of the four parts of the UCR data collection archived by ICPSR is released as a separate study under its own study number. The Police Employee data for the years 1975-1997 can be found in UNIFORM CRIME REPORTING PROGRAM DATA [UNITED STATES]: 1975-1997 (ICPSR 9028). Datasets: DS1: Uniform Crime Reporting Program Data [United States]: Police Employee (LEOKA) Data, 2004 Law enforcement officers killed or assaulted as reported by law enforcement agencies. inap.

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program each year by police agencies in the United States. These arrest reports provide data on 43 offenses including violent crime, drug use, gambling, and larceny. The data received by ICPSR were structured as a hierarchical file containing (per reporting police agency) an agency header record, 1 to 12 monthly header records, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to a rectangular format. Arrests reported to the Federal Bureau of Investigation's Uniform Crime Reporting program. Smallest Geographic Unit: states, counties, and cities Datasets: DS1: Uniform Crime Reporting Program Data [United States]: Arrests by Age, Sex, and Race, 1980 mail questionnaire; on-site questionnaire

Two major changes to the Uniform Crime Reports (UCR) county-level files were implemented beginning with the 1994 data. A new imputation algorithm to adjust for incomplete reporting by individual law enforcement jurisdictions was adopted. Within each county, data from agencies reporting 3 to 11 months of information were weighted to yield 12-month equivalents. Data for agencies reporting less than 3 months of data were replaced with data estimated by rates calculated from agencies reporting 12 months of data located in the agency's geographic stratum within its state. Secondly, a new Coverage Indicator was created to provide users with a diagnostic measure of aggregated data quality in a particular county. Data from agencies reporting only statewide figures were allocated to the counties in the state in proportion to each county's share of the state population.In the arrest files (Parts 1-3 and 5-7), data were estimated for agencies reporting 0 months based on the procedures mentioned above. However, due to the structure of the data received from the FBI, estimations could not be produced for agencies reporting 0 months in the crimes reported files (Parts 4 and 8). Offense data for agencies reporting 1 or 2 months are estimated using the above procedures. Users are encouraged to refer to the codebook for more information.No arrest data were provided for Washington, DC, and Florida. Limited arrest data were available for Illinois and Kentucky. Limited offense data were available for Illinois, Kentucky, Mississippi, Missouri, Montana, and South Dakota.UCR program staff at the Federal Bureau of Investigation (FBI) were consulted in developing the new adjustment procedures. However, these UCR county-level files are not official FBI UCR releases and are being provided for research purposes only. Users with questions regarding these UCR county-level data files can contact the National Archive of Criminal Justice Data at ICPSR.Users should note that there are no records in the data for the borough of Denali, Alaska (FIPS code 02068) in any of the collections of the Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest and Offense Data from 1990 to 2003. The borough of Denali, Alaska (FIPS code 02068) was created from part of the Yukon-Koyukuk Census Area (FIPS code 02290) an unpopulated part of the Southeast Fairbanks Census Area (FIPS code 02240) effective December 7, 1990. Since no agency records for either arrests or crimes reported from Denali were present in any of the original FBI files, no data for the borough of Denali, Alaska appear in any the ICPSR collections for these years. This data collection contains county-level counts of arrests and offenses for Part I offenses (murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, and arson) and counts of arrests for Part II offenses (forgery, fraud, embezzlement, vandalism, weapons violations, sex offenses, drug and alcohol abuse violations, gambling, vagrancy, curfew violations, and runaways). Datasets: DS0: Study-Level Files DS1: Arrests, All Ages DS2: Arrests, Adult DS3: Arrests, Juveniles DS4: Crimes Reported DS5: Allocated Statewide Data for Arrests, All Ages DS6: Allocated Statewide Data for Arrests, Adults DS7: Allocated Statewide Data for Arrests, Juveniles DS8: Allocated Statewide Data for Crimes Reported County law enforcement agencies in the United States.

The dataset contains outcome variables, control variables, and policy variables. The outcome variables pertain to the change and growth in state-level incarceration rates between 1975 and 2002. Control variables include violent crime rate, property crime rate, percent population between ages of 18-24, percent population between ages of 25-34, percent population African American, percent population of Hispanic origin, percent population living in urban areas, percent adherents to "fundamentalist" religion, income per capita, unemployment rate, percent population below poverty level, GINI income distribution coefficient, state revenues per 100,000 residents, public welfare per 100,000 residents, police officers per 100,000 residents, drug arrest rate, corrections expenditures per 100,000 residents, citizen political ideology, government political ideology, governor's party affiliation, and region. Policy variables capture information regarding sentencing structure, drug policy, time served requirements, habitual offender laws (HOL), and mandatory sentences. Specifically, sentencing structure variables include information on determinate sentencing, structured sentencing, presumptive sentencing guidelines, voluntary sentencing guidelines, and presumptive sentencing. Drug policy variables include sentencing enhancement score (cocaine, heroin, and marijuana), severity levels for possession and sale (cocaine, heroin, and marijuana), minimum sentence for 28 grams of cocaine (sale), maximum sentence for the lowest quantity of cocaine (possession), minimum sentence for 28 grams of heroin (sale), maximum sentence for the lowest quantity of heroin (possession), minimum sentence for 500 grams of marijuana (sale), and minimum sentence for the lowest quantity of marijuana (possession). Variables regarding time served requirements include both time served (all offenses) and time served (violent offenses). The habitual offender laws variables capture information regarding the two-strikes law, three-strikes law, HOL targeted for violent offenses, and HOL targeted for drug offenses. Lastly, variables pertaining to mandatory sentences include number of mandatory minimums for weapons use, number of mandatory minimums for violent offenses, number of mandatory minimums for offenses against protected individuals, number of mandatory minimums for offenses committed while in state custody, and mandatory score. The study consisted of two phases completed between November 2002 and March 2004. The first phase of the research involved building a framework for understanding the types of state-level sentencing and corrections policies in use between 1975 and 2002. To do this, researchers reviewed prior analyses of policies to construct an initial outline of policies or general areas and their characteristics. Next, members of the Vera Institute of Justice's National Associates Program on State Sentencing and Corrections (SSC) reviewed the outline, suggested minor changes in the characteristics detailed, and constructed an initial data collection instrument (DCI). This initial DCI microdatabase was pilot-tested by collecting data on three states, refined, and then a finalized version of the DCI was developed for use in the second stage of the study. Phase two of the project consisted of state-level data collection for all 50 states for all study years, 1975 to 2002. The year 1975 was chosen as the cut-off year since, according to most criminologists and practitioners, most of the dramatic changes in state-level sentencing and corrections policies have occurred post-1975. The principal investigators and six research assistants began by analyzing microfiche versions of state codes as amended in 1975. Microfiche versions of superseded state codes (including supplements) and state sessions laws were then used to collect data on changes to each state's code for each year between 1975 and 2002. Data collection generally involved reading the entire criminal law and criminal procedure sections of each state's 1975 code, locating the relevant policy, and recording information about the provisions of the policy into the DCI. Annual code supplements were then analyzed to note changes to each state's code. When a revised version of the entire code was published, data collection then involved reviewing the entire criminal law and criminal procedure sections of each state's code again. Where changes to policies were unclear from annual supplements, microfiche versions of state sessions laws were consulted, which provided the actual legislation altering the code. This process continued until data collection reached 2002, and analysis turned to the bound versions of state codes as amended in 2002. In order to assess the impacts of state-level sentencing and corrections policies in the United States implemented between 1975 and 2002 on state incarceration rates during that same time period, researchers conducted a two-phase study between November 2002 a...

Mortality rate from firearms includes homicides, suicides, accidental deaths, deaths by law enforcement, and deaths for which intent was undetermined. Mortality rate is based on the location of residence and has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify firearm deaths are W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, and U01.4. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Violence is a public health crisis in the US, with gun violence being a major driver. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults (ages 15-34 years), males, and Black individuals most disproportionately impacted. Firearm-related suicides disproportionately impact older, White men. Comprehensive prevention strategies should work to address underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Special Policy Areas in Hackney - effective from 1st August 2018.

The effect of adopting a special policy, which the Council refers to as a Special Policy Area (‘SPA’), is to create a rebuttable presumption so that applications for new premises licences and club premises certificates or variations of these authorisations, which are likely to add to the existing cumulative impact will normally be refused or subject to certain limitations, following relevant representations being made. The applicant will need to demonstrate in their operating schedule that there will be no negative cumulative impact on one or more of the licensing objectives in order to rebut any such presumption.

The boundaries have been created over OS MasterMap based on several factors such us crimes rates, ambulance and emergency services, noise, cost benefit analysis and behaviour studies. This insight was used to propose two Special Policy Areas which were consulted and reviewed.