In the United States, Black people have higher rates of gun homicide than White people across all age groups. As of 2022, gun homicide rates were highest among Black people aged between 15 and 24 years, at ***** gun homicides per 100,000 of the population. In comparison, there were only **** gun homicides per 100,000 of the White population within this age range. However, the risk for gun homicide was greatest among all adolescents and adults between the ages of 15 to 44 in that year. The impact of guns on young Americans In the last few years, firearms have become the leading cause of death for American children and teenagers aged one to 19 years old, accounting for more deaths than car crashes and diseases. School shootings also remain on the rise recently, with the U.S. recording ** times as many school shootings than other high-income nations from 2009 to 2018. Black students in particular experience a disproportionately high number of school shootings relative to their population, and K-12 teachers at schools made up mostly of students of color are more likely to report feeling afraid that they or their students would be a victim of attack or harm. The right to bear arms Despite increasingly high rates of gun-related violence, gun ownership remains a significant part of American culture, largely due to the fact that the right to bear arms is written into the U.S. Constitution. Although firearms are the most common murder weapon used in the U.S., accounting for approximately ****** homicides in 2022, almost **** of American households have at least one firearm in their possession. Consequently, it is evident that firearms remain easily accessible nationwide, even though gun laws may vary from state to state. However, the topic of gun control still causes political controversy, as the majority of Republicans agree that it is more important to protect the right of Americans to own guns, while Democrats are more inclined to believe that it is more important to limit gun ownership.

These data assess the effects of the risk of local jail incarceration and of police aggressiveness in patrol style on rates of violent offending. The collection includes arrest rates for public order offenses, size of county jail populations, and numbers of new prison admissions as they relate to arrest rates for index (serious) crimes. Data were collected from seven sources for each city. CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: SUMMARY TAPE FILE 1A (ICPSR 7941), provided county-level data on number of persons by race, age, and age by race, number of persons in households, and types of households within each county. CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: SUMMARY TAPE FILE 3A (ICPSR 8071), measured at the city level, provided data on total population, race, age, marital status by sex, persons in household, number of households, housing, children, and families above and below the poverty level by race, employment by race, and income by race within each city. The Federal Bureau of Investigation (FBI) 1980 data provided variables on total offenses and offense rates per 100,000 persons for homicides, rapes, robbery, aggravated assault, burglary, larceny, motor vehicle offenses, and arson. Data from the FBI for 1980-1982, averaged per 100,000, provided variables for the above offenses by sex, age, and race, and the Uniform Crime Report arrest rates for index crimes within each city. The NATIONAL JAIL CENSUS for 1978 and 1983 (ICPSR 7737 and ICPSR 8203), aggregated to the county level, provided variables on jail capacity, number of inmates being held by sex, race, and status of inmate's case (awaiting trial, awaiting sentence, serving sentence, and technical violations), average daily jail populations, number of staff by full-time and part-time, number of volunteers, and number of correctional officers. The JUVENILE DETENTION AND CORRECTIONAL FACILITY CENSUS for 1979 and 1982-1983 (ICPSR 7846 and 8205), aggregated to the county level, provided data on the number of individuals being held by type of crime and sex, as well as age of juvenile offenders by sex, average daily prison population, and payroll and other expenditures for the institutions.

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

The Family Violence Prevention and Services Act of 1984 (FVPSA) provided funding, through the Office of Victims of Crime in the United States Department of Justice, for 23 law enforcement training projects across the nation from 1986 to 1992. FVPSA was enacted to assist states in (1) developing and maintaining programs for the prevention of family violence and for the provision of shelter to victims and their dependents and (2) providing training and technical assistance for personnel who provide services for victims of family violence. The National Institute of Justice awarded a grant to the Urban Institute in late 1992 to evaluate the police training projects. One of the program evaluation methods the Urban Institute used was to conduct surveys of victims in New York and Texas. The primary objectives of the survey were to find out, from victims who had contact with law enforcement officers in the pre-training period and/or in the post-training period, what their experiences and evaluations of law enforcement services were, how police interventions had changed over time, and how the quality of services and changes related to the police training funded under the FVPSA. Following the conclusion of training, victims of domestic assault in New York and Texas were surveyed through victim service programs across each state. Similar, but not identical, instruments were used at the two sites. Service providers were asked to distribute the questionnaires to victims of physical or sexual abuse who had contact with law enforcement officers. The survey instruments were developed to obtain information and victim perceptions of the following key subject areas: history of abuse, characteristics of the victim-abuser relationship, demographic characteristics of the abuser and the victim, history of law enforcement contacts, services received from law enforcement officers, and victims' evaluations of these services. Variables on history of abuse include types of abuse experienced, first and last time physically or sexually abused, and frequency of abuse. Characteristics of the victim-abuser relationship include length of involvement with the abuser, living arrangement and relationship status at time of last abuse, number of children the victim had, and number of children at home at the time of last abuse. Demographic variables provide age, race/ethnicity, employment status, and education level of the abuser and the victim. Variables on the history of law enforcement contacts and services received include number of times law enforcement officers were called because of assaults on the victim, number of times law enforcement officers actually came to the scene, first and last time officers came to the scene, number of times officers were involved because of assaults on the victim, number of times officers were involved in the last 12 months, and type of law enforcement agencies the officers were from. Data are also included on city size by population, city median household income, county population density, county crime rate, and region of state of the responding law enforcement agencies. Over 30 variables record the victims' evaluations of the officers' responsiveness, helpfulness, and attitudes.

Abstract (en): The National Crime Victimization Surveys (NCVS) series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes cover rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes cover burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft) as well as personal crimes against him- or herself. The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations. The data files include three weight variables: household, person, and incident. To use the weights correctly they must be adjusted. See the codebook for information on how to adjust the weights to calculate household, population, and victimization estimates. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Checked for undocumented or out-of-range codes.. All persons in the United States aged 12 and over. Smallest Geographic Unit: Region Stratified multistage cluster sample. 2006-01-18 File CB03140-ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-04-07 All concatenated incident-level files and rape subset files have been updated. These updates were made because of a previous change to the 1994 full hierarchical file relating to quarters 1 and 2 of 1995.2004-09-02 The Bureau of Justice Statistics has resupplied the 2000 data. The structures of the data files have not changed, but the content of all four data files has been updated. SAS and SPSS data definition statements have been updated, and the codebook has been modified to reflect these changes.2002-05-21 The data collection was updated to include a file creation date variable and to correct the values for variables V2120 and V2121 pertaining to public housing. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. face-to-face interview, computer-assisted telephone interview (CATI)2008-12-17 This data collection has been deaccessioned and is no longer available. Replaced by study 22921.Through 1999, the NCVS data were maintained under a single study number. Beginning with the year 2000 data, files from individual years have separate study numbers.The NCVS data are organized by year, with six collection quarters comprising an annual file: the four quarters of the current year plus the first two quarters of the following year.The number of records and variables for each file, as well as the logical record length, can be found in the codebooks.Incident-Level files were created from the annual hierarchical files and include information on victims rather than nonvictims. There are three types of Incident-Level files: single year, concatenated annual, and concatenated rape subset. In all t...

This project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

These data were collected to examine the relationships among crime rates, residents' attitudes, physical deterioration, and neighborhood structure in selected urban Baltimore neighborhoods. The data collection provides both block- and individual-level neighborhood data for two time periods, 1981-1982 and 1994. The block-level files (Parts 1-6) include information about physical conditions, land use, people counts, and crime rates. Parts 1-3, the block assessment files, contain researchers' observations of street layout, traffic, housing type, and general upkeep of the neighborhoods. Part 1, Block Assessments, 1981 and 1994, contains the researchers' observations of sampled blocks in 1981, plus selected variables from Part 3 that correspond to items observed in 1981. Nonsampled blocks (in Part 2) are areas where block assessments were done, but no interviews were conducted. The "people counts" file (Part 4) is an actual count of people seen by the researchers on the sampled blocks in 1994. Variables for this file include the number, gender, and approximate age of the people seen and the types of activities they were engaged in during the assessment. Part 5, Land Use Inventory for Sampled Blocks, 1994, is composed of variables describing the types of buildings in the neighborhood and their physical condition. Part 6, Crime Rates and Census Data for All Baltimore Neighborhoods, 1970-1992, includes crime rates from the Baltimore Police Department for aggravated assault, burglary, homicide, larceny, auto theft, rape, and robbery for 1970-1992, and census information from the 1970, 1980, and 1990 United States Censuses on the composition of the housing units and the age, gender, race, education, employment, and income of residents. The individual-level files (Parts 7-9) contain data from interviews with neighborhood leaders, as well as telephone surveys of residents. Part 7, Interviews with Neighborhood Leaders, 1994, includes assessments of the level of involvement in the community by the organization to which the leader belongs and the types of activities sponsored by the organization. The 1982 and 1994 surveys of residents (Parts 8 and 9) asked respondents about different aspects of their neighborhoods, such as physical appearance, problems, and crime and safety issues, as well as the respondents' level of satisfaction with and involvement in their neighborhoods. Demographic information on respondents, such as household size, length of residence, marital status, income, gender, and race, is also provided in this file.

This special topic poll, conducted April 30 to May 6, 1996, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. This poll sought Americans' views on the most important problems facing the United States, their local communities and their own families. Respondents rated the public schools, crime, and drug problems at the national and local levels, their level of optimism about their own future and that of the country, and the reasons they felt that way. Respondents were asked whether they were better off financially than their parents were at their age, whether they expected their own children to be better off financially than they were, and whether the American Dream was still possible for most people. Respondents then compared their expectations about life to their actual experiences in areas such as job security, financial earnings, employment benefits, job opportunities, health care benefits, retirement savings, and leisure time. A series of questions asked whether the United States was in a long-term economic and moral decline, whether the country's main problems were caused more by a lack of economic opportunity or a lack of morality, and whether the United States was still the best country in the world. Additional topics covered immigration policy and the extent to which respondents trusted the federal, state, and local governments. Demographic variables included respondents' sex, age, race, education level, marital status, household income, political party affiliation, political philosophy, voter registration and participation history, labor union membership, the presence of children in the household, whether these children attended a public school, and the employment status of respondents and their spouses. telephone interviewThe data available for download are not weighted and users will need to weight the data prior to analysis.The data collection was produced by Chilton Research Services of Radnor, PA. Original reports using these data may be found via the ABC News Polling Unit Website.According to the data collection instrument, code 3 in the variable Q909 (Education Level) included respondents who answered that they had attended a technical school.The original data file contained four records per case and was reformatted into a data file with one record per case. To protect respondent confidentiality, respondent names were removed from the data file.The CASEID variable was created for use with online analysis. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was at home at the time of interview. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Datasets: DS1: ABC News Listening to America Poll, May 1996

This survey focuses on crime. Issues addressed include media coverage of crime, criminal justice programs on television ('COPS,' 'Justice Files'), opinions of the neighborhood respondents live in, worrying about being a victim of crime, ability of the police (to protect, solve and prevent crime) opinions of community courts, sentencing, rehabilitation of criminals, the death penalty, gun control, legalization of marijuana, juvenile crime, and drug use. Demographic data include marital status, party affiliation, political ideology, education, religious preference, race, sex, age, and income.

The aim of this data collection was to gauge the impact of legalized casino gambling on the level and spatial distribution of crime in the Atlantic City region by comparing crime rates before and after the introduction of this type of gambling in the area. Data for the years 1972 through 1984 were collected from various New Jersey state publications for 64 localities and include information on population size and density, population characteristics of race, age, per capita income, education and home ownership, real estate values, number of police employees and police expenditures, total city expenditure, and number of burglaries, larcenies, robberies and vehicle thefts. Spatial variables include population attributes standardized by land area in square miles, and measures of accessibility, location, and distance from Atlantic City. For the 1970/1980 data file, additional variables pertaining to population characteristics were created from census data to match economic and crime attributes found in the 1972-1984 data. Data on eight additional locations are available in the 1970/1980 file.

Abstract

The concept of victimisation surveys (also known as International Crime Victim Survey (ICVS)) is well established in South Africa (SA) and internationally. Until recently the United Nations Interregional Crime and Justice Research Institute (UNICRI) coordinated and sometimes conducted the ICVS in developing countries. During the past two decades a number of surveys related to crime, crime victims and users of services provided by the safety and security cluster departments have been conducted by various service providers in South Africa. Besides these surveys, three national VOCS have been conducted. The first of these was the Victims of Crime Survey conducted in 1998 by Statistics South Africa. This survey was based on the ICVS questionnaire developed by UNICRI, with adjustments made for local conditions. The Institute for Security Studies (ISS) was responsible for conducting subsequent versions of the VOCS, the National Victimes of Crime Survey 2003 and the Victim Survey 2007.

Starting with the Victims of Crime Survey 2011, Statistics SA plans to conduct the VOCS annually. The ‘new’ Victims of Crime Survey (VOCS) series is a countrywide household-based survey and examines three aspects of crime:

• The nature, extent and patterns of crime in South Africa, from the victim’s perspective; • Victim risk and victim proneness, so as to inform the development of crime prevention and public education programmes; • People’s perceptions of services provided by the police and the courts as components of the criminal justice system.

The VOCS 2011 is comparable to the VOCS 1998, VOCS 2003 and VOCS 2007 in cases where the questions remained largely unchanged. However, it is important to note that the sample size for the VOCS 2011 is much bigger than any of the preceding surveys, and the data should be considered more reliable than the earlier surveys especially at lower levels of disaggregation.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the study were individuals and households

Universe

The target population of the survey consisted of all private households in all nine provinces of South Africa and residents in workers' hostels. The survey did not cover other collective living quarters such as students' hostels, old-age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the VOCS 2011 was based on a master sample (MS) originally designed as the sampling frame for the Quarterly Labour Force Survey (QLFS). The MS is based on information collected during the 2001 Population Census conducted by Stats SA. The MS has been developed as a general-purpose household survey frame that can be used by all household-based surveys, irrespective of the sample size requirement of the survey. The VOCS 2011, like all other household-based surveys, uses a MS of primary sampling units (PSUs) which comprises census enumeration areas (EAs) that are drawn from across the country.

The sample for the VOCS 2011 used a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The sample was designed to be representative at provincial level. A self-weighting design at provincial level was used and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income. A randomised probability proportional to size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. The sample size of 3 080 PSUs was selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the inverse sampling ratios (ISR) of each PSU. The sample size for the VOCS 2011 is 29 754 dwelling units.

Mode of data collection

Face-to-face [f2f]

Research instrument

The VOCS 2011 questionnaire was based on the questionnaires used in the International Crime Victim Survey (ICVS) and previous VOCSs conducted by the Institute for Security Studies (ISS) and Statistics SA. The questions are covered in 27 sections and deal with the following topics:

Flap Demographic information (name, sex, age, population group, etc.) Section 1 Household-specific characteristics (education, economic activities and household income sources Section 2 Beliefs about crime Section 3 Individual and community response to crime Section 4 Victim support and other interventions Section 5 Citizen interaction or community cohesion Section 6 Perception of the police service Section 7 Perception of the courts Section 8 Perception of correctional services Section 9 Corruption experienced by the respondent Section 10 Experience of household crime (screening table) Section 11 Theft of car experienced by a household member(s) in the previous 12 months Section 12 Housebreaking or burglary when no one was at home in the previous 12 months Section 13 Theft of livestock, poultry and other animals in the previous 12 months Section 14 Theft of crops planted by the household in the previous 12 months Section 15 Murder experienced by a household member(s) in the past 12 months Section 16 Theft out of a motor vehicle experienced by a household member(s) in the previous 12 months Section 17 Deliberate damaging/burning or destruction of dwelling experienced by a household member(s) in the previous 12 months Section 18 Motor vehicle vandalism or deliberate damage of a motor vehicle experienced by a household member(s) in the previous 12 months Section 19 Home robbery (including robbery often around or inside the household’s dwelling) experienced by a household member(s) in the previous 12 months

Sections 20–27 of this questionnaire required that an individual be randomly selected from the household to respond to questions classified as individual crimes. The methodology used was to select a person 16 years or older, whose birthday was the first to follow the survey date. These sections collected data on:

Section 20 Experiences of individual crimes (screening table) in the past 5 years and in the previous 12 months Section 21 Theft of bicycle experienced in the previous 12 months Section 22 Theft of motorbike or scooter experienced in the past 12 months Section 23 Car hijacking (including attempted hijacking) experienced in the previous 12 months Section 24 Robbery (including street robberies and other non-residential robberies, excluding car or truck hijackings, and home robberies) experienced in the previous 12 months Section 25 Assault experienced in the previous 12 months Section 26 Sexual offences (including rape) experienced in the previous 12 months Section 27 Consumer fraud experienced by the individual experienced in the previous 12 months All sections Comprehensive coverage of all aspects of domestic tourism and expenditure

The final data files correspond to sections of the questionnaireas follows:

Person: Data from Flap and Section 1 (excluding Section 1.6 and 1.7) Household: Data from Section 1.7 and Section 10-19 Section 20-27: Data from Section 20-27

The VOCS 2011 is comparable to the previous VOCSs in that several questions have remained unchanged over time. Where possible, it was generally indicated in the report. However, it must be noted that the VOCS 2011 sample size was more than double of the previous surveys. The current survey can thus provide more accurate estimates than the previous surveys, for example at provincial level and for domain variables, such as gender and race. Caution should be exercised when running cross tabulation of different crimes by province and other variables as in most cases the reported cases were too few for this type of analysis.

Cleaning operations

Capture was undertaken on Epi-Info. A process of double capture was undertaken in order to eliminate capture error.

Investigator(s): Bureau of Justice Statistics The National Crime Victimization Survey (NCVS) series was designed to achieve three primary objectives: to develop detailed information about the victims and consequences of crime, to estimate the number and types of crimes not reported to police, and to provide uniform measures of selected types of crime. All persons in the United States 12 years of age and older were interviewed in each household sampled. Each respondent was asked a series of screen questions to determine if he or she was victimized during the six-month period preceding the first day of the month of the interview. Screen questions cover the following types of crimes, including attempts: rape, robbery, assault, burglary, larceny, and motor vehicle theft. The data include type of crime; severity of the crime; injuries or losses; time and place of occurrence; medical expenses incurred; number, age, race, and sex of offender(s); and relationship of offender(s) to the victim (stranger, casual acquaintance, relative, etc.). Demographic information on household members includes age, sex, race, education, employment, median family income, marital status, and military history. A stratified multistage cluster sample technique was employed, with the person-level files consisting of a full sample of victims and a 10 percent sample of nonvictims for up to four incidents. The NCVS data are organized by collection quarter, and six quarters comprise an annual file. For example, for a 1979 file, the four quarters of 1979 are included as well as the first two quarters of 1980. NACJD has prepared a resource guide on NCVS. Years Produced: Updated annually

The ward profiles and ward atlas provide a range of demographic and related data for each ward in Greater London. They are designed to provide an overview of the population in these small areas by presenting a range of data on the population, diversity, households, life expectancy, housing, crime, benefits, land use, deprivation, and employment. Indicators included here are population by age and sex, land area, projections, population density, household composition, religion, ethnicity, birth rates (general fertility rate), death rates (standardised mortality ratio), life expectancy, average house prices, properties sold, housing by council tax band, tenure, property size (bedrooms), dwelling build period and type, mortgage and landlord home repossession, employment and economic activity, Incapacity Benefit, Housing Benefit, Household income, Income Support and JobSeekers Allowance claimant rates, dependent children receiving child-tax credits by lone parents and out-of-work families, child poverty, National Insurance Number registration rates for overseas nationals (NINo), GCSE results, A-level / Level 3 results (average point scores), pupil absence, child obesity, crime rates (by type of crime), fires, ambulance call outs, road casualties, happiness and well-being, land use, public transport accessibility (PTALs), access to public greenspace, access to nature, air emissions / quality, car use, bicycle travel, Indices of Deprivation, and election turnout. The Ward Profiles present key summary measures for the most recent year, using both Excel and InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place. The Ward Atlas presents a more detailed version of the data including trend data and generally includes the raw numbers as opposed to percentages or rates. The Instant Atlas reports use HTML5 technology, which can be used in modern browsers, including on Apple machines, but will not function on older browsers. WARD PROFILES Compare the ward measure against the Borough, London and National average. WARD ATLAS Access the raw data for all London wards. WARD ATLAS FOR 2014 BOUNDARIES In May 2014, ward boundaries changed in Hackney, Kensington and Chelsea, and Tower Hamlets. This version of the ward atlas gives data for these new wards, as well as retaining data on the unchanged wards in the rest of London for comparison purposes. Data for boroughs has also been included. Very few datasets have been published for the new ward boundaries, so the majority of data contained in this atlas have been modelled using a method of proportion of households from the old boundaries that are located in the new boundaries. Therefore, the data contained in this atlas are indicative only. Instant Atlas for 2014 Ward Atlas Tips: - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data. - To view data just for one borough*, use the filter tool. - Some legend settings can be altered by clicking on the cog icon next to the Wards tick box within the map legend. - The wards can be ranked in order by clicking at the top of the indicator column of the data table. Note: Additional indicator information and sources are included within the spreadsheet and Instant Atlas report. OTHER SMALL AREA PROFILES Other profiles available include Borough, LSOA and MSOA atlases. Data from these profiles were used to create the Well-being scores tool. *The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster. These profiles were created using the most up to date information available at the time of collection (September 2015).

In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.

These data were gathered to test a model of the socioeconomic and demographic determinants of the crime of arson. Datasets for this analysis were developed by the principal investigator from records of the Massachusetts Fire Incident Reporting System and from population and housing data from the 1980 Census of Massachusetts. The three identically-structured data files include variables such as population size, fire incident reports, employment, income, family structure, housing type, housing quality, housing occupancy, housing availability, race, and age.

This study sought to answer the question: If a woman is experiencing intimate partner violence, does the collective efficacy and community capacity of her neighborhood facilitate or erect barriers to her ability to escape violence, other things being equal? To address this question, longitudinal data on a sample of 210 abused women from the CHICAGO WOMEN'S HEALTH RISK STUDY, 1995-1998 (ICPSR 3002) were combined with community context data for each woman's residential neighborhood taken from the Chicago Alternative Policing Strategy (CAPS) evaluation, LONGITUDINAL EVALUATION OF CHICAGO'S COMMUNITY POLICING PROGRAM, 1993-2000 (ICPSR 3335). The unit of analysis for the study is the individual abused woman (not the neighborhood). The study takes the point of view of a woman standing at a street address and looking around her. The characteristics of the small geographical area immediately surrounding her residential address form the community context for that woman. Researchers chose the police beat as the best definition of a woman's neighborhood, because it is the smallest Chicago area for which reliable and complete data are available. The characteristics of the woman's police beat then became the community context for each woman. The beat, district, and community area of the woman's address are present. Neighborhood-level variables include voter turnout percentage, organizational involvement, percentage of households on public aid, percentage of housing that was vacant, percentage of housing units owned, percentage of feminine poverty households, assault rate, and drug crime rate. Individual-level demographic variables include the race, ethnicity, age, marital status, income, and level of education of the woman and the abuser. Other individual-level variables include the Social Support Network (SSN) scale, language the interview was conducted in, Harass score, Power and Control score, Post-Traumatic Stress Disorder (PTSD) diagnosis, other data pertaining to the respondent's emotional and physical health, and changes over the past year. Also included are details about the woman's household, such as whether she was homeless, the number of people living in the household and details about each person, the number of her children or other children in the household, details of any of her children not living in her household, and any changes in the household structure over the past year. Help-seeking in the past year includes whether the woman had sought medical care, had contacted the police, or had sought help from an agency or counselor, and whether she had an order of protection. Several variables reflect whether the woman left or tried to leave the relationship in the past year. Finally, the dataset includes summary variables about violent incidents in the past year (severity, recency, and frequency), and in the follow-up period.