Number Of Pilot Reported Near Midair Collisions By Degree Of Hazard

JohnSnowLabs

Files Size Format Created Updated License Source
2 40kB 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
number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard-csv 1kB csv (1kB) , json (7kB)
number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 5kB zip (5kB)

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.

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/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard
tree JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/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/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/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/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/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/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard/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/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 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