Traffic fatalities within the City of Chicago that are included in Vision Zero Chicago (VZC) statistics. Vision Zero is Chicago’s commitment to eliminating fatalities and serious injuries from traffic crashes. The VZC Traffic Fatality List is compiled by the Chicago Department of Transportation (CDOT) after monthly reviews of fatal traffic crash information provided by Chicago Police Department’s Major Accident Investigation Unit (MAIU). CDOT uses a standardized process – sometimes differing from other sources and everyday use of the term -- to determine whether a death is a “traffic fatality.” Therefore, the traffic fatalities included in this list may differ from the fatal crashes reported in the full Traffic Crashes dataset (https://data.cityofchicago.org/d/85ca-t3if). Official traffic crash data are published by the Illinois Department of Transportation (IDOT) on an annual basis. This VZC Traffic Fatality List is updated monthly. Once IDOT publishes its crash data for a year, this dataset is edited to reflect IDOT’s findings. VZC Traffic Fatalities can be linked with other traffic crash datasets using the “Person_ID” field. State of Illinois considers a “traffic fatality” as any death caused by a traffic crash involving a motor vehicle, within 30 days of the crash. Fatalities that meet this definition are included in this VZC Traffic Fatality List unless excluded by any criteria below. There may be records in this dataset that do not appear as fatalities in the other datasets. The following criteria exclude a death from being considered a "traffic fatality," and are derived from Federal and State reporting standards. The Medical Examiner determined that the primary cause of the fatality was not the traffic crash, including: a. The fatality was reported as a suicide based on a police investigation. b. The fatality was reported as a homicide in which the "party at fault" intentionally inflicted serious bodily harm that caused the victim's death. c. The fatality was caused directly and exclusively by a medical condition or the fatality was not attributable to road user movement on a public roadway. (Note: If a person driving suffers a medical emergency and consequently hits and kills another road user, the other road user is included, although the driver suffering a medical emergency is excluded.) The crash did not occur within a trafficway. The crash involved a train or other such mode of transport within the rail dedicated right-of-way. The fatality was on a roadway not under Chicago Police Department jurisdiction, including: a. The fatality was occurred on an expressway. The City of Chicago does not have oversight on the expressway system. However, a fatality on expressway ramps occurring within the City jurisdiction will be counted in VZC Traffic Fatality List. b. The fatality occurred outside City limits. Crashes on streets along the City boundary may be assigned to another jurisdiction after the investigation if it is determined that the crash started or substantially occurred on the side of the street that is outside the City limits. Jurisdiction of streets along the City boundary are split between City and neighboring jurisdictions along the street centerline. The fatality is not a person (e.g., an animal). Change 12/7/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

The number of road accidents per one million inhabitants in the United States was forecast to continuously decrease between 2024 and 2029 by in total 2,490.4 accidents (-14.99 percent). After the eighth consecutive decreasing year, the number is estimated to reach 14,118.78 accidents and therefore a new minimum in 2029. Depicted here are the estimated number of accidents which occured in relation to road traffic. They are set in relation to the population size and depicted as accidents per one million inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road accidents per one million inhabitants in countries like Mexico and Canada.

Dataset Title: Flight Crashes and Deaths (1970–2025)

Description: This dataset compiles information on aviation accidents and associated fatalities worldwide from 1970 through 2025. It includes annual counts of flight crashes and total death tolls, offering insights into trends in aviation safety over five decades.

Fields: - Year: Calendar year from 1970 to 2025. - Crashes: Total number of recorded aviation accidents in that year. -Deaths: Total number of fatalities resulting from aviation accidents in that year.

Format: CSV or Excel (3 columns, 56 rows)

Data Sources: Compiled from multiple aviation safety databases and publicly available aviation reports, including contributions from: - Aviation Safety Network (ASN) - National Transportation Safety Board (NTSB) - International Civil Aviation Organization (ICAO) - News archives and investigative bodies (for 2024–2025 data)

Use Cases: - Trend analysis of aviation safety improvements or setbacks - Statistical modeling for accident frequency and severity - Policy formulation and airline safety comparisons - Data journalism and historical analysis

Limitations: - Data accuracy may vary slightly for recent years (2024–2025) due to incomplete reporting or ongoing investigations. - Only includes incidents with aircraft carrying at least 12 passengers.

These data represent the Age-Adjusted Colorado County Mortality Rate Per 100,000 Persons for Motor Vehicle Accident as the Underlying Cause of Death (2015-2019). Population estimates for the denominator are calculated from the 2015-2019 American Community Survey. These data are from the Colorado Department of Public Health and Environment Vital Records Death Dataset and are published annually by the Colorado Department of Public Health and Environment.

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.

the Ministry of Road Transport and Highways of the Government of India releases annual reports on road accidents and casualties in the country. Additionally, many state governments also release data on road accidents within their jurisdiction. There are many potential causes of road accidents, including: Distracted driving (e.g. using a cell phone, eating, or applying makeup while driving) Impaired driving (e.g. driving under the influence of alcohol or drugs) Reckless or aggressive driving (e.g. speeding, tailgating, or running red lights) Fatigue or drowsy driving Poor road conditions (e.g. potholes, debris, or lack of proper signage) Vehicle defects or malfunctions Poor weather conditions (e.g. rain, snow, or fog) Inadequate infrastructure (e.g. lack of proper lighting, median barriers, or guardrails) Pedestrian or bicycle errors Wildlife crossing the road. There are several datasets available on road accidents, depending on the country and region. Here are a few examples:

In the United States, the National Highway Traffic Safety Administration (NHTSA) provides data on vehicle crashes, including details such as the location, cause, and number of injuries and fatalities. The United Kingdom's Department for Transport provides data on reported road accidents, including information on the type of vehicle, number of casualties, and severity of injuries. The World Health Organization (WHO) also has a Global Status Report on Road Safety, which provides data on road accidents and fatalities for countries around the world. In India, the Ministry of Road Transport and Highways (MoRTH) provides annual data on road accidents and fatalities. The Global Road Safety Partnership (GRSP) also has a wealth of data on road accidents and fatalities, particularly in low- and middle-income countries. It is important to note that these datasets may have different data collection methodologies, and may not include all road accidents that have occurred.

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.

The number of road traffic fatalities per one million inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 18.5 deaths (+13.81 percent). After the tenth consecutive increasing year, the number is estimated to reach 152.46 deaths and therefore a new peak in 2029. Depicted here are the estimated number of deaths which occured in relation to road traffic. They are set in relation to the population size and depicted as deaths per 100,000 inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road traffic fatalities per one million inhabitants in countries like Mexico and Canada.

This dataset contains information about vehicles (or units as they are identified in crash reports) involved in a traffic crash. This dataset should be used in conjunction with the traffic Crash and People dataset available in the portal. “Vehicle” information includes motor vehicle and non-motor vehicle modes of transportation, such as bicycles and pedestrians. Each mode of transportation involved in a crash is a “unit” and get one entry here. Each vehicle, each pedestrian, each motorcyclist, and each bicyclist is considered an independent unit that can have a trajectory separate from the other units. However, people inside a vehicle including the driver do not have a trajectory separate from the vehicle in which they are travelling and hence only the vehicle they are travelling in get any entry here. This type of identification of “units” is needed to determine how each movement affected the crash. Data for occupants who do not make up an independent unit, typically drivers and passengers, are available in the People table. Many of the fields are coded to denote the type and location of damage on the vehicle. Vehicle information can be linked back to Crash data using the “CRASH_RECORD_ID” field. Since this dataset is a combination of vehicles, pedestrians, and pedal cyclists not all columns are applicable to each record. Look at the Unit Type field to determine what additional data may be available for that record.

The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. 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.

Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

This is the number of people of all ages killed or seriously injured (KSI) in road traffic accidents, in an area, adjusted. This indicator includes only casualties who are fatally or seriously injured and these categories are defined as follows:

Fatal casualties are those who sustained injuries which caused death less than 30 days after the accident; confirmed suicides are excluded.

Seriously injured casualties are those who sustained an injury for which they are detained in hospital as an in-patient, or any of the following injuries, whether or not they are admitted to hospital: fractures, concussion, internal injuries, crushings, burns (excluding friction burns), severe cuts and lacerations, severe general shock requiring medical treatment and injuries causing death 30 or more days after the accident.

An injured casualty is recorded as seriously or slightly injured by the police on the basis of information available within a short time of the collision. This generally will not reflect the results of a medical examination, but may be influenced according to whether the casualty is hospitalised or not. Hospitalisation procedures will vary regionally.

Slight injuries are excluded from the total, such as a sprain (including neck whiplash injury), bruise or cut which are not judged to be severe, or slight shock requiring roadside attention.

Police forces use one of two systems for recording reported road traffic collisions; the CRaSH (Collision Recording and Sharing) or COPA (Case Overview Preparation Application). Estimates are calculated from figures which are as reported by police. Since 2016, changes in severity reporting systems for a large number of police forces mean that serious injury figures, and to a lesser extent slight injuries, are not comparable with earlier years. As a result, both adjusted and unadjusted killed or seriously injured statistics are available. Further information about the reporting systems can be found here.

Areas with low resident populations but have high inflows of people or traffic may have artificially high rates because the at-risk resident population is not an accurate measure of exposure to transport. This is likely to affect the results for employment centres e.g. City of London and sparsely populated rural areas which have high numbers of visitors or through traffic. Counts for Heathrow Airport are included in the London Region and England totals only.

From the publication of the 2023 statistics onwards, casualty rates shown in table RAS0403 to include rates based on motor vehicle traffic only. This is because the department does not consider pedal cycle traffic to be robust at the local authority level.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Airplane Crashes and Fatalities Since 1908 (Full history of airplane crashes throughout the world, from 1908-present)

At the time this Dataset was created in Kaggle (2016-09-09), the original version was hosted by Open Data by Socrata at the at: https://opendata.socrata.com/Government/Airplane-Crashes-and-Fatalities-Since-1908/q2te-8cvq, but unfortunately that is not available anymore. The dataset contains data of airplane accidents involving civil, commercial and military transport worldwide from 1908-09-17 to 2009-06-08.

While applying for a data scientist job opportunity, I was asked the following questions on this dataset:

1. Yearly how many planes crashed? how many people were on board? how many survived? how many died?
2. Highest number of crashes by operator and Type of aircrafts.
3. ‘Summary’ field has the details about the crashes. Find the reasons of the crash and categorize them in different clusters i.e Fire, shot down, weather (for the ‘Blanks’ in the data category can be UNKNOWN) you are open to make clusters of your choice but they should not exceed 7.
4. Find the number of crashed aircrafts and number of deaths against each category from above step.
5. Find any interesting trends/behaviors that you encounter when you analyze the dataset.

My solution was:

The following bar charts display the answers requested by point 1. of the assignment, in particular:

• the planes crashed per year
• people aboard per year during crashes
• people dead per year during crashes
• people survived per year during crashes

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F298505%2F37efb7629abf402544ddc46cc3a2d7bb%2F_results_0_0.png?generation=1587821759491827&alt=media" alt="">

The following answers regard point 2 of the assignment

• Highest number of crashes by operator: Aeroflot with 179 crashes
• By Type of aircraft: Douglas DC-3 with 334 crashes

I have identified 7 clusters using k-means clustering technique on a matrix obtained by a text corpus created by using Text Analysis (plain text, remove punctuation, to lower, etc.) The following table summarize for each cluster the number of crashes and death.

• Cluster 1: 258 crashes, 6368 deaths
• Cluster 2: 500 crashes, 9408 deaths
• Cluster 3: 211 crashes, 3513 deaths
• Cluster 4: 1014 crashes, 14790 deaths
• Cluster 5: 2749 crashes, 58826 deaths
• Cluster 6: 195 crashes, 4439 deaths
• Cluster 7: 341 crashes, 8135 deaths

The following picture shows clusters using the first 2 principal components: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F298505%2Fea73e0fe9ca12d594fd83f285d3eff62%2F_results_1_17.png?generation=1587821871806437&alt=media" alt="">

For each clusters I will summarize the most used words and I will try to identify the causes of the crash

Cluster 1 (258) aircraft, crashed, plane, shortly, taking. No many information about this cluster can be deducted using Text Analysis

Cluster 2 (500) aircraft, airport, altitude, crashed, crew, due, engine, failed, failure, fire, flight, landing, lost, pilot, plane, runway, takeoff, taking. Engine failure on the runway after landing or takeoff

Cluster 3 (211): aircraft, crashed, fog Crash caused by fog

Cluster 4 (1014): aircraft, airport, attempting, cargo, crashed, fire, land, landing, miles, pilot, plane, route, runway, struck, takeoff Struck a cargo during landing or takeoff

Cluster 5 (2749): accident, aircraft, airport, altitude, approach, attempting, cargo, conditions, control, crashed, crew, due, engine, failed, failure, feet, fire, flight, flying, fog, ground, killed, land, landing, lost, low, miles, mountain, pilot. plane, poor, route, runway, short, shortly, struck, takeoff, taking, weather
Struck a cargo due to engine failure or bad weather conditions mainly fog

Cluster 6 (195): aircraft, crashed, engine, failure, fire, flight, left, pilot, plane, runway
Engine failure on the runway

Cluster 7 (341): accident, aircraft, altitude, cargo, control, crashed, crew, due, engine, failure, flight, landing, loss, lost, pilot, plane, takeoff
Engine failure during landing or takeoff

Better solutions are welcome.

Thanks, Sauro

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012. 2012 Source: Fatality Analysis Reporting System (FARS)Note: Blank cells indicate data are suppressed. 2014 Source: Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Fatality rates based on fewer than 20 deaths are suppressed.

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.

This dataset shows numbers of people Killed or Seriously Injured (KSI) in Road Traffic Collisions by calendar year for Lincolnshire and districts. The dataset shows: Numbers of people KSI in road collisions KSI numbers of children age 0-15 Numbers of KSI casualties from collisions involving drivers age 17-24 and age 60 and over Annual total numbers of fatalities from road collisions Numbers below 5 have been removed, and where needed one or more further counts of 5 or greater have also been removed. This generally only affects district figures but means some figures for districts will not add up to the Lincolnshire total. The data is updated annually in May. Source: Lincolnshire Road Safety Partnership (LRSP). For any enquiries about this publication contact stayingalive@lincolnshire.gov.uk

National Collision Database (NCDB) – a database containing all police-reported motor vehicle collisions on public roads in Canada. Selected variables (data elements) relating to fatal and injury collisions for the collisions from 1999 to the most recent available data.

Crashes data includes crash event level details such as location - the Lat/Long of the nearest intersection, A and B street names, with distance and direction of the crash from nearest intersection, etc... It also includes crash level details like weather and roadway conditions, and time of day. Also included are the involved party (vehicle involved with), primary collision factor and severity of injury in terms of fatalities, and severe, moderate and minor injuries per crash.

The vehicles data includes the vehicle level details of the crash such as vehicle types, driver's (vehicle, party) age and sex, driver conditions and violations proceeding the crash, etc...

There is a one to many relationship that needs to be built that relates the crash to the vehicles involved. (i.e. there are an average of 2.07 vehicles/parties involved per crash)

Match the Crash name in vehicle data to the Name in the Crash data to relate the two sets of data.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

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