Summary

This study reviewed nine years of reports (2010-2018) compiled by the Federal Aviation Administration (FAA) concerning unauthorized laser illuminations of larger capacity transport aircraft (airliners, whether used for passenger or cargo service, that are designed to carry 60 or more passengers) in the US.

Among the findings was that the rate of reported laser events varies widely across the US (the 50 states, the District of Columbia, Puerto Rico, Guam, and other US territories), whether one looks at the rates in individual states or if one looks at the rates within selected US Census-designated Metropolitan Statistical Areas (MSAs).

In addition to examining the reporting rates, some of the insights from a review of the data included the following:

Background

In the US, the Federal Aviation Administration has long recognized that unauthorized laser illuminations of aircraft may have numerous hazardous effects on aircrew, including distraction, glare, afterimage, flash blindness, and, in extreme circumstances, persistent or permanent visual impairment (FAA Advisory Circular 70-2A).

As part of their effort to deal with the hazards posed by lasers, the FAA has encouraged air crew members, air traffic controllers, and the general public to submit reports of aircraft being illuminated by lasers. The FAA has collected this kind of data since at least 2004, and in 2011 published a study (Report DOT/FAA/AM-11/7) that analyzed 2,492 laser events that occurred in the US from 2004-2008.

Since 2008, the FAA has received substantially more reports. [The FAA’s Laser News, Laws, & Civil Penalties page] (https://www.faa.gov/about/initiatives/lasers/laws/) provides a link to Excel files of data covering 2010-2018.

Focus of this study

While the events in the FAA databases included reports from all sectors of aviation, including military operations, helicopters, and lighter-than-air aircraft, this study focused on events involving civilian operations involving large transport aircraft that were designed to carry 60 or more passengers. This in part due to the greater potential risk to both life and property when this kind of aircraft is struck by a laser.

Aircraft models and air traffic used in this study

Because this study focused on larger airliner type aircraft, the FAA traffic data used to compute laser encounter rates helped define what models were included. The airport traffic data was taken from the (FAA’s Operations Network (OPSNET)) site and covered the years 2010-2018.

The study focused on air carrier laser encounters, and only used air carrier traffic information. FAA defines air carrier aircraft as aircraft with a seating capacity of more than 60 seats or a maximum payload capacity of more than 18,000 pounds, carrying passengers or cargo for hire or compensation.

The events in this study, all of which were involved in civil aircraft flight operations, involved the following aircraft models:

Who is this report for?

This report may be useful for the following kinds of groups:

Methods

After downloading the data and removing records that did not contain sufficient information on the location of the laser encounter, the data was processed in order to summarize the likelihood of a laser encounter by geographical area (specifially, states, territorries, and selected metropolitan areas), time of day, day of the week, and month of the year. Heat maps were used to help illustrate these relationships.

Laser encounter data preparation

The raw laser encounter data was included in an Excel file with each sheet containing information for one calendar year. The various sheets for 2010 to 2018 (the last available complete year at the time of this study) were combined to form one CSV file. There were several variables included for each record, including the following that were used in this study:

  • Date
  • Time (UTC)
  • Aircraft ID
  • Aircraft type
  • Location (IATA or ICAO code)
  • City
  • State

The raw data file from the FAA contained numerous cases of incorrect data with respect to location (airport, city, and state), including misspellings and capitalization errors,as well as missing data. The events were manually reviewed to correct these errors when sufficient information was contained in the rest of the record.

Also, for consistency, locations were identified using the three-character IATA codes when they were available for an airport, navigation aid, or other location. Where IATA codes were not available, four-character ICAO codes were used.

Because part of this study focused on air carrier related laser events in selected metropolitan areas, part of the data preparation included adding three variables:

  • Event_ID: A uniuqe identifier

  • Aircraft_Type: Category variable for type of aircraft

  • Metro_area: Identifier for a Metropolitan Statistical Areas (MSAs) as identified by the US Census publication Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2018.

The MSAs used met one or more of the following criteria:

  • The MSA was in the top 40 in population in 2018
  • The MSA contained an airport that was a hub or major base of operations for one of the top 10 airlines by air carrier traffic

Laser encounter preliminary data cleaning

In addition to adding the above variables to each record, the raw data file from the FAA contained numerous cases of missing or incorrect data. The events were manually reviewed to correct these errors when sufficient information was contained in the rest of the record. The following types of changes were made:

  • Incorrect, misspelled, or missing data for any variable was corrected or filled in when enough informaiton was available in the record.

  • Removing duplicate records (also done a second time within the R program)

  • Missing, incorrect, or incomplete data that could not be found or corrected were coded as “UNK”.

  • Airports, navigational aids, and other locations identified using the three-character IATA codes or four-character ICAO codes whenever possible,

  • In some cases, the airport location code was substituted for a navigational aid code when they were located at or near an airport. For example, sevearl reports for the city of Baltimore, MD used the ‘BAL’ IATA code, which is for the VORTAC at the field, and the arport code ‘BWI’ was substituted.

  • Reported Laser colors were standardized by making all inputs with multiple identified colors of the form Color1/Color2, with the colors listed alphabetically, insuring that the first letter in a single word color identifier was capitalized, and correcting misspellings. Example: Blue and Green, became Multiple (Blue, Green), and Blue or green become Multiple (Blue or Green)

A variety of resources were used to identify key data for some records, including:

Preprocessed data and data dictionary

This preprocessed data is made available at http://www.airsafe.com/analyze/faa_laser_data_2010_2018.csv

The data dictionary that describes the variables in each record is available at http://www.airsafe.com/analyze/faa_laser_data_dictionary.pdf

Data transformation

Additional data transformations and changes would occur after uploading:

Once the revisions were complete, a total of 18 duplicated records were removed.

Further processing changed the UTC times to an integer from one to 24 to coincide with the hour of occurrence. Additional variables were added for the day of the week and the month corresponding to the date.

Dates in the FAA data were in form 5-Jan-06, and were converted to the date format of yyyy-mm-dd. The converted dates were used to create two additional variables based on the date, the day of the week and, the month of the year, to ensure proper ordering, the two new variables were made into factors and ordered as they would be in a calendar.

There are initially 22483 total records, but only 22483 records have data in the most important variables: Date, Time, Aircraft_Type, Altitude, City, State.

Quick summary of the data

Below are several summary graphics illustrating the distribution of laser encounters by:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    4.00    6.00    6.84    9.00   33.00

The following histograms illustrate the distribution of encounters by year, month, day of the week, and time of day (UTC) respectively.