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:
There were a total of 22,483 FAA laser incident reports from the years 2010-2018 involving air carrier aircraft that also included information about the location of the encounter.
Over that nine-year span, on any given day in the United States, there was a 98.2% chance that at least one large capacity (60 seats or greater) airliner had a potentially dangerous encounter with a laser beam. In other words, in the 3,287-day period from 1 Janury 2010 to 31 December 2018, there were only 58 days without at least one reported laser incident involving a larger air carrier type aircraft.
There were an average of 6.84 laser encounters per day, with as many as 33 strikes in a single day.
Laser events were more likely on late Friday night and early Saturday morning, during the months of July through December, and from 0000-0600 hours UTC.
The average rate of reported laser incidents in the United States was 18.14 per 100,000 air carrier flights. There were 23 states and 17 MSAs with a higher encounter rate.
Southwest, the airline with the highest number of reported laser encounters, had an average of over one reported encounter per day.
Among the 50 states, Puerto Rico, and the District of Columbia, the reported rate of reported encounters, measured by number of encounters per 100K flights, ranged from a low of 2.52 for Alaska to a high of 992.65 for Delaware.
Among the 44 MSAs that were analyzed, the rate ranged form a , went from a low of 1.99 for Anchorage, AK to a high of 81.48 for the Riverside-San Bernardino-Ontario, CA Metropolitan Statistical Area, which is east of Los Angeles and includes the California counties of Riverside and San Bernardino counties.
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.
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.
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:
Airbus: A220, A300, A310, A318, A319, A320, A321, A330, A340, A350, A380
ATR: ATR 72
Boeing: 707, 727, 737, 747, 747, 757, 767, 777, 787
Boeing (McDonnell-Douglas): 717, DC-8, DC-9, DC-10, MD-10, MD-11, MD-80, MD-90
Bombardier (Canadair): C100, CRJ-700, CRJ-900
Bombardier (de Havilland): Dash 8
Embraer: ERJ 170, ERJ 175, ERJ 190
This report may be useful for the following kinds of groups:
Anyone who wants to educate flight crews, airport authorities, law enforcement and other relevant groups as to the extent of the problem (This could be done just cutting and pasting the graphics and basic stats in the report).
Organizations that deal with policies related to the commercial use of lasers, for example pushing for specific limits on what can be sold to the public.
Organizations currently involved in understanding or reducing aviation safety risks associated with inappropriate use of lasers.
Organizations, especially law enforcement agencies that, that seek to understand when and where the risk of laser strikes are highest.
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.
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:
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:
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:
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
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.
From 2010 to 2018, there were 22,483 encounters where a laser beam affected one or more aircraft at or near at least 1,286 unique airports or other locations.
During this nine-year period, there was an average of 6.84 laser encounters per day, with as many as 33 strikes in a single day. The median number of daily laser encounters was 4.
There were only 58 days from the nine-year period 2010-2018 with no reported laser strikes on aircraft in the United States. In other words, on any given day in the United States, there was a 98.2% chance that at least one aircraft will have a potentially dangerous encounter with a laser beam.
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.
A chi-square test was used to test the null hypothesis that the laser strikes were uniformly distributed by the day of the week or the month of the year. The null hypothesis was rejected in both cases because the p-value was much less than 0.05:
The distribution by both day of the week and month of the year can be displayed by a table and tested as well for uniformity.
The following table describes the distribution of laser encounters by day of the week and month of the year.
##
## Sun Mon Tue Wed Thu Fri Sat
## Jan 259 289 201 207 226 244 279
## Feb 184 201 180 165 179 228 256
## Mar 224 213 261 219 249 326 326
## Apr 227 221 217 209 234 261 267
## May 222 209 207 224 224 235 294
## Jun 191 221 243 226 229 275 294
## Jul 244 251 293 290 245 296 339
## Aug 271 278 288 274 295 282 331
## Sep 288 272 293 255 262 323 363
## Oct 283 270 315 306 292 318 385
## Nov 354 301 305 285 330 288 348
## Dec 317 289 302 285 326 342 363
As was the case when conducting a chi-square test on the distribution of laser encounters by day of the week or month of the year, when considering the two together, the null hypothesis of a uniform distribution of strike encounters by a combination of day of the week and month of the year would also be rejected (p-value of 0.000163.
It is also possible to visually depict this non-uniform distribution using heat maps. The heat map would reflect the data in the previous table, with 84 cells representing a combination of the month and day of the week. The colors correspond to a level of intensity with white being on the low end of the scale and dark blue on the upper end.
There are three ways to display this heat map, all of which use the data in Table 1. In the first option, the darkest cell corresponds to the month of the year and day of the week with the most laser encounters.
The above map shows that July through November tends to have more laser encounters, as does Friday and Saturday.
By scaling the heat map by the row values (month of the year), the darker cells in each row correspond to the days of the week with the most laser encounters. This means that a column that is consistently darker would correspond to the days of the week that is more likely to have laser encounters.
This second heat map show that for most months of the year, Friday and Saturday have a consistently higher number of laser encounter reports than other days of the week.
By scaling the heat map by the column values (day of the week), the darker cells in each column correspond to the months with the most laser encounters.
This third heat map shows that the months of July through December have consistently higher levels of laser encounter reports than the other eight months of the year.
Using the same table of data summarizing the distribution of strikes by a combination of month of the year and day of the week, the three heat maps highlighted the following:
Figure 6: The combination of month and day with relatively high numbers of laser encounters.
Figure 7: The days of the week with consistently higher levels of stirke reports throughout the year.
Figure 8: The months of the year with consistently higher levels of strike reports.
Together, the Table 1 and the three heat maps suggest a clear overall pattern of laser encounter reporting that is consistent with higher levels of laser encounters occurring from July through December, and also on Friday and Saturday.
These general trends were evident, but not as strongly, in the unscaled heat map. This suggest that the scaled heat maps are preferable for exposing laser encounter trends associated with particular days of the week or months of the year.
While laser encounters could potentially occur at any time of day, they would be most notable well after sunset or well before sunrise. That is reflected by the fact that for the US as a whole, about 81.9% of the reports were for encounters that occurred between 0000 and 0600 hours UTC, which would correspond to between 5 p.m. and 11 p.m in the Pacific Time Zone and 8 p.m. and 2 a.m. in the Eastern Time Zone during Daylight Savings Time, and an hour earlier during Standard Time.
##
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1 152 117 117 90 44 17 23 73 177 239 230 233
## 2 200 181 216 215 171 150 166 270 337 330 344 336
## 3 290 272 325 272 277 276 330 323 359 429 316 312
## 4 268 220 356 315 330 381 407 408 395 373 320 315
## 5 229 177 285 326 328 320 401 369 307 277 305 256
## 6 198 168 203 194 236 233 262 238 196 159 213 229
## 7 133 105 136 90 93 149 187 129 107 72 134 136
## 8 54 48 63 51 47 67 82 76 52 42 78 72
## 9 18 20 30 26 38 36 32 45 18 29 34 36
## 10 14 11 16 11 22 16 29 31 28 25 22 21
## 11 18 10 20 10 8 5 13 21 13 21 16 22
## 12 13 10 7 7 5 4 3 15 9 16 17 35
## 13 6 7 10 8 4 5 4 6 15 6 10 22
## 14 7 4 5 1 1 5 0 3 5 13 7 10
## 15 7 0 1 1 1 0 0 0 0 0 2 9
## 16 3 1 0 2 1 1 2 0 2 1 3 2
## 17 2 0 1 0 1 2 0 0 0 1 1 2
## 18 2 0 0 0 0 1 0 0 0 0 1 3
## 19 0 1 0 0 0 2 0 0 0 0 2 1
## 20 0 2 0 0 0 1 1 1 0 1 0 0
## 21 0 0 1 1 0 0 1 0 1 0 0 3
## 22 0 2 3 1 2 1 4 1 0 0 1 6
## 23 10 1 1 5 2 2 1 3 2 12 28 42
## 24 81 36 22 10 4 5 10 7 33 123 127 121
The heat map below, which is scaled by the month of the year (the x-axis) shows a seasonal shift in reporting, with with the concentration of reports shifting to later in the day from May to August.
Risk can be defined as the product of a hazard (such as damage costs) and the probability that this hazard occurs. In other words, (probability)x(hazard) = risk. In this study, the hazard is a laser encounter and the probability is the number of events per 100,000 flights.
This study examined two ways of comparing relative risk : comparing the rate of lasers encounters among the states of the US, and comparing the rate among the 44selected MSAs, including those representing the top 40 in population in 2018.
The selcted MSAs were used in part because air traffic isn’t concentrated in states as a whole, but rather in the portions of the state where the air traffic is concentrated. MSAs were also attractive because several of the major nodes of air carrier traffic, such as New York: Washington, DC, Chicago, and St. Louis, are in MSAs that cover more than one state.
The following table has information from all the states and territories orderd by:
## State Operations (M) Events Rate per 100K
## Alabama 0.42 148 35.50
## Alaska 1.19 30 2.52
## Arizona 3.69 1112 30.12
## Arkansas 0.30 68 22.42
## California 14.68 4991 34.00
## Colorado 4.29 489 11.40
## Connecticut 0.50 119 23.67
## Delaware 0.00 27 992.65
## District of Columbia 1.90 111 5.85
## Florida 11.17 1612 14.43
## Georgia 7.01 467 6.66
## Guam 0.20 14 6.98
## Hawaii 2.44 404 16.57
## Idaho 0.40 90 22.40
## Illinois 6.97 746 10.71
## Indiana 1.07 479 44.92
## Iowa 0.29 88 29.83
## Kansas 0.15 52 35.59
## Kentucky 1.75 774 44.32
## Louisiana 1.02 154 15.02
## Maine 0.21 25 11.95
## Maryland 1.90 240 12.63
## Massachusetts 2.49 234 9.40
## Michigan 2.66 313 11.79
## Minnesota 2.70 287 10.62
## Mississippi 0.13 48 37.71
## Missouri 2.17 289 13.32
## Montana 0.32 39 12.04
## Nebraska 0.42 53 12.70
## Nevada 3.52 674 19.17
## New Hampshire 0.21 34 16.08
## New Jersey 2.80 477 17.03
## New Mexico 0.52 157 30.01
## New York 7.38 796 10.79
## North Carolina 4.57 400 8.75
## North Dakota 0.13 16 12.70
## Northern Mariana Islands 0.05 0 0.00
## Ohio 1.49 280 18.73
## Oklahoma 0.67 157 23.38
## Oregon 1.67 684 40.90
## Pennsylvania 3.16 608 19.27
## Puerto Rico 0.70 452 64.60
## Rhode Island 0.31 65 20.86
## South Carolina 0.64 168 26.20
## South Dakota 0.11 17 15.97
## Tennessee 2.74 441 16.12
## Texas 12.01 2022 16.83
## Utah 1.72 442 25.75
## Vermont 0.11 14 12.42
## Virgin Islands 0.10 1 0.96
## Virginia 2.20 238 10.84
## Washington 3.81 691 18.15
## West Virginia 0.05 47 99.08
## Wisconsin 0.80 92 11.47
## Wyoming 0.08 7 9.23
## State Operations (M) Events Rate per 100K
## California 14.68 4991 34.00
## Texas 12.01 2022 16.83
## Florida 11.17 1612 14.43
## New York 7.38 796 10.79
## Georgia 7.01 467 6.66
## Illinois 6.97 746 10.71
## North Carolina 4.57 400 8.75
## Colorado 4.29 489 11.40
## Washington 3.81 691 18.15
## Arizona 3.69 1112 30.12
## Nevada 3.52 674 19.17
## Pennsylvania 3.16 608 19.27
## New Jersey 2.80 477 17.03
## Tennessee 2.74 441 16.12
## Minnesota 2.70 287 10.62
## Michigan 2.66 313 11.79
## Massachusetts 2.49 234 9.40
## Hawaii 2.44 404 16.57
## Virginia 2.20 238 10.84
## Missouri 2.17 289 13.32
## District of Columbia 1.90 111 5.85
## Maryland 1.90 240 12.63
## Kentucky 1.75 774 44.32
## Utah 1.72 442 25.75
## Oregon 1.67 684 40.90
## Ohio 1.49 280 18.73
## Alaska 1.19 30 2.52
## Indiana 1.07 479 44.92
## Louisiana 1.02 154 15.02
## Wisconsin 0.80 92 11.47
## Puerto Rico 0.70 452 64.60
## Oklahoma 0.67 157 23.38
## South Carolina 0.64 168 26.20
## New Mexico 0.52 157 30.01
## Connecticut 0.50 119 23.67
## Alabama 0.42 148 35.50
## Nebraska 0.42 53 12.70
## Idaho 0.40 90 22.40
## Montana 0.32 39 12.04
## Rhode Island 0.31 65 20.86
## Arkansas 0.30 68 22.42
## Iowa 0.29 88 29.83
## Maine 0.21 25 11.95
## New Hampshire 0.21 34 16.08
## Guam 0.20 14 6.98
## Kansas 0.15 52 35.59
## Mississippi 0.13 48 37.71
## North Dakota 0.13 16 12.70
## South Dakota 0.11 17 15.97
## Vermont 0.11 14 12.42
## Virgin Islands 0.10 1 0.96
## Wyoming 0.08 7 9.23
## Northern Mariana Islands 0.05 0 0.00
## West Virginia 0.05 47 99.08
## Delaware 0.00 27 992.65
## State Operations (M) Events Rate per 100K
## California 14.68 4991 34.00
## Texas 12.01 2022 16.83
## Florida 11.17 1612 14.43
## Arizona 3.69 1112 30.12
## New York 7.38 796 10.79
## Kentucky 1.75 774 44.32
## Illinois 6.97 746 10.71
## Washington 3.81 691 18.15
## Oregon 1.67 684 40.90
## Nevada 3.52 674 19.17
## Pennsylvania 3.16 608 19.27
## Colorado 4.29 489 11.40
## Indiana 1.07 479 44.92
## New Jersey 2.80 477 17.03
## Georgia 7.01 467 6.66
## Puerto Rico 0.70 452 64.60
## Utah 1.72 442 25.75
## Tennessee 2.74 441 16.12
## Hawaii 2.44 404 16.57
## North Carolina 4.57 400 8.75
## Michigan 2.66 313 11.79
## Missouri 2.17 289 13.32
## Minnesota 2.70 287 10.62
## Ohio 1.49 280 18.73
## Maryland 1.90 240 12.63
## Virginia 2.20 238 10.84
## Massachusetts 2.49 234 9.40
## South Carolina 0.64 168 26.20
## Oklahoma 0.67 157 23.38
## New Mexico 0.52 157 30.01
## Louisiana 1.02 154 15.02
## Alabama 0.42 148 35.50
## Connecticut 0.50 119 23.67
## District of Columbia 1.90 111 5.85
## Wisconsin 0.80 92 11.47
## Idaho 0.40 90 22.40
## Iowa 0.29 88 29.83
## Arkansas 0.30 68 22.42
## Rhode Island 0.31 65 20.86
## Nebraska 0.42 53 12.70
## Kansas 0.15 52 35.59
## Mississippi 0.13 48 37.71
## West Virginia 0.05 47 99.08
## Montana 0.32 39 12.04
## New Hampshire 0.21 34 16.08
## Alaska 1.19 30 2.52
## Delaware 0.00 27 992.65
## Maine 0.21 25 11.95
## South Dakota 0.11 17 15.97
## North Dakota 0.13 16 12.70
## Guam 0.20 14 6.98
## Vermont 0.11 14 12.42
## Wyoming 0.08 7 9.23
## Virgin Islands 0.10 1 0.96
## Northern Mariana Islands 0.05 0 0.00
## State Operations (M) Events Rate per 100K
## Delaware 0.00 27 992.65
## West Virginia 0.05 47 99.08
## Puerto Rico 0.70 452 64.60
## Indiana 1.07 479 44.92
## Kentucky 1.75 774 44.32
## Oregon 1.67 684 40.90
## Mississippi 0.13 48 37.71
## Kansas 0.15 52 35.59
## Alabama 0.42 148 35.50
## California 14.68 4991 34.00
## Arizona 3.69 1112 30.12
## New Mexico 0.52 157 30.01
## Iowa 0.29 88 29.83
## South Carolina 0.64 168 26.20
## Utah 1.72 442 25.75
## Connecticut 0.50 119 23.67
## Oklahoma 0.67 157 23.38
## Arkansas 0.30 68 22.42
## Idaho 0.40 90 22.40
## Rhode Island 0.31 65 20.86
## Pennsylvania 3.16 608 19.27
## Nevada 3.52 674 19.17
## Ohio 1.49 280 18.73
## Washington 3.81 691 18.15
## New Jersey 2.80 477 17.03
## Texas 12.01 2022 16.83
## Hawaii 2.44 404 16.57
## Tennessee 2.74 441 16.12
## New Hampshire 0.21 34 16.08
## South Dakota 0.11 17 15.97
## Louisiana 1.02 154 15.02
## Florida 11.17 1612 14.43
## Missouri 2.17 289 13.32
## Nebraska 0.42 53 12.70
## North Dakota 0.13 16 12.70
## Maryland 1.90 240 12.63
## Vermont 0.11 14 12.42
## Montana 0.32 39 12.04
## Maine 0.21 25 11.95
## Michigan 2.66 313 11.79
## Wisconsin 0.80 92 11.47
## Colorado 4.29 489 11.40
## Virginia 2.20 238 10.84
## New York 7.38 796 10.79
## Illinois 6.97 746 10.71
## Minnesota 2.70 287 10.62
## Massachusetts 2.49 234 9.40
## Wyoming 0.08 7 9.23
## North Carolina 4.57 400 8.75
## Guam 0.20 14 6.98
## Georgia 7.01 467 6.66
## District of Columbia 1.90 111 5.85
## Alaska 1.19 30 2.52
## Virgin Islands 0.10 1 0.96
## Northern Mariana Islands 0.05 0 0.00
In Table 5, the state of Delaware stands out because of its very high rate of laser encounters. This is due to its relativley low number of air carrier operations. In the nine years of the study, Delaware had only 2,720 air carrier flight operations, or 0.83, flights per day, compared to the next highest state or territory, West Virginia, which had 47,438 air carrier flight operations, or 14.43 flights per day.
The number of reported laser encounters in Delaware, was likely not due to air carrier operations within the state, but rather to Delaware’s locaiton within an area of high air carrier activity in the northeast US.
The FAA collected laser encounter data for a total of 54 states and territories. The most encounters were in California with 4,991. The encounters were concentrated in a few states, with only 14 states and territories having on average more than one reported laser encounter per week.
The following three maps illustrate the extent of the hazard in the continental US, as well as a key weakness of comparing rates of laser encounters by state.
The first heat map depicts the disribution of air carrier traffic by state.
The second heat map depicts the distribution of the the number of laser encounters by state.
The last heat map shows the relative rate of laser encounters per 100K air carrier flights.
There are three key issues with using traffic and laser encounter data on the state level:
The first type of issue is illutrated by the three heat maps depicted the continental US. In the first two heat maps, it is clear from the layout which states have a disproportionate share of air carrier traffic or reported laser encounters.
The third graphic illustrates all three of the key issues:
-This last graphic also does not show where the very active areas within a state may be, especially if an active area has flight operations and laser events spread over sevearal states.
An alternative to comparing rates by state is to do so with a different geographical grouping, specifically the Metropolitan Statistical Areas (MSAs) defined by the US Census Bureau. As mentioned before, all the MSAs were either among the top 40 in poplulation in 2018, or the MSA had a major hub airport for one or more airlines that was among the to ten in laser encounters. Those airlines are listed below:
## Airline Events Weekly rate
## SWA 4246 9.05
## AAL 2278 4.85
## DAL 2260 4.81
## UAL 2087 4.45
## SKW 1132 2.41
## ASA 1042 2.22
## JBU 984 2.10
## UPS 969 2.06
## FDX 929 1.98
## RPA 657 1.40
Those airlines were in order: Southwest, American, United, Skywest, Alaska, jetBlue, UPS, FedEx, and Republic.
Based on the locations of the hubs or major operating locations of these 10 airlines, the following MSAs were added from outside the top 40:
A total of 44 metropolitan areas were reviewed to determine the laser event risk of a general geographic area. Each metropolitan area included any airport or other location with at least one air carrier event, or one air carrier operation.
The following 44 metropolitan statistical areas (MSAs) were analyzed to determine the number and rate of laser events in those areas: Anchorage, Atlanta, Austin, Baltimore, Boston, Charlotte, Chicago, Cincinnati, Cleveland, Columbus, Dallas, Denver, Detroit, Houston, Indianapolis, Kansas City, Las Vegas, Los Angeles, Louisville, Memphis, Miami, Milwaukee, Minneapolis, Nashville, New York, Orlando, Philadelphia, Phoenix, Pittsburgh, Portland, Providence, Riverside, Sacramento, Saint Louis, Salt Lake City, San Antonio, San Diego, San Francisco, San Jose, San Juan, Seattle, Tampa, Virginia Beach, Washington_DC
The following table has metropolitan areas ranked by laser encounter rate, and also includes all metro areas, all non-metro areas, and the US as a whole for comparison.
## Metro_area Events Traffic Rate_per_100K Rate_ratio_US
## Riverside 535 656637 81.48 4.49
## San Juan 419 638579 65.61 3.62
## San Jose 530 874226 60.63 3.34
## Portland 592 1476409 40.10 2.21
## San Diego 571 1488162 38.37 2.12
## Louisville 323 944739 34.19 1.88
## Indianapolis 308 986474 31.22 1.72
## US_non_metro 5730 18555857 30.88 1.70
## San Antonio 228 833392 27.36 1.51
## Phoenix 921 3368639 27.34 1.51
## Los Angeles 1697 6388176 26.56 1.46
## Providence 67 311622 21.50 1.19
## Salt Lake City 361 1707978 21.14 1.17
## San Francisco 810 4154909 19.50 1.07
## Sacramento 158 842017 18.76 1.03
## Las Vegas 578 3151327 18.34 1.01
## Nashville 178 980626 18.15 1.00
## US_all 22483 123972735 18.14 1.00
## Houston 674 3767755 17.89 0.99
## Cincinnati 129 734790 17.56 0.97
## Philadelphia 364 2203649 16.52 0.91
## Seattle 525 3238263 16.21 0.89
## US_metro 16753 105416878 15.89 0.88
## Pittsburgh 116 768673 15.09 0.83
## Austin 194 1310035 14.81 0.82
## Miami 752 5344223 14.07 0.78
## Tampa 196 1431852 13.69 0.75
## Orlando 370 2723988 13.58 0.75
## Columbus 78 586785 13.29 0.73
## Kansas City 127 957481 13.26 0.73
## Cleveland 75 600692 12.49 0.69
## Memphis 178 1596029 11.15 0.61
## Baltimore 209 1892985 11.04 0.61
## New York 982 8998929 10.91 0.60
## Chicago 745 6889337 10.81 0.60
## Denver 426 4002331 10.64 0.59
## Virginia Beach 29 273113 10.62 0.59
## Saint Louis 117 1146683 10.20 0.56
## Detroit 233 2298097 10.14 0.56
## Minneapolis 264 2673499 9.87 0.54
## Dallas 531 5543914 9.58 0.53
## Boston 207 2460021 8.41 0.46
## Milwaukee 49 582887 8.41 0.46
## Washington_DC 273 3446140 7.92 0.44
## Charlotte 247 3375703 7.32 0.40
## Atlanta 368 6810651 5.40 0.30
## Anchorage 19 954461 1.99 0.11
The following 34 states and territories were included in one or more MSAs: Alaska, Arizona, California, Colorado, Delaware, District of Columbia, Florida, Georgia, Illinois, Indiana, Kansas, Kentucky, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nevada, New Hampshire, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin
The airports in the included MSAs had 85% of all air carrier traffic, 74.5% of the laser encounter events, and a laser encounter rate of 15.89 events per 100K air carrier flight operations
The following 21 states and territories did not have territory in any of the included: Alabama, Arkansas, Connecticut, Guam, Hawaii, Idaho, Iowa, Louisiana, Maine, Mississippi, Montana, Nebraska, New Mexico, North Dakota, Northern Mariana Islands, Oklahoma, South Carolina, South Dakota, Vermont, Virgin Islands, Wyoming.
The areas outside of any MSA had 15% of all air carrier traffic, 25.5% of the air carrier laser encounter events, and a laser encounter rate of 30.88 events per 100K air carrier flight operations.
The laser encounter data collected by the FAA represents an important resource for understanding the kinds of current and future risks that aircraft face from lasers. This study was focused on the distribution of laser encounters and how visual summaries such as heat maps can be used in conjunction with statistical measurements to communicate the scale and scope of the problem.
The raw data provided by the FAA required further processing before it could used in this study, and that data cleaning processed revealed a number of issues with the data collection process, including inconsistencies in how airport identifiers are used.
The patterns of laser encounters revealed in this analysis suggested that likelihood of encounters strongly related to the time of day, and somewhat less strongly related to the day of the week or the month of the year. Also, while the laser encoutner rate is significantly higher than the national average for many of the largest US Census-defined Metropolitan Statistical areas airports, the risk of air carrier aircraft exposure to lasers is present throughout the US.
Variations in encounter rates among the states were evaluated, as were variations among the largest metropolitan areas. It was not possible to observe regional differences within a state, particularly large states like California and Texas, and the Census-defined MSAs often cross state boundaries.
As a group, the 44 MSAs, which included the top 40 MSAs in populaiton,accounted for 85% of all air carrier traffic, had a lower rate of rate of laser encounters than other parts of the US, which included rural areas as well as metropolitan areas with smaller populations.
While one could speculate about factors that led to the differences in laser encounter rates, without additional information, the fact that the differences are sometimes an order of magnitude or more between different geographical areas is perhap reason enough to further investigate this scale and scope of the risk.
Laser Illumination of Flight Crew Personnel by Month, Day of Week, and Time of Day for a 5-Year Study Period: 2004-2008 DOT/FAA/AM-11/7 https://www.faa.gov/data_research/research/med_humanfacs/oamtechreports/2010s/media/201107.pdf
FAA’s Air Traffic Activity System (ATADS) https://aspm.faa.gov/opsnet/sys/Airport.asp
Laser Incidents (FAA) https://www.faa.gov/about/initiatives/lasers/laws/
Reported Laser Incidents for 2010-2014 (FAA) https://www.faa.gov/about/initiatives/lasers/laws/media/laser_incidents_2010-2014.xls
Processed Laser Incident data used in this study http://www.airsafe.com/analyze/faa_laser_data_2010_2018.csv
US Census Bureau Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2018 https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html
Air Traffic Activity System (FAA) https://aspm.faa.gov/opsnet/sys/Airport.asp
Patterns of laser strikes on US aircraft: 2010 to 2018 (this report)