In financial year 2018, around **** thousand new road accident claims were lodged to the ACC by European people in New Zealand. The ACC provides financial support and compensation to citizens, residents and temporary visitors in New Zealand if they have suffered accidental personal injuries.

Motor-vehicle deaths in the United States have decreased greatly since the 1970s and 1980s. In 2023, there were around **** deaths from motor vehicles per 100,000 population, compared to a rate of **** deaths per 100,000 in 1970. Laws requiring drivers and passengers to wear safety belts and advancements in safety technology in vehicles are major drivers for these reductions. Motor-vehicle accidents in the U.S. Americans spend a significant amount of time behind the wheel. Many cities lack convenient and reliable public transportation and, especially in rural areas, cars are a necessary means of transportation. In 2020, August was the month with the highest number of fatal crashes, followed by September and June. The deadliest time of day for fatal vehicle crashes is between * and * p.m., most likely due to the after-work rush hour and more people who are under the influence of alcohol. Drinking and driving among youth Drinking and driving remains a relevant problem across the United States and can be especially problematic among younger people. In 2023, around *** percent of those aged 21 to 25 years in the United States reported driving under the influence of alcohol in the preceding year. Furthermore, around ***** percent of those aged 16 to 20 drove after drinking within the past year.

About this dataset

Context

The dataset tabulates the population of Accident by race. It includes the population of Accident across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Accident across relevant racial categories.

Key observations

The percent distribution of Accident population by race (across all racial categories recognized by the U.S. Census Bureau): 96.33% are white, 0.23% are Black or African American, 0.23% are American Indian and Alaska Native, 0.23% are Asian and 2.98% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Accident
• Population: The population of the racial category (excluding ethnicity) in the Accident is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Accident total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Accident Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Accident by race. It includes the distribution of the Non-Hispanic population of Accident across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Accident across relevant racial categories.

Key observations

Of the Non-Hispanic population in Accident, the largest racial group is White alone with a population of 404 (96.88% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Accident
• Population: The population of the racial category (for Non-Hispanic) in the Accident is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Accident total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Accident Population by Race & Ethnicity. You can refer the same here

In financial year 2018, the cost of active road related claims lodged to the ACC by European people in New Zealand amounted to over 304 million New Zealand dollars. The ACC provides financial support and compensation to citizens, residents and temporary visitors in New Zealand if they have suffered accidental personal injuries.

description:

This data set maps the locations of crashes involving pedestrians in the Chapel Hill Region of North Carolina.

The data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network, public vehicular areas and private properties (if reported) from January 2007 through December 2013. Users are able to click and view information specific to each crash. Information for each crash includes: County, City, Crash Date, Crash Day, Crash Group, Crash Location, Crash Time, Crash Severity, Bike/Pedestrian Age Group, Bike/Pedestrian Alcohol Detected, Bike Direction, Bike/Pedestrian Injury, Bike/Pedestrian Position, Bike/Pedestrian Race, Bike/Pedestrian Sex, Ambulance Response, Driver Age Group, Driver Estimated Speed, Speed Limit, Driver Alcohol Detected, Driver Injury, Driver Race, Driver Sex, Driver Vehicle Type, Hit and Run, Development, Light Condition, Locality, Number of Lanes, Road Characteristics/Class/Condition/Configuration, Road Defects/Features, Traffic Control, Crash Type, and/or Weather. Crash identification numbers have been removed from the data for protection of privacy. Crash records were obtained NCDOTs Traffic Engineering Accident Analysis System (TEAAS).

This data set maps the locations of crashes involving pedestrians in the Chapel Hill Region of North Carolina.

The data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network, public vehicular areas and private properties (if reported) from January 2007 through December 2013. Users are able to click and view information specific to each crash. Information for each crash includes: County, City, Crash Date, Crash Day, Crash Group, Crash Location, Crash Time, Crash Severity, Bike/Pedestrian Age Group, Bike/Pedestrian Alcohol Detected, Bike Direction, Bike/Pedestrian Injury, Bike/Pedestrian Position, Bike/Pedestrian Race, Bike/Pedestrian Sex, Ambulance Response, Driver Age Group, Driver Estimated Speed, Speed Limit, Driver Alcohol Detected, Driver Injury, Driver Race, Driver Sex, Driver Vehicle Type, Hit and Run, Development, Light Condition, Locality, Number of Lanes, Road Characteristics/Class/Condition/Configuration, Road Defects/Features, Traffic Control, Crash Type, and/or Weather. Crash identification numbers have been removed from the data for protection of privacy. Crash records were obtained NCDOTs Traffic Engineering Accident Analysis System (TEAAS).

This data set maps the locations of crashes involving bicyclists in the Chapel Hill Region of North Carolina.

The data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network, public vehicular areas and private properties (if reported) from January 2007 through December 2013. Users are able to click and view information specific to each crash. Information for each crash includes: County, City, Crash Date, Crash Day, Crash Group, Crash Location, Crash Time, Crash Severity, Bike/Pedestrian Age Group, Bike/Pedestrian Alcohol Detected, Bike Direction, Bike/Pedestrian Injury, Bike/Pedestrian Position, Bike/Pedestrian Race, Bike/Pedestrian Sex, Ambulance Response, Driver Age Group, Driver Estimated Speed, Speed Limit, Driver Alcohol Detected, Driver Injury, Driver Race, Driver Sex, Driver Vehicle Type, Hit and Run, Development, Light Condition, Locality, Number of Lanes, Road Characteristics/Class/Condition/Configuration, Road Defects/Features, Traffic Control, Crash Type, and/or Weather. Crash identification numbers have been removed from the data for protection of privacy. Crash records were obtained NCDOT’s Traffic Engineering Accident Analysis System (TEAAS).

Road accidents have been a major cause for concern across the Indian subcontinent. In 2022 alone, the country reported nearly *** thousand fatalities due to road accidents. Each year, about ***** to **** percent of the country’s GDP was invested in road accidents. Notably, while India has about *** percent of the world’s vehicle population, it also accounted for about *** percent of the global road traffic incidents. Almost ** percent of the accidents involved young Indians. Cases and causesTwo-wheelers had the maximum involvement in fatal road accidents across the country in 2018. A major portion of the accidents that year occurred at T-junctions. Over speeding has been a cause for concern throughout the country regardless of day or nighttime. Moreover, fast and risky maneuvers and illegal street races on roads and highways not designed for the purpose created significant trouble for the police. Over ** percent of the accidents occurred on straight roads. Additionally, state highways had a share of about ** percent of the total road accidents in 2018. Future scenarioRoughly around 17 accident-related deaths occur across India every hour. Fewer cops and empty roads at night, and sometimes even during the day seem to enable motorists to do away with the traffic rules. However, efforts were made to reduce these discrepancies. The police had equipped themselves with night vision speed guns to identify the culprits. Over speeding fine was increased in the amendment of the Motor Vehicles Act as well. The road network has played a crucial role in India’s economic development and the government is likely to continue to invest resources in making road safety a vital component of everyday commute.

Between 1982 and September 2024, 82 out of the 151 mass shootings in the United States were carried out by White shooters. By comparison, the perpetrator was African American in 26 mass shootings, and Latino in 12. When calculated as percentages, this amounts to 54 percent, 17 percent, and eight percent respectively. Race of mass shooters reflects the U.S. population Broadly speaking, the racial distribution of mass shootings mirrors the racial distribution of the U.S. population as a whole. While a superficial comparison of the statistics seems to suggest African American shooters are over-represented and Latino shooters underrepresented, the fact that the shooter’s race is unclear in around nine percent of cases, along with the different time frames over which these statistics are calculated, means no such conclusions should be drawn. Conversely, looking at the mass shootings in the United States by gender clearly demonstrates that the majority of mass shootings are carried out by men. Mass shootings and mental health With no clear patterns between the socio-economic or cultural background of mass shooters, increasing attention has been placed on mental health. Analysis of the factors Americans considered to be to blame for mass shootings showed 80 percent of people felt the inability of the mental health system to recognize those who pose a danger to others was a significant factor. This concern is not without merit – in over half of the mass shootings since 1982, the shooter showed prior signs of mental health issues, suggesting improved mental health services may help deal with this horrific problem. Mass shootings and guns In the wake of multiple mass shootings, critics have sought to look beyond the issues of shooter identification and their influences by focusing on their access to guns. The majority of mass shootings in the U.S. involve firearms which were obtained legally, reflecting the easy ability of Americans to purchase and carry deadly weapons in public. Gun control takes on a particular significance when the uniquely American phenomenon of school shootings is considered. The annual number of incidents involving firearms at K-12 schools in the U.S. was over 100 in each year since 2018. Conversely, similar incidents in other developed countries exceptionally rare, with only five school shootings in G7 countries other than the U.S. between 2009 and 2018.

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.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data. The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years. The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Prior research finds that in encounters with law enforcement minorities are punished more severely than white civilians. Less is known about the effect of race on encounters and its implications for research on racial profiling. Using high-frequency location data of rideshare Lyft drivers in Florida (N=222,838), we estimate the effect of driver race on citations and fines for speeding across 19,356,683 location pings. Compared to a white driver traveling the same speed, we find that racial/ethnic minority drivers are 24 to 33 percent more likely to be cited for speeding and pay 23 to 34 percent more money in fines. We find no evidence that accident and re-offense rates can explain these estimates, suggesting that underlying our results is an animus against minorities.

The United States military has a long history of ethnic minorities serving in its ranks, with black Americans having served as far back as the Revolutionary War. The Vietnam War took place during a period of changing race relations in the United States, with the Civil Rights Movement reaching its peak in the mid-1960s, and this too was reflected in the military. The Vietnam War was the first major conflict in which black and white troops were not formally segregated, however, discrimination did still occur with black soldiers reporting being subject to overt racism, being unjustly punished, and having fewer promotion opportunities than their white counterparts.

In the early phases of the war, black casualty rates were much higher than for other races and ethnicities, with some reports showing that black soldiers accounted for 25 percent of the casualties recorded in 1965. This declined substantially as the war progressed, however, the proportion of black service personnel among those fallen during the war was still disproportionately high, as black personnel comprised only 11 percent of the military during this era. A smaller number of other ethnic minorities were killed during the war, comprising two percent of the total.

Prevalence of risky behaviors associated with traffic crashes in the 14 Brazilian capitals participating in the life in traffic project and adjusted prevalence ratio by age, sex, education, race and type of road user, 2019.

Bloomington Police Department Calls for Service that reported an accident.

Note that this is every call for service that documents an accident, regardless of the outcome of the accident. Not all accidents become State Crash Reports, and, therefore, the data contained in this set will not match accident data supplied by the Indiana State Police.This set of raw data contains information from Bloomington Police Department Calls for Service that reported an accident.

Key code for Race:

A- Asian/Pacific Island, Non-Hispanic B- African American, Non-Hispanic C- Hawaiian/Other Pacific Island, Hispanic H- Hawaiian/Other Pacific Island, Non-Hispanic I- Indian/Alaskan Native, Non-Hispanic K- African American, Hispanic L- Caucasian, Hispanic N- Indian/Alaskan Native, Hispanic P- Asian/Pacific Island, Hispanic S- Asian, Non-Hispanic T- Asian, Hispanic U- Unknown W- Caucasian, Non-Hispanic

Key Code for Reading Districts:

Example: LB519

L for Law call or incident B stands for Bloomington 5 is the district or beat where incident occurred All numbers following represents a grid sector.

Disclaimer: The Bloomington Police Department takes great effort in making open data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided by many people and that cannot always be verified. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data, or for the use or interpretation of the results of any research conducted.

In 2023, the crude death rate of the ethnic Indian in Malaysia was 8.3 deaths for every 1,000 people, the highest among other ethnic groups. By comparison, the crude death rate of the Bumiputera, the largest ethnic group in Malaysia, was at 5.8 deaths per 1,000 people.

About this dataset

Context

The dataset presents the median household income across different racial categories in Accident. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Accident population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 96.33% of the total residents in Accident. Notably, the median household income for White households is $60,208. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $60,208.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Accident.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Accident median household income by race. You can refer the same here

Between 2012 and 2020, the number of self-inflicted deaths among white prisoners in public prisons in England and Wales went up from 49 to 57.

Heart disease is currently the leading cause of death in the United States. In 2022, COVID-19 was the fourth leading cause of death in the United States, accounting for almost six percent of all deaths that year. The leading causes of death worldwide are similar to those in the United States. However, diarrheal diseases and neonatal conditions are major causes of death worldwide, but are not among the leading causes in the United States. Instead, accidents and chronic liver disease have a larger impact in the United States.

Racial differences

In the United States, there exist slight differences in leading causes of death depending on race and ethnicity. For example, assault, or homicide, accounts for around three percent of all deaths among the Black population but is not even among the leading causes of death for other races and ethnicities. However, heart disease and cancer are still the leading causes of death for all races and ethnicities.

Leading causes of death among men vs women

Similarly, there are also differences in the leading causes of death in the U.S. between men and women. For example, among men, intentional self-harm accounts for around two percent of all deaths but is not among the leading causes of death among women. On the other hand, influenza and pneumonia account for more deaths among women than men.