Exploratory Spatial Analysis of Pilot Fatality Rates in General Aviation Crashes Using Geographic Information Systems

Jurek G. Grabowski1, Frank C. Curriero2, Susan P. Baker3 and Guohua Li1

1 Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD.
2 Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
3 Center for Injury Research and Policy, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Geographic information systems and exploratory spatial analysis were used to describe the geographic characteristics of pilot fatality rates in 1983–1998 general aviation crashes within the continental United States. The authors plotted crash sites on a digital map; rates were computed at regular grid intersections and then interpolated by using geographic information systems. A test for significance was performed by using Monte Carlo simulations. Further analysis compared low-, medium-, and high-rate areas in relation to pilot characteristics, aircraft type, and crash circumstance. Of the 14,051 general aviation crashes studied, 31% were fatal. Seventy-four geographic areas were categorized as having low fatality rates and 53 as having high fatality rates. High-fatality-rate areas tended to be mountainous, such as the Rocky Mountains and the Appalachian region, whereas low-rate areas were relatively flat, such as the Great Plains. Further analysis comparing low-, medium-, and high-fatality-rate areas revealed that crashes in high-fatality-rate areas were more likely than crashes in other areas to have occurred under instrument meteorologic conditions and to involve aircraft fire. This study demonstrates that geographic information systems are a valuable tool for injury prevention and aviation safety research.

accidents; aviation; geography; information systems; Monte Carlo method

Abbreviations: NTSB, National Transportation Safety Board; SD, standard deviation


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
General aviation is typically defined as any noncommercial, civil aviation that includes, but is not limited to, recreational flying, pilot training flights, aerial acrobatics, aerial application of insecticides or herbicides, aerial survey, sight-seeing, and aerial search and rescue. As of 1996, there were 187,312 registered general aviation aircraft in the United States that flew a total of 26 million flight hours. Every year, the National Transportation Safety Board (NTSB; Washington, DC) records approximately 2,000 general aviation crashes, with an average of 765 fatalities. Even with the recent decline in the number of general aviation crashes, they remain the predominant source of aviation-related fatalities. For example, in 1996, 98 percent of all aviation crashes and 66 percent of all aviation fatalities were related to general aviation (1Go).

In recent years, four studies have delineated the association between aviation crashes and geography but were limited in several respects (2GoGoGo–5Go). First, analyses were based on state populations (4Go, 5Go) or on pilot flight hours (2Go). To our knowledge, the severity of crashes, as reflected in crash fatality rates, has not been studied in relation to their geographic occurrence except in a single state, Colorado, where research found that 32 percent of the crashes in mountainous terrain within 50 nautical miles (1 mile = 1.85 km) of Aspen, Colorado, were fatal compared with 19 percent in the rest of Colorado (3Go). Second, conclusions were aggregated to the state level, a political classification of geography that does not, for example, allow comparison of specific mountainous regions, thus increasing the possibility of overlooking extremely localized areas of higher-than-average rates within individual states or that cross state lines. Third, variation in the spatial distribution of aviation fatalities was not studied by using geographic information systems and spatial statistics.

The aim of this study was to examine the geographic distribution of crash fatalities of general aviation pilots. In this paper, spatially referenced crash data are displayed in detailed contour maps, and geographic variations in pilot fatality rates are assessed statistically on the basis of Monte Carlo simulations.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Data for this study were taken from the NTSB factual reports on crashes that occurred between 1983 and 1998. Under current US federal regulations, aircraft operators are obligated to notify the NTSB immediately of any event in which any person aboard an operational civil aircraft is involved in a death or injury and/or in which the aircraft receives substantial damage. The NTSB then conducts crash investigations to recommend safety practices and regulations. Data collected during an investigation include pilot demographics, aircraft characteristics, and crash circumstances (such as time, location, and weather conditions at the time of the crash). To ensure unbiased and complete reporting of their investigation, the NTSB has no legislative, regulatory, or enforcement powers.

The NTSB recorded 35,570 general aviation crashes between 1983 and 1998. Excluded from our study were 1,335 crashes involving gliders, balloons, blimps, ultralights, and gyroplanes; 2,931 crashes occurring in international waters, Puerto Rico, Alaska, or Hawaii; and 3,160 crashes for which longitude and/or latitude data were missing. Consequently, crash data were analyzed for only flights of airplanes and helicopters conducted under Title 14 of the Code of Federal Regulations Part 91 in the continental United States (refer to the following Internet Web site for more information: http://www.access.gpo.gov/nara/cfr/index.html). Because of lower speeds and more controlled descents and impacts during taxiing, take-off, and landings, crashes on airport property are one sixth as likely to result in a pilot fatality as a crash occurring away from airport property (6GoGo–8Go). Therefore, 14,093 crashes that occurred on airport property were also excluded from our analysis.

Crashes were coded as fatal or nonfatal according to pilot-in-command casualty and were then plotted on an electronic map of the continental United States by using the crash-site longitudes and latitudes provided by the NTSB. To estimate the crash fatality rate for any one location, a grid, with intersection points 50 miles apart, was created over the crash-site map. For each grid intersection, a circular spatial filter with a 50-mile radius from the grid intersection was drawn, and the numbers of fatal crashes and nonfatal crashes within the circle were counted. Pilot crash fatality rates were calculated for each grid intersection by dividing the number of fatal crashes within a filter by the total number of crashes within the same filter. By repeatedly testing various radii, we determined that the majority of filters with a 50-mile radius contained at least 10 crashes. If a filter had nine or fewer observed crashes, rates were not calculated directly but were interpolated from neighboring grid intersections that had 10 or more observations (9Go). Interpolation was possible because of the overlap of approximately 25 percent of the area of one circular filter with its neighboring filter. The 50-mile-radius filter was sufficient to include enough crashes so that a fatality rate could be estimated while also capturing the variability in the spatial pattern of these rates. For comparison, pilot crash fatality rates per state were also calculated, and results are presented in table 1.


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TABLE 1. Pilot fatality rates per 100 general aviation crashes, by state, United States, 1983–1998

 
Given the spatial pattern of general aviation crashes, hypothesis tests were performed for the computed fatality rates at each grid intersection to ascertain how likely the distribution of fatal crashes was due to chance alone. For each crash location, Monte Carlo simulations determined whether a crash site was fatal or nonfatal; the overall national general aviation crash fatality rate was used as the probability of a crash fatality. Then, the rate estimation procedure described above was applied to estimate rates for each grid intersection using the Monte Carlo simulated data set. Repeating this process 1,000 times provided a distribution of 1,000 estimated fatality rates for each grid intersection. The percentage of these rates above the computed rate for each grid intersection provided Monte Carlo p values testing for general aviation crash fatality rates that were significantly high. The procedure to estimate rates and to assess the significance of estimated rates by using Monte Carlo simulations is discussed further by Rushton et al. (10Go, 11Go).

Once fatality rates were calculated, all crash sites were categorized as occurring within low-fatality-rate areas, medium-fatality-rate areas, or high-fatality-rate areas. Since the range of fatality rates among all states was observed to be 19–39 percent, low-fatality-rate areas were defined as having fatality rates of less than 19 percent. High-fatality-rate areas were defined as having fatality rates higher than 39 percent. Chi-square tests and one-way analysis of variance tests were used to determine whether factors related to pilot characteristics, aircraft characteristics, and crash circumstance were disproportionately distributed among the low-, medium-, and high-rate areas. Significance was achieved if p values were 0.05 or less. All p values presented in the study were based on two-sided tests. Chi-square tests were also used to determine whether factors associated with fatal crashes were disproportionately distributed among the low-, medium-, and high-rate areas.

The data were analyzed by using SAS software (12Go). The Distance Mapping and Analysis Program (DMAP) was applied to estimate fatality rates by creating grids and variable spatial filters; it was also used to test for statistical significance with Monte Carlo simulations (13Go). Interpolated geographic data were displayed by using ArcView GIS software (14Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
During the 16-year period under study, 31 percent of the 14,051 general aviation crashes were fatal to the pilot. Of the 1,232 filters created within the continental United States, 1,080 had a calculated rate (at least one fatality and 10 crashes), five had a calculated rate equal to zero (no fatalities and at least 10 crashes), and the remaining 147 had no crashes or fewer than the required 10 crashes needed for rate calculation. Many of these 147 filters were located near the borders of Canada, Mexico, the Gulf of Mexico, the Atlantic Ocean, and the Pacific Ocean so that approximately 50 percent of the filter areas lay outside the continental United States. A visual inspection of plotted crash sites (map not shown) showed 27 large areas of potential high-density clustering of aviation crashes, particularly in the northeast portion and on the West Coast of the United States. Although California, Texas, and Florida had the most crashes (1,873, 1,000, and 902, respectively), Nevada, Connecticut, Tennessee, and Louisiana had the highest crash fatality rates: 36–39 pilot fatalities per 100 crashes. Kentucky, South Carolina, Kansas, Iowa, and North Dakota had the lowest pilot crash fatality rates: 18–26 pilot fatalities per 100 crashes (table 1).

The contour map of pilot crash fatality rates showed that rates within the continental United States varied noticeably, even within individual states (figure 1). Seventy-four geographic areas within the continental United States were categorized as having low fatality rates. Fifty-three geographic areas had higher-than-average rates (40–90 fatalities/100 crashes). One such area in northeast Tennessee included portions of six states (figure 2).



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FIGURE 1. Interpolated map of pilot fatality rates per 100 general aviation crashes (A) and probability map from Monte Carlo simulations (B) for general aviation, continental United States, 1983–1998.

 


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FIGURE 2. Interpolated map of pilot fatality rates per 100 general aviation crashes (A) and probability map from Monte Carlo simulations (B) for general aviation, northeast Tennessee and surrounding areas, 1983–1998.

 
Monte Carlo simulations were used to determine whether areas with high pilot crash fatality rates truly distinguish areas in which the risk is higher than the overall national rate of 31 fatalities per 100 crashes. Figure 2B maps the Monte Carlo p values corresponding to the estimated rates for some of the areas shown in figure 1A. Four large areas of the continental United States (northern California, southern California, central Oklahoma, and northern Tennessee) had significantly high pilot crash fatality rates. Many smaller areas concentrated in the Rocky Mountains and the Appalachian region also had significantly high pilot crash fatality rates.

A total of 1,077 crashes occurred in the high-fatality-rate areas, of which 533 resulted in pilot death, a pilot fatality rate of 49.5 per 100 crashes. In the low-fatality areas, 736 crashes resulted in 93 deaths, a pilot fatality rate of 12.6 per 100 crashes.

The average age of pilots increased from 42 years in low-fatality areas to 44 years in medium-fatality areas and 45 years in high-fatality areas. The average crash site elevation was 2,029 feet (standard deviation (SD), 2,108 feet) (1 foot = 30.48 cm) in low-fatality-rate areas, 1,500 feet (SD, 2,085 feet) in medium-fatality-rate areas, and 3,342 feet (SD, 3,132 feet) in high-fatality-rate areas. One-way analysis of variances was significant for age and elevation (F = 8.2, p < 0.001; and F = 140, p < 0.0001, respectively).

Compared with crashes in low-fatality areas, those in high-fatality-rate areas were twice as likely to be associated with flying during instrument meteorologic conditions (adverse weather) (p < 0.0001), to involve twin-engine aircraft (p = 0.003), to occur at an elevation of 2,000 feet or more (p < 0.0001), and to result in fire (p < 0.0001) (table 2).


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TABLE 2. Proportions of general aviation crashes in low-, medium-, and high-rate areas, by pilot characteristic, aircraft characteristic, and crash circumstance, continental United States, 1983–1998

 
Postcrash pilot survival in both low- and high-fatality-rate areas was significantly associated with crash circumstances (table 3). In low-fatality-rate areas, a fatality was the result in 46 percent of the crashes involving a fire versus 8 percent of the crashes without a fire ({chi}2 = 92.1, p < 0.0001). In high-fatality-rate areas, a fatality occurred in 83 percent of the crashes involving a fire compared with 40 percent of the crashes without a fire ({chi}2 = 128.4, p < 0.0001). Crashes that occurred while flying under instrument meteorologic conditions were twice as likely to be fatal as those occurring during visual meteorologic conditions in high-fatality-rate areas ({chi}2 = 123.8, p < 0.0001). Fatality rates in high-rate areas also increased significantly for crashes occurring at night, crashes in which the pilot did not wear shoulder restraints, and crashes involving helicopters or multiengine aircraft ({chi}2 = 17.0, 9.9, 25.6, and 30.2, respectively; p < = 0.002 for all). Although similar increasing trends were observed for the low-rate areas, the proportion of fatalities in these areas was lower.


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TABLE 3. General aviation pilot fatalities, by variables related to aircraft and crash circumstance, in low- and high-fatality-rate areas of the continental United States, 1983–1998

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Geographic health studies are recognized as powerful tools for exposing etiologies or describing disease experience in order to study the genetic and environmental influences of a particular infectious disease. With the advent of easily obtainable spatial data, spatial statistics, and computer technology, geographic health research has become an attractive method for depicting spatial relations between injury and geography.

Unlike tabular data aggregated to a state level, this study created digital maps that instantly conveyed visual information on the spatial distribution of pilot fatality rates so that areas with subtle patterns of high rates could be identified quickly. This distinction is important because state borders did not coincide with the borders of areas with high case fatality rates. Thus, mapping rates into predefined areas, such as choropleth maps of states, would have concealed critical spatial patterns. For example, table 1 shows that Tennessee, Kentucky, Virginia, North Carolina, South Carolina, and Georgia had pilot crash fatality rates of 26–36 per 100 crashes. However, certain areas within these states had pilot crash fatality rates ranging from 40 to 52 per 100 crashes (figure 2).

Although this study was an exploratory evaluation of spatially distributed measures of pilot fatality rates, it provides strong etiologic insight into crash patterns undetectable by nongeographic epidemiologic methodology, bolstering the notion that patterns of fatalities do not occur at random locations. Our results indicate that the areas with high pilot crash fatality rates directly mirror the physiographic regions of the United States. Specifically, higher rates were found in the rugged terrain of the Pacific Mountain System, Intermontane Plateaus, Rocky Mountain System, Interior Highlands, Appalachian region, and the Canadian Shield region, and low-to-medium rates were found in the flat terrain of the Interior Plains and Atlantic Coastal Plains.

In areas with high fatality rates, it was found that pilot fatality was associated with multiengine aircraft. Survivability may decrease because the more powerful multiengine planes tend to impact at higher G-forces than single-engine aircraft do (8Go). Rugged, mountainous areas and bad weather also may contribute to decreased pilot survivability by increasing the length of time required for each of the following phases of search-and-rescue operations: time to search-and-rescue notification, time to accurately locate the crash site, response time to the crash scene, stabilization of the crash site, time for occupant extraction to the rescue vehicle, and time of transport to the hospital (15Go).

It is noteworthy that this spatial analysis is limited to the risk of pilot fatality given a crash. Geographic patterns of crash risk remain unaddressed because of the lack of exposure data. Although the Monte Carlo simulations provide an approach to understanding the statistical significance of the geographic patterns, a certain level of subjectivity is involved when the locations of grid intersections are created and the radius of the spatial filter is chosen. We evaluated this subjectivity by repeating the methods described above using grid intersections and radii of the spatial filters equal to 100 miles and then 10 miles. We observed that a larger filter size resulted in fewer areas with high rates and smaller filters resulted in many smaller areas with high rates; however, almost identical rate patterns were observed when mapped. Since many of the 147 filters without the required 10 crashes needed to calculate the fatality rate were located near the borders of Canada, Mexico, the Gulf of Mexico, the Atlantic Ocean, and the Pacific Ocean in such a way that approximately 50 percent of the filter area was outside US borders, border effect must be considered when the results are interpreted.

While it is impossible to eliminate all the danger inherent in flight, the number of fatalities and the severity of injury associated with general aviation crashes could be reduced. Protective structure and equipment modifications should enable general aviation aircraft to sustain the higher G-force impact encountered in many rough-terrain crashes. New technologies, such as automatic collision notification sensors, which detect collisions and automatically transmit information on the exact location, time, date, and force of the collision to a central monitoring station via the automatic vehicle location and global positioning systems satellite network, should be developed and regulated as mandatory equipment in general aviation aircraft. In turn, fire, police, and emergency medical response centers should be equipped with Automatic Vehicle Location and Global Positioning Systems receiver systems so they can effectively dispatch emergency vehicles closest to the crash site, thereby reducing response time. This reduction in response time could save lives. To further reduce search time in rugged and rural areas, all rescue aircraft should incorporate thermal and night vision technology as standard equipment.

Demonstration of high-fatality-rate areas supports the need for greater regional prevention efforts, beginning with pilot education. Pilots should be instructed on medical first aid, proper mountain flying techniques, emergency locator transmitters use and maintenance, and the importance of keeping a fully charged cellular phone on board. Pilots likely to fly over high-risk areas should also take advantage of survival courses that deal specifically with harsh climate and terrain.


    ACKNOWLEDGMENTS
 
Grants RO1AG13642 and R01AA09963 from the National Institutes of Health and grant R49CCR302486 from the Centers for Disease Control and Prevention supported this research.

The authors thank the National Transportation Safety Board for assistance in providing data.


    NOTES
 
An earlier version of this study was presented at the Annual Meeting of the American Public Health Association in Boston, Massachusetts, November 12–16, 2000.

Reprint requests to Dr. Guohua Li, Department of Emergency Medicine, The Johns Hopkins University School of Medicine, 1830 East Monument Street, Suite 6-100, Baltimore, MD 21205 (e-mail: ghli{at}jhmi.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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  5. Baker S, O'Neill B, Ginsburg M, et al. The injury fact book. 2nd ed. New York, NY: Oxford University Press, 1992:107.
  6. Li G, Baker SP. Crashes of commuter aircraft and air taxies: what determines pilot survival? J Occup Med 1993;35:1244–9.[ISI][Medline]
  7. Li G, Baker SP. Correlates of pilot fatality in general aviation crashes. Aviat Space Environ Med 1999;70:305–9.[ISI][Medline]
  8. Rostykus PS, Cummings P, Mueller BA. Risk factors for pilot fatalities in general aviation airplane crash landings. JAMA 1998;280:997–9.[Abstract/Free Full Text]
  9. Nobre F, Macedo M. Feasibility of contour mapping epidemiological data with missing values. Stat Med 1995;14:605–13.[ISI][Medline]
  10. Rushton G, Krishnamurthy R, Krishnamurti D, et al. The spatial relationship between infant mortality and birth defect rates in a US city. Stat Med 1996;15:1907–19.[ISI][Medline]
  11. Rushton G, Lolonis P. Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 1995;15:717–26.[ISI]
  12. SAS Institute, Inc. SAS version 8 software. Cary, NC: SAS Institute Inc, 1999.
  13. Distance Mapping and Analysis Program. Iowa City, IA: The University of Iowa, Department of Geography. (http://www.uiowa.edu/~geog/health/index.html).
  14. Environmental Systems Research Institute, Inc. ArcView GIS 3.2 software. Redlands, CA: Environmental Systems Research Institute, Inc, 1999.
  15. Li G, Baker SP, Dodd RS. The epidemiology of aircraft fire in commuter air carrier and air taxi crashes Aviat Space Environ Med 1996;67:434–7.[ISI][Medline]
Received for publication January 17, 2001. Accepted for publication August 29, 2001.





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