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 17 per 1,000 people in high income households became victims of a violent crime between 2008 and 2012.

In 2023, the state with the highest crime rate in the United States per 100,000 inhabitants was New Mexico. That year, the crime rate was ******** crimes per 100,000 people. In comparison, New Hampshire had the lowest crime rate at ****** crimes per 100,000 people. Crime rate The crime rate in the United States has generally decreased over time. There are several factors attributed to the decrease in the crime rate across the United States. An increase in the number of police officers and an increase in income are some of the reasons for a decrease in the crime rate. Unfortunately, people of color have been disproportionately affected by crime rates, as they are more likely to be arrested for a crime versus a white person. Crime rates regionally The District of Columbia had the highest rate of reported violent crimes in the United States in 2023 per 100,000 inhabitants. The most common crime clearance type in metropolitan counties in the United States in 2020 was murder and non-negligent manslaughter. The second most dangerous city in the country in 2020 was Detroit. Detroit has faced severe levels of economic and demographic declines in the past years. Not only has the population decreased, the city has filed for bankruptcy. Despite the median household income increasing, the city still struggles financially.

The study contains cross-section data on the relationship between aggregate levels of punishment and crime rates. It examines deterrent effects of punishment on seven Federal Bureau of Investigation (FBI) index crimes: murder, rape, assault, larceny, robbery, burglary, and auto theft, committed in 1960 in 47 states of the United States (excluded were New Jersey, Alaska, and Hawaii). For each state, the data include variables for the reported crime rates for each of the seven index crimes. For each of the index crimes, there are two sanction variables included: the probability of prison commitment and the average time served by those sentenced (severity of punishment). There are 11 socioeconomic variables, including family income, income distribution, unemployment rate for urban males in the age groups 14-24 and 35-39, labor force participation rate, educational level, percentage of young males in population, percentage of non-white young males living in the population, percentage of population living in Standard Metropolitan Statistical Areas, sex ratio, and place of occurrence. The data also include per capita police expenditures for 1959 and 1960. A related data collection is PARTICIPATION IN ILLEGITIMATE ACTIVITIES: EHRLICH REVISITED, 1960 (ICPSR 8677). It provides alternative model specifications and estimations.

The crime rate represents the annual number of reported violent and property crimes per 100,000 inhabitants. What's included in violent crimes are murder, non-negligent manslaughter, rape, robbery and aggravated assault. Property crimes include burglary, larceny-theft and motor vehicle theft. Data is sourced from the Federal Bureau of Investigation.

The National Crime Victimization Survey (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 include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include 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 also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). 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. This version of the NCVS, referred to as the collection year, contains records from interviews conducted in the 12 months of the given year. This dataset represents the revised version of the NCVS on a collection year basis for 2016. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred. The 2016 National Crime Victimization Survey (NCVS) violent and property crime estimates were significantly higher than 2015, but it was not possible to determine the degree to which the change in rates resulted from the sample redesign rather than real changes in U.S. victimization levels. Therefore, the Bureau of Justice Statistics (BJS) examined the 2015 and 2016 victimization rates separately for new and continuing sample counties in the 2016 Criminal Victimization bulletin. The BJS requested that the Census Bureau create a 2016 revised file with outgoing county interviews from July-December 2015, continuing county interviews from January-June 2016, and all interviews (continuing and new counties) from July-December 2016. In other words, the outgoing 2015 cases replaced the new 2016 cases in the first half of 2016. The files in this study serve as a separate research file to allow data users to make comparisons between 2015, 2016, and 2017 NCVS estimates using a nationally representative sample. It provides a sample that still represents the entire country but does not have the inflated crime rates seen in the new counties in 2016.

crime rate per 100K population for 2021 was 10.50, a 0.96% increase from 2020.

<ul style='margin-top:20px;'>

<li> crime rate per 100K population for 2020 was <strong>10.40</strong>, a <strong>1.89% decline</strong> from 2019.</li>
<li> crime rate per 100K population for 2019 was <strong>10.60</strong>, a <strong>0.95% increase</strong> from 2018.</li>
<li> crime rate per 100K population for 2018 was <strong>10.50</strong>, a <strong>1.87% decline</strong> from 2017.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

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?

This statistic shows the rate of violent victimization in the United States between 2008 and 2012 distinguished by victim-offender relationship & poverty level. During 2008 to 2012, the rate of intimate partner violence for persons in poor households (8.1 per 1,000) was almost double the rate for low-income persons (4.3 per 1,000) and almost four times the rate for high-income persons (2.1 per 1,000).

This article provides empirical evidence that good political governance and good economic policies can lower homicide rates. Therefore, violent crime is not simply determined by modernization, population characteristics, and cultural factors. This result follows from rigorous econometric testing based on a cross-national panel of homicide data from up to 117 countries over the period 1980–97. Contrary to most existing studies, which have applied ordinary least squares on data drawn from one time period only, this analysis uses a fixed-effects estimator with fully robust standard errors. A fixed-effects estimator elegantly controls for time-invariant determinants, such as cultural factors, and allows the pooling of homicide data from otherwise incompatible sources. This is complemented by random-effects estimation in sensitivity analysis. The results suggest that economic growth, higher income levels, respect for human rights, and the abolition of the death penalty are all associated with lower homicide rates. The same is true for democracy at high levels of democracy. The transition from autocracy to democracy is likely to be accompanied by a rising homicide rate, however, until full democracy has been reached. Results also indicate that policies aimed at improving equity have no effect on violent crime. In particular, there is evidence that the positive effect of income inequality on homicide rates found in many studies might be spurious. The results reported here are strikingly similar to those found for the causes of civil war.

This article argues that the link between income inequality and violent property crime might be spurious, complementing a similar argument in prior analysis by the author on the determinants of homicide. In contrast, Fajnzylber, Lederman & Loayza provide seemingly strong and robust evidence that inequality causes a higher rate of both homicide and robbery/violent theft, even after controlling for country-specific fixed effects. The results in the present article suggest that inequality is not a statistically significant determinant, unless either country-specific effects are not controlled for or the sample is artificially restricted to a small number of countries. The reason for the link between inequality and violent property crime being spurious is that income inequality is likely to be strongly correlated with country-specific fixed effects, such as cultural differences. A high degree of inequality might be socially undesirable for any number of reasons, but that it causes violent crime is far from proven.

Data was collected for each of the 50 states from the year 2019. For each state, the following information is given: total population, total White population, total Black population, total Hispanic population, median household income, total prison population, total parole population, total amount of law enforcement employees, the violent crime rate, and the GDP.

Selection of time series of different scientific publications and of publication of the official statistics:

EUROSTAT, European Statistical Office OECD: Organisation for Economic Cooperation and Development; ONS: Office for National Statistics, England; SCB: Statistiska Centralbyran, Sweden; Federal Statistical Office, Wiesbaden. Deutschland; WHO: World Health Organization.

In 2024, there were approximately 1.9 homicides per 100,000 inhabitants in El Salvador. Since 2015, when it stood at 103, the murder rate has been dropping annually in this Central American country. Crime current state The region has witnessed a substantial reduction in the number of homicides since 2015, resulting in the most common crimes becoming increasingly more centered on non-lethal offenses and material-related transgressions, which now pose the most prevalent threats. This shift is equally apparent across both genders, with the rate of femicides steadily declining, paralleling a consistent decrease in overall victimization rates. Consequently, El Salvador achieved the ranking of the third safest country within the Latin American homicide rate context. Notwithstanding these notable improvements, a lingering sense of caution endures among the populace, as nearly half of them remain apprehensive about the prospect of falling victim to criminal activities. Main economic problems Following an extended phase marked by elevated inflation, the region continues to grapple with challenges in its efforts to recover. The impact has been most pronounced on the prices of essential food items, rendering them increasingly unaffordable for a population where approximately 20 percent live under poverty conditions. Furthermore, the unemployment rate persists, with one out of every two individuals still seeking employment opportunities. A significant proportion, approximately 60 percent, remain apprehensive about job loss, recognizing the subsequent loss of their primary income source. In response, the government is envisaging an enhancement in both the employment rate and the GDP, albeit with a gradual recovery trajectory following the substantial downturn experienced during the COVID-19 pandemic.

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 51 percent of violent crime against persons in poor households were reported to police.

The global crime analytics software market is expected to grow at CAGR of 8.2% for the forecast period 2023-2030.

Growing demand for effective crime prevention and reduction techniques due to rising crime rate is expected to drive the growth of the crime analytics software market
North America dominates the crime analytics software market

Factors Affecting Crime Analytics Software Market Growth

Growing demand for effective crime prevention and reduction techniques due to rising crime rate; 

The increasing crime rates and growing demand for effective crime prevention and reduction techniques primarily drives the demand for crime analytics software. In recent years, the rise in crime rates has become a major concern for many countries around the world. Murder cases, terrorism, cybercrime has increases drastically in in last few years due to the growing inflation and unemployment. With the development of online and mobile technologies, cybercrime is becoming more and more common. According to the study, about 40% of internet users worldwide experienced cybercrime in 2022. As crime rates increase, law enforcement agencies and other organizations recognize the need to make informed decisions based on data. Traditional reactive policing methods are often insufficient to address rising crime rates due to improper recording of data and information. In such cases, crime analytics software helps to collect, analyse, and visualize crime-related data, enabling law enforcement professionals to understand crime patterns, allocate resources, and develop targeted strategies.

In 2021, a total of 60,96,310 crimes, comprising 36,63,360 Indian Penal Code (IPC) crimes and 24,32,950 Special and Local Laws (SLL) crimes were registered in India.

(Source: Crime in India - Wikipedia)

In a report released in July 2023, Statistics Canada researchers found that violent crime rose by five per cent in 2022, reaching its highest point since 2007.

(Source: Police-reported crime is on the rise again, with violent crime at its highest since 2007 | CBC News)

Crime Analytics Software Market Restraint:

Budgetary restrictions faced by law enforcement organizations.

Crime analytics software often requires initial investment for licensing, infrastructure, and training. Law enforcement agencies with tight budgets might find it difficult to allocate funds for such expenses, leading to hesitation in adopting these solutions. Besides the initiation cost, crime analytics software also requires technology upgrades and software purchases. This might hamper the adoption of crime analytics software in police stations and investigation agencies.

Crime Analytics Software Market Opportunity:

Technology development as an opportunity for the market.

New technological innovations have been developed to prevent crime and to improve the performance of the police. Innovations in criminal justice technology can be categorised into hard technology comprises hardware & components, and soft technology includes computer software, information systems, etc. With the adoption of different hardware such as CCTV cameras, metal detectors, bulletproof teller windows, security systems encourage the crime analytics software.

On June 2023, Google Cloud has announced the launch of Anti Money Laundering AI (AML AI), an artificial intelligence (AI)-powered product designed to help global financial institutions more effectively and efficiently detect money laundering.

(Source: Google Cloud Launches AI-Powered Anti Money Laundering Product for Financial Institutions (prnewswire.com))

The COVID-19 impact on the Crime Analytics Software Market.

The COVID-19 pandemic has had a significant impact on the crime analytics software market, resulting in both challenges and opportunities for the industry. The most immediate impact of the pandemic was the widespread imposition of travel restrictions, lockdowns, and quarantines. Due to the lockdowns, social distancing measures, and changes in daily routines, the burglary and street-level crimes have noticed some reduction. Crime analytics software would have been crucial in identifying and analysing these shifts. Remote work became essential during the pandemic, including for law enforcement agencies. Crime analytics software that could be accessed and operated remotely gained importance, allowing personnel to analyse data...

BackgroundGun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author’s knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings.Methods and findingsThis study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health—social mobility, social capital, income inequality, racial and economic segregation, and social spending—with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1–standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73–0.91, p

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.

The General Social Survey (GSS) program gathers data on social subjects in order to monitor changes in the living conditions and well being of Canadians over time and to provide immediate information on specific social policy issues of current or emerging interest.Cycle 18 of the GSS is the fourth cycle dedicated to the topic of victimization; previous cycles were carried out in 1988, 1993, and 1999. Content from Cycle 13 on senior abuse and public perception of alternatives to imprisonment was not repeated. New topics of interest were added including stalking, use of restraining orders and social disorder. Other subjects common to all four cycles include perceptions of crime, police and courts; crime prevention precautions; and accident and crime incident reports.

This data asset contains the data from the survey carried out in Mexico as part of the Juntos para la Prevención de la Violencia Performance Evaluation conducted by the Center on Conflict and Development at Texas A&M University. We surveyed a population that is representative at the urban national level for ages 16 to 29 (n = 1,539). Our sampling design ensures that our sample is not only representative across common sociodemographic categories (e.g., education and income), but also by level of violence. To do so, we consider three variables that capture levels of violence at the municipal level: homicide rate, reported nonhomicidal crime, and perceived level of violence. Homicide rates are considered more accurate official statistics compared to nonhomicidal crimes, as they are often reported more often by the general population and are typically recorded more accurately because they are definitionally specific and typically go through the health system (UNODC 2019). However, this measure does not capture the full reality of insecurity. For this reason, we also include measures generated from Mexico’s National Survey of Urban Public Security (ENSU) to capture nonhomicidal violence and insecurity at the municipal level. Given that the ENSU data are not representative at the municipal level, using this survey and the 2015 intercensus, we generate municipal estimates using multilevel regression and poststratification (MRP). These measures capture the preponderance of nonhomicidal crime (MRP victimization) and perceived community insecurity (MRP insecurity) at the municipal level. With these estimates and homicide rates, we then order municipalities based on level of insecurity and sample via seriation. Our sampling strategy generated a survey sample that is reflective of the ENSU survey in terms of violence level across all three categories. The dataset includes 102 columns and 1,539 rows (corresponding to each respondent). The survey aims to gather information about respondents’ sociodemographic characteristics, victimization, in/security perceptions, protective factors against delinquency, and exposure to and perceptions about gang participation. It also has embedded an original vignette experiment. Experimental vignette studies in survey research use short descriptions of hypothetical scenarios (vignettes) that are usually presented to respondents within surveys in order to elicit how their judgments about such scenarios affect outcomes of interest, often revealing their perceptions, values, or social norms. In our vignette, we randomize the perpetrator’s socioeconomic status and upbringing, the type of criminal involvement (leader vs. gang member), the severity of the crime, and the type of victim to understand how youth attribute blame.

This national-level survey of youth was undertaken to gather detailed behavioral and attitudinal data concerning weapons and violence. The research project sought to obtain information from a broad sample of high-school-aged youth to achieve diversity regarding history, cultural background, population size and density, urban and non-urban mix, economic situation, and class, race, and ethnic distributions. Data for the study were derived from two surveys conducted during the spring of 1996. The first survey was a lengthy questionnaire that focused on exposure to weapons (primarily firearms and knives) and violence, and was completed by 733 10th- and 11th-grade male students. Detail was gathered on all weapon-related incidents up to 12 months prior to the survey. The second survey, consisting of a questionnaire completed by 48 administrators of the 53 schools that the students attended, provided information regarding school characteristics, levels of weapon-related activity in the schools, and anti-violence strategies employed by the schools. The student survey covered demographic characteristics of the respondent, family living situations, educational situations and aspirations, drug, criminal, and gang activities, crime- and violence-related characteristics of family and friends, respondent's social and recreational activities, exposure to violence generally, personal victimization history, and possession of and activities relating to firearms and knives. Administrators were asked to provide basic demographic data about their schools and to rate the seriousness of violence, drugs, guns, and other weapons in their institutions. They were asked to provide weapon-related information about the average male junior in their schools as well as to estimate the number of incidents involving types of weapons on school grounds during the past three years. The administrators were also asked to identify, from an extensive list of violence reduction measures, those that were practiced at their schools. Variables are also provided about the type of school, grades taught, enrollment, and size of the community. In addition to the data collected directly from students and school administrators, Census information concerning the cities and towns in which the sampled schools were located was also obtained. Census data include size of the city or town, racial and ethnic population distributions, age, gender, and educational attainment distributions, median household and per capita income distributions, poverty rates, labor force and unemployment rates, and violent and property crime rates.