United States General Aviation Safety Data

JohnSnowLabs

Files Size Format Created Updated License Source
2 69kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics
Download

Data Files

File Description Size Last changed Download
united-states-general-aviation-safety-data-csv 2kB csv (2kB) , json (12kB)
united-states-general-aviation-safety-data_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 6kB zip (6kB)

united-states-general-aviation-safety-data-csv  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) Year of data
Total_Fatalities 2 integer Total number of fatalities from U.S. registered civil aircraft not operated under 14 CFR 121 or 14 CFR 135. Accidents on foreign soil and in foreign waters are excluded. Suicide, sabotage, and stolen/unauthorized cases included in accidents, fatalities and rate computation in this table are: 1985 (11 accidents, 6 fatal accidents); 1990 (4, 1); 1991 (8, 5); 1992 (2, 1); 1993 (5, 4); 1994 (3, 2); 1995 (10, 6); 1996 (4, 0); 1997 (5, 2); 1998 (6, 4); 1999 (3, 1); 2000 (7, 7); 2001 (3, 1); 2002 (7, 6); 2003 (4, 3); 2004 (3, 0); 2005 (2, 1); 2006 (2, 1); 2007 (2, 2); 2008 (2, 0); 2009 (3, 0); 2010 (3, 2), 2011 (1, 0), 2012 (1, 1), 2013(3, 3), 2014(0, 0).
Total_Seriously_Injured_Persons 3 integer Total number of seriously injured persons in accidents
Total_Accidents 4 integer Total number of civil aircraft accidents happended. a U.S. registered civil aircraft not operated under 14 CFR 121 or 14 CFR 135. Accidents on foreign soil and in foreign waters are excluded. Suicide, sabotage, and stolen/unauthorized cases included in accidents, fatalities and rate computation in this table are: 1985 (11 accidents, 6 fatal accidents); 1990 (4, 1); 1991 (8, 5); 1992 (2, 1); 1993 (5, 4); 1994 (3, 2); 1995 (10, 6); 1996 (4, 0); 1997 (5, 2); 1998 (6, 4); 1999 (3, 1); 2000 (7, 7); 2001 (3, 1); 2002 (7, 6); 2003 (4, 3); 2004 (3, 0); 2005 (2, 1); 2006 (2, 1); 2007 (2, 2); 2008 (2, 0); 2009 (3, 0); 2010 (3, 2), 2011 (1, 0), 2012 (1, 1), 2013(3, 3), 2014(0, 0). Since April 1995, the National Transportation Safety Board has been required by law to investigate all public-use accidents, increasing the number of NTSB reported general aviation accidents by approximately 1.75%.
Total_Fatal_Accidents 5 integer Total number of civil aircraft accidents with fatality. U.S. registered civil aircraft not operated under 14 CFR 121 or 14 CFR 135. Accidents on foreign soil and in foreign waters are excluded. Suicide, sabotage, and stolen/unauthorized cases included in accidents, fatalities and rate computation in this table are: 1985 (11 accidents, 6 fatal accidents); 1990 (4, 1); 1991 (8, 5); 1992 (2, 1); 1993 (5, 4); 1994 (3, 2); 1995 (10, 6); 1996 (4, 0); 1997 (5, 2); 1998 (6, 4); 1999 (3, 1); 2000 (7, 7); 2001 (3, 1); 2002 (7, 6); 2003 (4, 3); 2004 (3, 0); 2005 (2, 1); 2006 (2, 1); 2007 (2, 2); 2008 (2, 0); 2009 (3, 0); 2010 (3, 2), 2011 (1, 0), 2012 (1, 1), 2013(3, 3), 2014(0, 0). Since April 1995, the National Transportation Safety Board has been required by law to investigate all public-use accidents, increasing the number of NTSB reported general aviation accidents by approximately 1.75%.
Flight_Hours_In_Thousands 6 integer Number of flight hours in thousands in the year
Fatalities_Rates_Per_100000_Flight_Hours 7 number Fatality Rates are computed by dividing the number of Total fatalities by the number of Flight hours. Rates are computed by dividing the number of Total fatalities, by the number of Flight hours, except for the exclusions mentioned in footnote; Suicide, sabotage, and stolen/unauthorized cases.
Seriously_Injured_Persons_Rates_Per_100000_Flight_Hours 8 number Rates are computed by dividing the number of Total seriously injured persons by the number of Flight hours. Rates are computed by dividing the number of Total seriously injured persons by the number of Flight hours, except for the exclusions mentioned in footnote; Suicide, sabotage, and stolen/unauthorized case.
Total_Accidents_Rates_Per_100000_Flight_Hours 9 number Rates are computed by dividing the number of Total accidents by the number of Flight hours. Rates are computed by dividing the number of Total accidents by the number of Flight hours, except for the exclusions mentioned in footnote; Suicide, sabotage, and stolen/unauthorized cases.
Total_Accidents_Fatal_Rates_Per_100000_Flight_Hours 10 number Rates are computed by dividing the number of and Total accidents, fatal by the number of Flight hours. Rates are computed by dividing the number of Total accidents, fatal by the number of Flight hours, except for the exclusions mentioned in footnote; Suicide, sabotage, and stolen/unauthorized cases.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data
tree JohnSnowLabs/united-states-general-aviation-safety-data
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/r/1.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/JohnSnowLabs/united-states-general-aviation-safety-data/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()
Datapackage.json