The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly). Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

Fatality Analysis Reporting System (FARS) was created in the United States by the National Highway Traffic Safety Administration (NHTSA) to provide an overall measure of highway safety, to help suggest solutions, and to help provide an objective basis to evaluate the effectiveness of motor vehicle safety standards and highway safety programs.

FARS contains data on a census of fatal traffic crashes within the 50 States, the District of Columbia, and Puerto Rico. To be included in FARS, a crash must involve a motor vehicle traveling on a trafficway customarily open to the public and result in the death of a person (occupant of a vehicle or a non-occupant) within 30 days of the crash. FARS has been operational since 1975 and has collected information on over 989,451 motor vehicle fatalities and collects information on over 100 different coded data elements that characterizes the crash, the vehicle, and the people involved.

FARS is vital to the mission of NHTSA to reduce the number of motor vehicle crashes and deaths on our nation's highways, and subsequently, reduce the associated economic loss to society resulting from those motor vehicle crashes and fatalities. FARS data is critical to understanding the characteristics of the environment, trafficway, vehicles, and persons involved in the crash.

NHTSA has a cooperative agreement with an agency in each state government to provide information in a standard format on fatal crashes in the state. Data is collected, coded and submitted into a micro-computer data system and transmitted to Washington, D.C. Quarterly files are produced for analytical purposes to study trends and evaluate the effectiveness highway safety programs.

Content

There are 40 separate data tables. You can find the manual, which is too large to reprint in this space, here.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.nhtsa_traffic_fatalities.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

This dataset was provided by the National Highway Traffic Safety Administration.

In 2021, more than 44,000 male drivers were involved in fatal crashes in U.S. road traffic, which accounted for 72.3 percent of the total, while female drivers were involved in about 15,100 fatal crashes. The number of drivers who were involved in fatal crashes has shown an increase of about 16.2 percent from 2016.

This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

In 2023, China recorded 60,028 fatalities in traffic accidents across the country. The number of fatalities has increased from 60,676in the previous year. Road traffic in China The number of road traffic fatalities in China varies greatly from region to region. Guangdong and Hubei had been the provinces with the highest number of traffic fatalities. All located in the eastern coastal area of China, they had also been the regions with the most traffic accidents in 2023. On the contrary, only a small number of fatalities had been reported in central and western regions of China. Reasons for this imbalance may be found in less traffic volume as well as the existence of fewer urban congested areas.Since 2016, the number of casualties and fatalities from traffic accidents in China has increased significantly, reaching 25,3895 injuries and 60,028 deaths in 2023. Nevertheless, traffic accidents have emerged as one of the leading causes of death in China. The primary reasons may be unregulated road works and a lack of awareness among Chinese drivers. The development of neither road infrastructure nor driving behavior in China had been able to keep up with the increasing number of traffic participants and registered cars. As of 2003, only 24 million vehicles had been registered in China, whereas by 2019 that number had skyrocketed to 253.76 million cars. In 2023 alone, the number of newly registered vehicles in China had amounted to around 24.5 million cars.

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details. Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

Crash data shows information about each traffic crash on city streets within the City of Chicago limits and under the jurisdiction of Chicago Police Department (CPD). Data are shown as is from the electronic crash reporting system (E-Crash) at CPD, excluding any personally identifiable information. Records are added to the data portal when a crash report is finalized or when amendments are made to an existing report in E-Crash. Data from E-Crash are available for some police districts in 2015, but citywide data are not available until September 2017. About half of all crash reports, mostly minor crashes, are self-reported at the police district by the driver(s) involved and the other half are recorded at the scene by the police officer responding to the crash. Many of the crash parameters, including street condition data, weather condition, and posted speed limits, are recorded by the reporting officer based on best available information at the time, but many of these may disagree with posted information or other assessments on road conditions. If any new or updated information on a crash is received, the reporting officer may amend the crash report at a later time. A traffic crash within the city limits for which CPD is not the responding police agency, typically crashes on interstate highways, freeway ramps, and on local roads along the City boundary, are excluded from this dataset. All crashes are recorded as per the format specified in the Traffic Crash Report, SR1050, of the Illinois Department of Transportation. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here. As per Illinois statute, only crashes with a property damage value of $1,500 or more or involving bodily injury to any person(s) and that happen on a public roadway and that involve at least one moving vehicle, except bike dooring, are considered reportable crashes. However, CPD records every reported traffic crash event, regardless of the statute of limitations, and hence any formal Chicago crash dataset released by Illinois Department of Transportation may not include all the crashes listed here. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

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.

The graph illustrates the number of truck accidents in the United States from 2020 to 2025. The x-axis represents the years, ranging from 2020 to 2025, while the y-axis shows the number of truck accidents. In 2020, there were 142,637 accidents, which increased to a peak of 165,731 in 2021. The number slightly declined to 164,503 in 2022 and further decreased to 152,114 in 2023. The projected or preliminary figure for 2024 is 138,985, marking the lowest number in the dataset at the moment. Overall, the data exhibits a sharp increase in truck accidents in 2021, followed by a consistent downward trend in the subsequent years. This information is presented in a line graph format, effectively highlighting the annual changes and trends in truck accident occurrences in the United States.

Context

The aviation accident database throughout the world, from 1908-2019.

• All civil and commercial aviation accidents of scheduled and non-scheduled passenger airliners worldwide, which resulted in a fatality (including all U.S. Part 121 and Part 135 fatal accidents)
• All cargo, positioning, ferry and test flight fatal accidents.
• All military transport accidents with 10 or more fatalities.
• All commercial and military helicopter accidents with greater than 10 fatalities.
• All civil and military airship accidents involving fatalities.
• Aviation accidents involving the death of famous people.
• Aviation accidents or incidents of noteworthy interest.

There are similar dataset available on Kaggle. This dataset is cleaned versioned and source code is available on github.

Content

Data is scraped from planecrashinfo.com. Below you can find the dataset column descriptions:

• Date: Date of accident, in the format - January 01, 2001
• Time: Local time, in 24 hr. format unless otherwise specified
• Airline/Op: Airline or operator of the aircraft
• Flight #: Flight number assigned by the aircraft operator
• Route: Complete or partial route flown prior to the accident
• AC Type: Aircraft type
• Reg: ICAO registration of the aircraft
• cn / ln: Construction or serial number / Line or fuselage number
• Aboard: Total aboard (passengers / crew)
• Fatalities: Total fatalities aboard (passengers / crew)
• Ground: Total killed on the ground
• Summary: Brief description of the accident and cause if known

Acknowledgements

The original data is from the Plane Crash info website (http://www.planecrashinfo.com/database.htm). Dataset is scraped with Python. Source code is also public on Github

Inspiration

Find the root cause of plane crashes. Find any insights from dataset such as - Which operators are the worst - Which aircrafts are the worst

#La CNESST listens to your data needs! Answer our questionnaire.## Do you use or have specific needs related to [CNESST] data (https://www.cnesst.gouv.qc.ca/fr)? Take a few minutes to answer the following questionnaire: Consultation on your CNESST data needs To communicate with us about: - the questionnaire, write to us about: - the questionnaire, write to us at donnees.ouvertes@cnesst.gouv.qc.ca. - of a technical problem, write to us at consultation@cnesst.gouv.qc.ca or call us on 1 donnees.ouvertes@cnesst.gouv.qc.ca 866 216-7918, specifying the title of the consultation. Thanks for your participation! # # The dataset shows death claims accepted by the CNESST from January 1 to December 31. The CNESST administers the occupational health and safety regime. The Law on Industrial Accidents and Occupational Diseases (LATMP) aims to compensate for occupational injuries and the consequences they cause for beneficiaries. The death claims presented in the data set meet the following criteria: * They are the consequence of a work accident or an occupational disease within the meaning of the LATMP. * These claims represent people who were covered by the occupational health and safety insurance plan administered by the CNESST. * The date of registration of the acceptance of the death claim is between January 1 and December 31 of the reference year. Note that the death may have occurred during a year prior to the reference year.

Data Source: California Office of Traffic Safety

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: https://data.countyofnapa.org/stories/s/abqu-wcty

Why was the data collected?  California Office of Traffic Safety (OTS) ranking metric is a tool used to compare similarly sized cities on traffic safety statistics. A smaller the assigned number means that the city is ranked higher, and a higher ranking means the city has worse traffic safety compared to similar locations.

How was the data collected? Crash data comes from Statewide Traffic Records System (SWITRS). This system collects and processes data gathered from a collision scene. Population estimates come from California Department of Finance (DoF), which are based on changes in births, deaths, domestic migration, and international migration. Estimates are developed using aggregate data from a variety of sources, including birth and death counts provided by the Department of Public Health, driver's license data from the Department of Motor Vehicles, housing unit data from local governments, school enrollment data from the Department of Education, and federal income tax return data from the U.S. Internal Revenue Service. Daily Vehicle Miles Traveled (DVMT) come from California Department of Transportation (Caltrans). The Traffic Data Branch at Caltrans estimates the number of vehicle miles that motorists traveled on California State Highways using a sampling of up to 20 traffic monitoring sites and reports on that data. Crash rankings are based on a ranking method that assigns statistical weights to categories including observed crash counts, population, and vehicle miles traveled. Counties are assigned statewide rankings, while cities are assigned population group rankings. DUI arrests data comes from the Department of Justice.

Who was included and excluded from the data & Where was the data collected? Data for the rankings is taken from Incorporated cities only. This includes local streets and state highways within city limits that share jurisdiction with the CHP. DUI arrest data is only available for cities that report it to the Department of Justice. Data from the OTS crash was sources specifically for Napa County, the City of Napa, American Canyon, Calistoga, St. Helena and Yountville.

When was the data collected? 2017-2022

Where can I learn more about this data? Office of traffic safety: https://www.ots.ca.gov/media-and-research/crash-rankings/ Methodology: https://rosap.ntl.bts.gov/view/dot/24410

Description

This dataset includes accidents in the City and County of Denver for the previous five calendar years plus the current year to date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Accident data is updated Monday through Friday.

Disclaimer

The information provided here regarding public safety in Denver are offered as a courtesy by the City and County of Denver. By downloading this data, you acknowledge that you have read and understand the Disclaimer below and agree to be bound by it. Certain information is omitted, in accordance with legal requirements and as described more fully in this Disclaimer.

All materials contained on this site are distributed and transmitted "as is," without any representation as to completeness or accuracy and without warranty or guarantee of any kind. The City and County of Denver is not responsible for any error or omission on this site or for the use or interpretation of the results of any research conducted here.

About Accident Data

The Denver Police Department strives to make accident data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated Monday through Friday, adding new incidents and updating existing data with information gathered through the investigative process.

Not surprisingly, accident data become more accurate over time, as new crashes are reported and more information comes to light during investigations.

Accidents that occurred at least 30 days ago tend to be the most accurate, although records are returned for incidents that happened yesterday. This dynamic nature of accident data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the City and County of Denver, even though they draw from the same database.

The Denver Police Department completes accident reports if there is $1,000 or greater in damage, an injury or fatality or drug/alcohol involvement. If the accident is classified as a counter report it is sent to the State without being entered into the database.

About Accident Locations

Accident locations reflect the approximate locations of the crash but are not mapped to actual location of the accident. Certain accidents may not appear on maps if there is insufficient detail to establish a specific, mappable location.

The program collects data for analysis of traffic safety crashes to identify problems, and evaluate countermeasures leading to reducing injuries and property damage resulting from motor vehicle crashes. The FARS dataset contains descriptions, in standard format, of each fatal crash reported. To qualify for inclusion, a crash must involve a motor vehicle traveling a traffic-way customarily open to the public and resulting in the death of a person (occupant of a vehicle or a non-motorist) within 30 days of the crash. Each crash has more than 100 coded data elements that characterize the crash, the vehicles, and the people involved. The specific data elements may be changed slightly each year to conform to the changing user needs, vehicle characteristics and highway safety emphasis areas. The type of information that FARS, a major application, processes is therefore motor vehicle crash data.

As a result of the continued annual growth in global air traffic passenger demand, the number of airplanes that were involved in accidents is on the increase. Although the United States is ranked among the 20 countries with the highest quality of air infrastructure, the U.S. reports the highest number of civil airliner accidents worldwide. 2020 was the year with more plane crashes victims, despite fewer flights The number of people killed in accidents involving large commercial aircraft has risen globally in 2020, even though the number of commercial flights performed last year dropped by 57 percent to 16.4 million. More than half of the total number of deaths were recorded in January 2020, when an Ukrainian plane was shot down in Iranian airspace, a tragedy that killed 176 people. The second fatal incident took place in May, when a Pakistani airliner crashed, killing 97 people. Changes in aviation safety In terms of fatal accidents, it seems that aviation safety experienced some decline on a couple of parameters. For example, there were 0.37 jet hull losses per one million flights in 2016. In 2017, passenger flights recorded the safest year in world history, with only 0.11 jet hull losses per one million flights. In 2020, the region with the highest hull loss rate was the Commonwealth of Independent States. These figures do not take into account accidents involving military, training, private, cargo and helicopter flights.

List of every shooting incident that occurred in NYC during the current calendar year. This is a breakdown of every shooting incident that occurred in NYC during the current calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a shooting incident in NYC and includes information about the event, the location and time of occurrence. In addition, information related to suspect and victim demographics is also included. This data can be used by the public to explore the nature of police enforcement activity. Please refer to the attached data footnotes for additional information about this dataset.

This collection focuses on how changes in the legal drinking age affect the number of fatal motor vehicle accidents and crime rates. The principal investigators identified three areas of study. First, they looked at blood alcohol content of drivers involved in fatal accidents in relation to changes in the drinking age. Second, they looked at how arrest rates correlated with changes in the drinking age. Finally, they looked at the relationship between blood alcohol content and arrest rates. In this context, the investigators used the percentage of drivers killed in fatal automobile accidents who had positive blood alcohol content as an indicator of drinking in the population. Arrests were used as a measure of crime, and arrest rates per capita were used to create comparability across states and over time. Arrests for certain crimes as a proportion of all arrests were used for other analyses to compensate for trends that affect the probability of arrests in general. This collection contains three parts. Variables in the Federal Bureau of Investigation Crime Data file (Part 1) include the state and year to which the data apply, the type of crime, and the sex and age category of those arrested for crimes. A single arrest is the unit of analysis for this file. Information in the Population Data file (Part 2) includes population counts for the number of individuals within each of seven age categories, as well as the number in the total population. There is also a figure for the number of individuals covered by the reporting police agencies from which data were gathered. The individual is the unit of analysis. The Fatal Accident Data file (Part 3) includes six variables: the FIPS code for the state, year of accident, and the sex, age group, and blood alcohol content of the individual killed. The final variable in each record is a count of the number of drivers killed in fatal motor vehicle accidents for that state and year who fit into the given sex, age, and blood alcohol content grouping. A driver killed in a fatal accident is the unit of analysis.

#La CNESST listens to your data needs! Answer our questionnaire.## Do you use or have specific needs related to [CNESST] data (https://www.cnesst.gouv.qc.ca/fr)? Take a few minutes to answer the following questionnaire: Consultation on your CNESST data needs To communicate with us about: - the questionnaire, write to us about: - the questionnaire, write to us at donnees.ouvertes@cnesst.gouv.qc.ca. - of a technical problem, write to us at consultation@cnesst.gouv.qc.ca or call us on 1 donnees.ouvertes@cnesst.gouv.qc.ca 866 216-7918, specifying the title of the consultation. Thanks for your participation! # # The dataset shows claims for occupational injuries registered in one calendar year and admitted no later than March 1 of the following year. Occupational injuries are recorded in CNESST systems following the receipt of claims. These claims represent workers who were covered by the occupational health and safety insurance plan for which the CNESST is responsible. The Law on Industrial Accidents and Occupational Diseases (LATMP) aims to compensate for occupational injuries and the consequences they cause for beneficiaries. Modification of the Occupational injuries dataset in August 2022 These presence indicators for the types of injuries have been added: - Occupational deafness injuries - Machine injuries - Musculoskeletal disorders - Musculoskeletal disorders - Psychological injuries - Psychological injuries - COVID-19 injuries

List of every criminal summons issued in NYC during the current calendar year.

This is a breakdown of every criminal summons issued in NYC by the NYPD during the current calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a criminal summons issued in NYC by the NYPD and includes information about the type of crime, the location and time of enforcement. In addition, information related to suspect demographics is also included. This data can be used by the public to explore the nature of police enforcement activity. Please refer to the attached data footnotes for additional information about this dataset.