Number Of Pilot Reported Near Midair Collisions By Degree Of Hazard

JohnSnowLabs

Files Size Format Created Updated License Source
2 40kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics
Download

Data Files

number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard-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_Collision_All_Degrees_Of_Hazard 2 integer Total number of Pilot-Reported Near Midair Collisions (NMAC) by Degree of Hazard. A situation where collision avoidance was due to chance, rather than an act on the part of the pilot.
Critical_Collisions 3 integer Number of Near Midair Collisions (NMAC) of critical degree of hazards. A situation where collision avoidance was due to chance, rather than an act on the part of the pilot. Less than 100 feet of aircraft separation would be considered critical.
Potential_Collisions 4 integer Number of Near Midair Collisions (NMAC) of potential degree of hazards
No_Hazard_Collisions 5 integer Number of Near Midair Collisions (NMAC) of no degree of hazards
Unclassified 6 integer Number of Near Midair Collisions (NMAC) of unclassified degree of hazards.
Near_Mid_Air_Collision_Involving_Aircraft_Operating_Under_14_CFR_121 7 integer Number of Near mid air collision. Before Mar. 20, 1997, 14 CFR 121 applied only to aircraft with more than 30 seats or a maximum payload capacity of more than 7,500 pounds. Since Mar. 20, 1997, 14 CFR 121 includes aircraft with 10 or more seats that formerly operated under 14 CFR 125. This change makes it difficult to compare pre-1997 data with more recent years' data.

number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard_zip  

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

Read me

Import into your tool

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

install.packages("jsonlite")
library("jsonlite")

json_file <- "http://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))
print(data)

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

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('http://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/datapackage.json')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

data = package.resources[0].read()
print(data)

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 = 'http://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = 'http://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json