A number of studies have explored the relationship between public housing policy, poverty, and crime. This Commentary discusses the results of a recent study, which investigated the effects of closing large public housing developments on crime. To see if the demolitions—and the associated deconcentration of poverty—reduced crime or merely displaced it, researchers examined the case of Chicago. They found that closing large public housing developments and dispersing former residents throughout a wider portion of the city was associated with net reductions in violent crime, at the city level.

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 dataset contains a selection of 27 indicators of public health significance by Chicago community area, with the most updated information available. The indicators are rates, percents, or other measures related to natality, mortality, infectious disease, lead poisoning, and economic status. See the full description at https://data.cityofchicago.org/api/assets/BB7058D2-E8A1-4E11-86CE-6CF1738F0A02.

This statistic shows the rate of violent crime victimization in the United States from 2008 to 2012 distinguished by poverty level. Between 2008 and 2012, people in high-income households had the lowest rate of violent victimization. About ** per 1,000 people in high income households became victims of a violent crime between 2008 and 2012.

This compilation of data, which was gathered from a variety of federal agencies and private organizations, provides information for the United States as a whole, the 50 states and the District of Columbia, and 3,141 counties and county equivalents (defined as of April 24, 1989). Data are included for the following general areas: age, ancestry, agriculture, banking, business, construction, crime, education, elections, government, health, households, housing, labor, land area, manufactures, money income, personal income, population, poverty, retail trade, service industries, social insurance and human services, veterans, vital statistics, wholesale trade, and journey to work.

This statistic shows the percentage of violent crime reported to police in the United States from 2008 to 2012, by type of crime and poverty level. Between 2008 and 2012, about ** percent of violent crime against persons in poor households were reported to police.

In 2025, Pietermaritzburg in South Africa ranked as the world's most dangerous city with a crime rate of 82 per 100,000 inhabitants. Five of the 10 cities with the highest crime rates worldwide are found in South Africa. The list does not include countries where war and conflict exist. South Africa dominates crime statistics When looking at crime rates, among the 10 most dangerous cities in the world, half of them are found in South Africa. The country is struggling with extremely high levels of inequality, and is struggling with high levels of crime and power outages, harming the country's economy and driving more people into unemployment and poverty. Crime in Latin America On the other hand, when looking at murder rates, Latin America dominates the list of the world's most dangerous countries. Violence in Latin America is caused in great part by drug trafficking, weapons trafficking, and gang wars.

This research is an exploration of a spatial approach to identify the contexts of unemployment-crime relationships at the county level. Using Exploratory Spatial Data Analysis (ESDA) techniques, the study explored the relationship between unemployment and property crimes (burglary, larceny, motor vehicle theft, and robbery) in Virginia from 1995 to 2000. Unemployment rates were obtained from the Department of Labor, while crime rates were obtained from the Federal Bureau of Investigation's Uniform Crime Reports. Demographic variables are included, and a resource deprivation scale was created by combining measures of logged median family income, percentage of families living below the poverty line, and percentage of African American residents.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

Context

Wanted to study does following factors impacted US president elections. I tried to analyse 6 states data out of which four won by democrats and 6 by republicans. The factors are . Education,Health,Poverty/Income,Race(%of whites),Median Age etc.

Content

Here are the columns that are being analyzed.

Average of Republicans 2016 Average of Democrats 2016 Difference(Rep-Dem) Average of Poverty.Rate.below.federal.poverty.threshold Average of Graduate Degree Average of Median Earnings 2010 Average of Gini.Coefficient Average of White (Not Latino) Population Average of School Enrollment Average of Infant.mortality Average of Unemployment Average of median_age Average of Violent.crime

Acknowledgements

Thanks to Opendatasoft https://data.opendatasoft.com/explore/dataset/usa-2016-presidential-election-by-county%40public/information/ for providing data at county level. Data has been rolled up to state level.

Inspiration

Well does perceptions are backed by data or not.

In 2023, 11.7 percent of Montana's population lived below the poverty line. This is a slight decrease from the previous year, when 12.1 percent of Montana's population lived below the poverty line. The poverty rate of the United States can be found here.

Content

Hate Crimes

This folder contains data behind the story Higher Rates Of Hate Crimes Are Tied To Income Inequality.

Sources: Kaiser Family Foundation Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation United States Elections Project Southern Poverty Law Center FBI

Correction

Please see the following commit: https://github.com/fivethirtyeight/data/commit/fbc884a5c8d45a0636e1d6b000021632a0861986

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.

Violent Crime in CA

Regional, County, City/Town Rates 2006-2010

By Health [source]

About this dataset

This dataset contains information on the rate of violent crime across California - its regions, counties, cities and towns. The data was collected as part of a larger effort by the Office of Health Equity to better understand public health indicators and ensure equitable outcomes for all.

The numbers reflect more than just a problem in California communities - it reflects a problem with unequal access to resources and opportunity across race, ethnicities and geographies. African Americans in California are 11 times more likely to die from assault or homicide compared to white Californians. Similarly, certain regions report higher crime rates than others at the county level- indicating underlying issues with poverty or institutionalized inequality.

Law enforcement agencies teamed up with the Federal Bureau of Investigations’ Uniform Crime Reports to collect this data table which includes details such as reported number of violent crimes (numerator), population size (denominator), rate per 1,000 population (ratex1000) confidence intervals (LL_95CI & UL_95CI ) standard errors & relative standard errors (se & rse) as well as ratios between city/town rates vs state rates (RR_city2state). Additionally, each record is classified according to region name/code and race/ethnicity code/name , giving researchers further insight into these troubling statistics at both macro and micro levels.

Armed with this information we can explore new ways identify inequitable areas and begin looking for potential solutions that combat health disparities within our communities like never before!

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How to use the dataset

The data is presented with twenty columns providing various segments within each row including:

• Crime definition
• Race/ethnicity code
• Region code
• Geographic area identifier
• Numerator and Denominator values of population
• Standard Error and 95% Confidence Intervals
• Relatvie Standard Error (RSE) value

• Ratios related to city/towns rate to state rate

  The information provided can be used for a variety of applications such as creating visualizations or developing predictive models. It is important to note that rates are expressed per 1,000 population for their respective geographic area during each period noted by the report year field within the dataset. Additionally CA_decile column may be useful in comparing counties due numerical grading system identifying a region’s percentile ranking when compared to other counties within the current year’s entire dataset as well as ratios present under RR_city2state which presents ratio comparison between city/town rate and state rate outside given geographic area have made this an extremely valuable dataset for further analysis

Research Ideas

• Developing a crime prediction and prevention program that uses machine learning models to identify criminal hotspots and direct resources to those areas
• Exploring the connection between race/ethnicity and rates of violence in California
• Creating visualizations and interactive maps to display types of violent crime across different counties within California

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Violent_Crime_Rate_California_2006-2010-DD.csv

File: rows.csv | Column name | Description ...

This dataset supports the manuscript Lawful Handgun Licensing, Population Density, Poverty, Police Staffing, and Violent Crime: A Two-Cohort Comparison of U.S. Jurisdictions (2023–2024). It contains processed violent crime counts, rates per 100,000, population denominators, poverty statistics, and police staffing levels for two cohorts of U.S. jurisdictions. Cohort A includes counties with high levels of licensed handgun ownership (Brevard FL, Macomb MI, Genesee MI, Sumner TN, Sullivan TN, Pierce WA). Cohort B includes restrictive licensing jurisdictions (New York City, Los Angeles, San Francisco, Washington DC, Boston). The package includes: County/city-level violent crime counts for homicide, rape, robbery, and aggravated assault (2023–2024). Population denominators (2024 Census estimates). Derived violent crime rates per 100,000. Poverty measures and law enforcement officer staffing rates. Python replication script to regenerate tables and figures used in the study. Accessed data sources: FBI Crime Data Explorer, Major Cities Chiefs Association, NYPD annual tables, FDLE FIBRS, Michigan MICR, Tennessee Bureau of Investigation, Washington Association of Sheriffs and Police Chiefs, and U.S. Census QuickFacts. Data pulled on August 27, 28, and 30, 2025.

Abstract (en): These data provide official index crime rates and social and economic indicators of crime rates at three levels of aggregation (city, state, and metropolitan areas) for four decennial years: 1950, 1960, 1970, and 1980. Information is provided on Uniform Crime Reports murder, rape, robbery, aggravated assault, burglary, larceny theft, and vehicle theft rates per 100,000 population. Social and economic indicators include percent black population, percent divorced males, the mean and median family incomes, families below the poverty line, and percent unemployed for each area. The availability of the data for the crime rates in 1980 determined the geographic locations included in the data collection. Data from earlier years do not exist for all geographic locations for which data were available in 1980. 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.; Standardized missing values.; Checked for undocumented or out-of-range codes.. 2006-01-18 File CB6151.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Funding insitution(s): National Science Foundation (SES8217865). The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

This map shows a comparable measure of crime in the United States. The crime index compares the average local crime level to that of the United States as a whole. An index of 100 is average. A crime index of 120 indicates that crime in that area is 20 percent above the national average.The crime data is provided by Applied Geographic Solutions, Inc. (AGS). AGS created models using the FBI Uniform Crime Report databases as the primary data source and using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. The crimes included in the models include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. The total crime index incorporates all crimes and provides a useful measure of the relative “overall” crime rate in an area. However, these are unweighted indexes, meaning that a murder is weighted no more heavily than a purse snatching in the computations. The geography depicts states, counties, Census tracts and Census block groups. An urban/rural "mask" layer helps you identify crime patterns in rural and urban settings. The Census tracts and block groups help identify neighborhood-level variation in the crime data.------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

This map shows a comparable measure of crime in the United States. The crime index compares the average local crime level to that of the United States as a whole. An index of 100 is average. A crime index of 120 indicates that crime in that area is 20 percent above the national average.The crime data is provided by Applied Geographic Solutions, Inc. (AGS). AGS created models using the FBI Uniform Crime Report databases as the primary data source and using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. The crimes included in the models include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. The total crime index incorporates all crimes and provides a useful measure of the relative “overall” crime rate in an area. However, these are unweighted indexes, meaning that a murder is weighted no more heavily than a purse snatching in the computations. The geography depicts states, counties, Census tracts and Census block groups. An urban/rural "mask" layer helps you identify crime patterns in rural and urban settings. The Census tracts and block groups help identify neighborhood-level variation in the crime data.------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

This statistic represents the rate of violent victimization in the United States from 2008 to 2012 distinguished by presence of a weapon and poverty level,. Between 2008 and 2012, persons in poor households had a higher rate of violent victimization involving a firearm (*** per 1,000) compared to persons above the FPL.

This data set is used to determine the best U.S state for 2023, by analyzing many factors such as average household income, average home value, crime rate, education, poverty rate, and job opportunities per state.