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An environmental analysis of public
UAP sightings and sky view
potential
R. M. Medina
1*, S. C. Brewer
1 & S. M. Kirkpatrick
2
Sightings of unidentied ying objects (UFOs) or unidentied anomalous phenomena (UAP) have
been reported throughout history. Given the potential security and safety risks they pose, as well as
scientic curiosity, there is increasing interest in understanding what these sighting reports represent.
We approach this problem as an important one of the human experience and that can be examined
through a geographical lens: what local factors may increase or decrease the number of sighting
reports? Using a Bayesian regression method, we test hypotheses based on variables representing
sky view potential (light pollution, tree canopy, and cloud cover) and the potential for objects to be
present in the sky (aircraft and military installations). The dependent variable includes over 98,000
publicly reported UAP sightings in the conterminous United States during the 20-year period from
2001 to 2020. The model results nd credible correlations between variables that suggest people see
more “phenomena” when they have more opportunity to. This analysis is one of few investigations of
UAP sighting reports at a national scale providing context to help examine individual reports. Given
that these objects are labeled unidentiable in the personal sense, there are many natural and/or
human based explanations worth exploring.
ere has been growing interest by the United States government in Unidentied Aerial Phenomena (UAP).
Given the new focus on this potential security threat and the operational safety risks posed by these objects, the
UAP Task Force was initiated on August 4, 2020 1. is task force had a limited scope, authority, and resources
to address the issue and was temporary in its duration. e Deputy Secretary of Defense gave direction to
transition the UAP task force into the Airborne Object Identication and Management Synchronization Group
(AOIMSG) on November 23, 2021 2. Congressional legislation, however, overtook that direction and today’s
All-Domain Anomaly Resolution Oce (AARO) was established on July 20, 2022, as the single authoritative
UAP Oce with the DoD and tasked with leading and synchronizing a whole of government approach to the
issue 3. e mission of the AARO is to: “synchronize eorts across the Department of Defense, and with other
U.S. federal departments and agencies, to detect, identify and attribute objects of interest in, on or near military
installations, operating areas, training areas, special use airspace and other areas of interest, and, as necessary, to
mitigate any associated threats to safety of operations and national security. is includes anomalous, unidentied
space, airborne, submerged and transmedium objects” 3. Supporting these eorts, this research team explores
spatial patterns of publicly reported UAP sightings (analogous to UFO sighting reports in this research) from
an open-source online dataset.
In the public 2021 Director of National Intelligence (DNI) report, research on UAP sighting reports between
2004 and 2021 leaves most of its 144 government-based reports unexplained, due to limited data. Only one sight-
ing report was explained with high condence and was found to be a deating balloon 4. e follow-up 2022 DNI
report indicates the number of governmental sourced reports rose to 510, with nearly half still unexplained. e
DNI states that there is no single explanation for these UAP, with potential sources including clutter, commercial
drones, national security threats, and other unexplained phenomena. Other early incarnations of government-
based UFO research eorts (e.g., Project Sign in 1948, Project Grudge, then the most popular, Project Blue Book
led by Dr. Allen Hynek in the 1950-1960s 5, and the following Condon Report funded by the U.S. Air Force and
conducted at the University of Colorado) ended with about 5% of unidentied sightings 6. UAP research is oen
inconclusive, and our ability to explain these events seems to have become less easily resolved as our sensor
technology has advanced and our air activity has increased.
Here, we ask three foundational research questions: (1) What is the viability of publicly oered data on UAP
sighting reports? (2) Are there credible spatial patterns to these reports? and (3) If so, can these patterns be
OPEN
1Department of Geography, University of Utah, Salt Lake City, UT 84112, USA. 2United States Department of
Defense, Washington D.C. 20301, USA. *email: richard.medina@geog.utah.edu
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explained by physical and/or built environment factors? To answer these questions, we use UFO sighting report
data from the National UFO Research Center 7. We model the total count of these reports over a 20-year period
from 2001 to 2020, using environmental explanatory variables—light pollution, cloud cover, tree canopy cover,
airports, and military installations. is model is intended to represent both the available view of the sky as well
as the potential for airborne objects. We hypothesize that (a) factors limiting visibility will be negatively corre-
lated with sighting reports, and (b) factors related to air trac will be positively correlated, or simply that people
will report sightings of UAPs where they have the most opportunity to see them. To our knowledge, this is the
rst attempt to understand how spatial variation in reports is linked to environmental variables. is analysis
represents one of few attempts to examine this phenomenon at the national level and oers a starting point for
a similar approach to be applied to U.S. Government data on UAP activity to help identify possible sources.
History of UAP sighting research and Environmental Explanations
ere has been little traditional academic research on UAPs. is is expected as there are always eorts to dis-
credit scientic endeavors toward understanding this phenomenon 8, however we should not ignore the fact
that many people throughout the world report having seen unknown and unexplainable objects in the sky. What
research does exist, tends to rely on rsthand accounts, psychological explanations, or specic events, which
limits the systematic analysis of large area patterns 9–12. Furthermore, veriable data sources and questionable
accounts have limited the rigor of previous work. Data availability for larger studies has been a longstanding
issue. Recently in the U.S. there has been increased attention given to sightings by members of the military, or
other government personnel. Databases of these events are now being kept by the AARO and the supporting
Services, but these eorts only began in 2019, though they do hold information going back to 1996 13. Congress
has directed AARO to extend this research back to 1945.
An explanation for some UAP sightings is natural phenomena. For example, the planet Venus is oen mis-
taken for a UAP. At times, it is seen close to the horizon and can shine through the trees to produce an irregular
pattern of light and reection 14. e second most likely explanation is human-made aircra, 15 including vari-
ous objects, such as weather balloons, originally believed to be responsible for the Roswell, New Mexico Case
in 1947, arguably the most popular UAP case in U.S. popular culture. Follow up disclosures by the Air Force
describe the activity responsible for the event as being a classied, multi-balloon project intended to detect
Soviet nuclear tests 16. Current factors contributing to UAP sighting reports include the exponential growth
in satellite and spacecra launches and orbiters (e.g., SpaceX Starlink), as well as increased drone activity. e
use of these and other modern technologies has likely led to increased UAP reports. e 2021 U.S. Oce of the
Director of National Intelligence’s Preliminary Assessment on Unidentied Aerial Phenomena 4 and the most
recent (2022) DNI Report on UAP 13 list ve potential explanatory categories for UAP sightings—airborne clut-
ter, natural atmospheric phenomena, U.S. government or industry developmental programs, foreign adversary
systems, and other 4.
Early research attempting to explain an increase in sighting reports in Utah’s Uinta Basin uses airborne insect
infestation as a correlate. e selected insects showed patterns of “brilliant colored ares or brushes of bluish
white light from various external points on their bodies” during electric eld stimulation 17. e articially gener-
ated electric eld was suggested to resemble a weather-related phenomenon called St. Elmo’s Fire, where static
electricity causes patterns of visible colored light. Interestingly, this research was refuted soon aer publication
and described as “somewhat unrealistic,” 18 though the authors did respond with a rebuttal 19,20.
Other historical research suggests connections between seismic activity and UFO sightings. Persinger and
Derr 21 recall the tectonic strain hypothesis 22–24—“that a substantial portion of UFO phenomena are generated
by strain elds; they are evoked by the changing stresses within the earth’s crust” 25. Other research suggests that
seismic activity connected with solar activity or the use of seismic intensity may be a better predictor than just
with seismic activity alone 26.
Maybe the most popular natural explanation for UAP sightings is ball lightning, characterized by “a spherical
or roughly spherical light-emitting object whose size varies from a few cm to a meter or more, with an average
diameter of about 20cm, and whose colors vary from red to yellow, white, blue, and (rarely) green” 27. One of
the issues with the ball lightning hypothesis is that it is such a rare, and rarely recorded event, that its existence
is not accepted by all researchers. However, relatively recent research has conrmed what is believed to be a ball
lightning incident 28.
e recent increase in interest in UAP reports has been accompanied by the development of new methods
to assess and explain sightings, 29,30 including custom built observatories and sensors, as well as mobile apps
designed to leverage crowd-sourced information. While these methods bring new sophistication to the analysis
of individual events, there remains no information on the general context of sightings, i.e., why sighting reports
are more common in certain regions of the country, and less common in others. Rather than attempt to explain
what people are seeing in the sky, we explore the combination of visibility and air trac as it relates to reported
sightings, thus providing a rst order understanding of why the number of sighting reports varies spatially.
Given their relative rarity, it seems unlikely that insects, seismic activity, and/or ball lightning are responsible
for the majority of reports, especially those seen in the daytime. Understanding the environmental context of
these sightings will make it easier to propose and test new explanations for their occurrence and help to identify
any truly anomalous sightings.
Materials and methods
Public UAP sighting report data
is research uses data from the National UFO Reporting Center (NUFORC) online 31. NUFORC was formed
in 1974 and “the Center’s primary function over the past four decades has been to receive, record, and to the
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greatest degree possible, corroborate and document reports from individuals who have been witness to unusual,
possibly UFO-related events” 32. NUFORC accepts online, phone, and written reports. e data are updated
approximately once a month. Our extracted dataset includes 122,983 reported sightings in the United States
from June 1930 to June 2022. Fields in the dataset include Date, City, State, Country, Shape, Duration, Sum-
mary, Posted Date, and Image. Coordinates at the city level were calculated using Microso online services. e
resulting spatio-temporal dataset includes 121,949 points (locatable in the United States), which is 99.16% of
the total extraction. We focus on the conterminous U.S. from 2001 to 2020 for (1) ease of interpretation and (2)
because the tree canopy data (discussed below) are only available for the coastal region of Alaska. is reduces
the number of reported sightings to 98,724 (shown in Fig.1).
For analysis, we aggregate to the county level across this time period for spatial continuity. For all spatial
studies the Modiable Areal Unit Problem (MAUP) is always a consideration. While calculating and analyz-
ing sighting reports might be less biased if aggregated to equally sized cells, estimating population within such
cells requires a series of assumptions. Also, since these reporting events are relatively rare, counties provide
large enough areas for a meaningful aggregation of points. Our temporal range is selected such that entries are
assumed to be relatively recent events and not generated from memories decades ago. Internet access to report
a sighting would be more possible beginning about 2000 and is likely responsible for the increase in sighting
reports over time. Furthermore, from 2000 to 2010 especially, and in rural areas, a potential reporting bias exists
due to lessened internet access in those areas. A timeline of reported sightings for the study period is provided in
Fig.2, with a marked peak in reports between 2012 and 2014, followed by a sharp drop between 2015 and 2018.
In the spatial sciences, data like these are typically referred to as Volunteered Geographical Information (VGI).
VGI are volunteered either knowingly or unknowingly by individuals, typically with the assistance of location
enabled digital tools 33. Like with other crowdsourced data, there is little hope for assurance of quality for VGI
Figure1. NUFORC Reported Sighting Spatial Distribution for the Conterminous U.S. from 2001 to 2020.
Figure2. Timeline of NUFORC Reported Sightings from 2001 to 2020.
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34. is problem is compounded in this dataset where some may be attempting to disinform. It is clear that these
data cannot be veried, and even if interviews with each person were possible, there would be issues determining
truth and accuracy, especially for retroactive reporting. However, NUFORC does attempt to limit false reporting.
First, they provide information including descriptions, images, and video of Starlink Satellites, which can look
unidentied to those that have not seen them before. Second, they provide a description of Venus as a potential
for unidentied sighting reports. ird, NUFORC discusses hoax and joke reports, which are said to be ignored
and discarded 35. Given the size and structure of the data, it is not clear that all hoaxes can be identied, but at
least NUFORC is paying attention to hoax cases. We cannot dierentiate those sighting reports that have obvi-
ous and/or logical explanations, but we note that these still represent an ‘unidentied’ sighting report. However,
this is the only dataset of this size and detail that allows for geographic research. Furthermore, it is impossible
to discredit over 120,000 cases.
Explanatory variables
We use 3 explanatory datasets to represent physical and built environment attributes that would restrict the view
of the sky: light pollution, cloud cover, and tree canopy, and 2 datasets that represent airborne activity that might
be mistaken for UAPs. All data preparation and calculations are made using Microso Excel and ESRI ArcGIS
Pro soware. All covariates were z-score transformed prior to modeling.
Light pollution e data source for light pollution is the New World Atlas of Articial Sky Brightness 36,37.
is raster data set is available in geoti format with 30 arcsecond or 1km per pixel resolution and covers the
entire world. Values represent simulated zenith radiance in [mcd/m2]. e data for the U.S. were extracted and
the mean value for light pollution was calculated for each U.S. County.
Cloud cover Cloud cover data are sourced from the EarthEnv Project 38. ese data are compiled using
15years (2000–2014) of twice-daily remotely sensed observations from the Moderate Resolution Imaging Spec-
troradiometer (MODIS) sensor. ey are provided in geoti format at 1km resolution for the entire world. e
cloud cover values were averaged for each U.S. County.
Tree canopy e tree canopy data are from the Multi-Resolution Land Characteristics Consortium and cre-
ated by the United States Forest Service (USFS) using Landsat imagery and “other available ground and ancillary
information” 39. Tree canopy estimates cannot be calculated by spectral signature alone. Here they are generated
using random forest models that are trained on manually classied Digital Orthophoto Quarter Quadrangles
(DOQQ) as response variables 40,41. is helps estimate the dierence between tree canopy and other vegetative
land cover. e resulting data values represent 2016 canopy cover at 30m resolution and are available for the
continental U.S., coastal Alaska and Hawaii. Because of the size of the le and the resolution of other datasets
in the model, the image was upsampled to 1km resolution. e tree canopy values were then averaged for each
U.S. County.
Airports ese data are provided by ESRI’s, ArcGIS Online service accessible through the ArcGIS Pro so-
ware. ey include categories for airports, heliports, seaplane bases, ultralights, gliderports, balloonports, and
other in the U.S. ere are 19,850 entries in this dataset represented as points. e data are standardized as the
number of airports per sq. km.
Military installations Military installation data are sourced from U.S. Census TIGER/Line shapeles and
downloaded from data.gov 42. e U.S. Census created this dataset in collaboration with the U.S. Department of
Defense and the U.S. Department of Homeland Security. e data delineate the boundaries of military installa-
tions. For this research, those boundaries were overlaid onto U.S. counties, where the area of military installation
of each county is calculated.
Models
We rst explore the NUFORC dataset using the Getis-Ord (Gi*) index based on the number of sighting reports
per 10,000 people per county. is identies signicant clusters of low values (cold spots) and high values (hot
spots), by comparing the aggregate number of standardized reports in a set of neighboring counties to the full
distribution. e neighboring counties are selected as k-nearest neighbors (k-NN) with the K parameter set to
8. Rather than setting a xed distance parameter or contiguity requirements, k-NN ensures that each county
considers the same number of neighbors. e population standardization of the sighting report variable should
help correct for regions with larger counties, such as the West, which generally cover larger areas 43,44.
To model potential for seeing UAPs we use Bayesian small area estimation, based on the relative rate of
sighting reports in the population of a location. Small area models incorporate a spatial autoregressive term to
limit the inuence of extreme values, which can result from small population sizes. Here, the count of reported
sightings
yi
for county i is assumed to follow a Poisson distribution:
where
Ei
is the expected number of reports for county i and
θi
is the relative rate. To get the expected value, rst
we estimate the per capita rate of reports for the conterminous U.S. as the total number of reports divided by the
total population. e expected value for any county is obtained by multiplying this value by the population of
that county. Where
θi>1
, the number of reports is greater than would be expected based on population alone. A
recent analysis of the NUFORC dataset suggests that the number of reports may also be linked to county area 45.
However, given that the distribution of the population in a given area may be highly variable, it is unclear how to
use this in the expected rate calculation. We therefore assume that the expected rate of reports is based simply on
the capacity of a county to produce reports. Finally, the relative rates are modeled using K covariates as follows:
yi
∼
Pois(θiEi)
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where
K
k=1
βkX
ik
represents the set of z-score transformed covariates representing visibility and air trac described
above with associated coecients. Finally, the model error (
∈
) is decomposed into a spatial autoregressive eect
and non-spatial random noise. Our model assumes that the individual reports are independent. While this is
unlikely to be true of the events that caused the sightings, as these may be reported by multiple individuals, we
assume that the reports are independently provided.
Model parameters and coecients are estimated using Integrated Nested Laplacian Approximation (INLA)
46. INLA was chosen over MCMC approaches due to its computational eciency with large spatially structured
models. Model results are reported as the mean of the posterior probability distribution for each coecient
(Table1). Variance Ination Factors (VIFs), which signal potential multicollinearity within a model, for all vari-
ables in the model are well under 2. VIF values are traditionally accepted if they are under 5. Bayesian posterior
estimates can be used to test specic hypotheses 47. Here, we test the hypotheses that the relationship between
each covariate and the rate of sighting reports is positive (i.e., > 1) or negative (< 1). Support for a given hypothesis
is based on the posterior probability distribution of model coecients and is described as the credibility of that
hypothesis. For example, if 95% of the posterior distribution of a coecient is above one, this indicates a positive
relationship between that covariate and the rate of sighting reports and would be assigned a credibility of 95% of
a positive relationship. If the posterior distribution is equally split into negative and positive estimates, this would
be assigned a credibility of approximately 50% for either hypothesis. As the model is based on log-transformed
relative rates, the posterior estimates of coecients have been exponentiated to help in interpretation. Coecients
are reported as the mean of the posterior distribution plus the 95% credibility interval (Table1). A map of the
spatial error term (u) is included in supplementary information.
Results
e results from a hotspot analysis (Fig.3) show a strong trend with many more population standardized sight-
ings (i.e., county reports per 10,000 people) reported in the Western U.S. and in the very Northeast, along with
some isolated areas including the tri-state border region of Illinois, Indiana, and Kentucky, surrounding Evans-
ville, Indiana, and the area surrounding Washington D.C. Clusters of low sighting reports are found through
the central plains and in the southeast.
Table1 provides the results of the model, based on the posterior probability distribution of each coecient.
With the exception of the intercept, all model coecients describe the rate of change of the relative rate of sighting
reports for a one standard deviation increase in that coecient. Values above 1 indicate a positive relationship
(i.e., increasing reports); values below 1 indicate a negative relationship (decreasing reports). For example, the
coecient for Mean Light Pollution is 0.923, indicating that a one standard deviation increase in light pollution
will result in a 7.7% decrease in sighting reports.
All results except for cloud cover support the general hypothesis that people will see things when they have
the opportunity to. Cloud cover has a non-credible relationship with sighting reports, with no support of either
a negative or positive relationship.
Discussion and conclusions
We recall here our initial research questions: (1) What is the viability of publicly oered data on reported UAP
sightings? (2) Are there credible spatial patterns to these sighting reports? and (3) If so, can these patterns be
explained by physical and/or built environment factors? For question 1, the publicly available data from NUFORC
online are useable data; however, they require substantial processing for spatial analysis. ese data could be
used for ner resolution (city level) research, rather than county level used here.
e main concern of these ndings is, are these volunteered data valid? e short answer is that it is likely
that some are and some aren’t. However, we suggest that if the data were entirely invalid (assuming homogeneous
log
θi=
K
k=1
βkxik
+∈
i
Table 1. Results from Bayesian small area model. From le to right: variable name; mean posterior
distribution (95% credible range); credibility of positive relationship with sighting reports; credibility of
negative relationship with sighting reports; brief description of result.
Variables Exponentiated Results Positive coecient credibility (%) Negative coecient credibility (%) Relationship
(Intercept) 0.862 (0.848, 0.877)
Mean Canopy 0.961 (0.915, 1.01) 6 94 More Canopy = Fewer
Reports
Mean Cloud Cover 0.998 (0.929, 1.072) 48 52 No Credible Relationship
Mean Light Pollution 0.923 (0.899, 0.947) 0 100 More Light = Fewer
Reports
Percent Military Area 1.013 (0.994, 1.033) 92 8 More Military = More
Reports
Air Trac/Sq. Km 1.099 (1.068, 1.131) 100 0 More Air Trac = More
Reports
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psychological and sociological distribution of submissions), the sighting reports would exhibit little to no spatial
pattern and are unlikely to follow a pattern that can be explained by rst-order visibility indicators. Another data
question is, are there any temporal and/or geographic errors? Likely, because some entries into this dataset are
reported retrospectively, not always in the rst person. We attempt to limit this by using data from 2001-pre-
sent, but that does not completely resolve the issue. Geographic errors were limited by upscaling the data to the
county level. A nal issue we consider is that these reported cases require knowledge of NUFORC and access
to communications. e authors found the website and organization while searching for data. Some may nd
the website while searching for an organization to report to. Still, there is likely bias in who has knowledge of
this resource since it is not widely advertised. In all, we posit that this dataset has value in understanding these
sighting reports; that either this indicates people are seeing things they can’t explain (or that they don’t want to
explain with more logical explanations), or this indicates where people are thinking more about UAPs. Both are
important and have physical/social implications.
For questions 2 and 3, there are credibly identiable patterns to these sighting reports, and these patterns
relate to environmental characteristics. e explanatory variables are intended to represent both (1) the oppor-
tunity to see something and (2) the potential for something human constructed to be in the eld of view. We
have not considered satellites or drones, which are likely important factors, nor the fact that airplanes (and
helicopters, etc.) do not only y around their takeo and landing locations. However, around the locations we
use, aircra are likely to be closer to the ground, more visible, and more frequently present. Using the military
installation data, we hope to capture, not only aircra, but also nighttime training activities that might use, for
example, tracer rounds, drones, and other forms of illumination in relatively desolate areas.
If we assume that most sighting reports here are representative of true sightings that people determined to be
unidentied, then our results have interesting implications. Our model shows that the majority of standardized
sighting reports are in the western parts of the U.S. and in the very northeast. We hypothesize that the higher
rate of western sightings could be due to (1) the physical geography of the West (i.e., the lack of vegetative
canopies and wide-open spaces), (2) cultures of outdoor activity (e.g., recreation and other activities enjoyed
in more temperate weather throughout the year), and (3) cultures of paranormal ideation (e.g., impacts of Area
51, Roswell, New Mexico). ere are also some isolated counties throughout the rest of the country that warrant
further investigation to identify what properties may generate relatively more UAP attention. In these results,
however, cloud cover is not credible, possibly related to higher rates of sighting reports in both the coastal regions
of the Pacic Northwest (relatively clouded) and desert regions of the Mountain West (relatively clear). We
initially expected cloud cover to be credibly related to reports, as clouds can cause light to scatter and by doing
so, obscure reective or illuminated things that are moving within or above them and create patterns that some
might consider unexplained. However, that was not the case. All other variable relationships are as expected
and align with our initial hypotheses, that people report more sightings where they have a better view of the sky.
e question now is why? is research begins to answer this question by considering how much human made
airborne activity is occurring. e highly credible relationships with air trac and with military activity sug-
gest that people are seeing, but not recognizing, things that are human made. As an example, a hot air balloon
seen from a far enough distance can look unexplainable, especially if it is seen by someone who has not seen
one before. Drones, which we did not test specically for, can seem to y erratically in areas where people aren’t
used to seeing things moving in the sky. It is unlikely that events, such as ball lightning, seismic based lights,
insects, or other natural occurrences are responsible for more than a small portion of these reports, as they are
rare events themselves.
While these results provide an initial assessment of factors linked to the reported sightings of unidentied
or unexplained phenomenon, they also generate further questions. We nd credible relationships and spatial
Figure3. Hotspot Analysis (Getis-Ord Gi*) of Reported Sightings from 2001 to 2020.
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patterns that require further investigation. Why, for example, are the rates of sighting reports low in California,
when they are high in many of the surrounding states? Why do the rates of reports uctuate across time? Our
future research will include temporal considerations (e.g., variation over time) to hopefully address some of these
questions. We further note that our covariates represent average conditions, and while these clearly explain much
of the rst-order pattern in sighting reports, additional factors may be identied by exploring the remaining
pattern in the spatial errors (SI Fig.1) or by considering changes over time or individual events.
Some patterns in the reported sightings might be explained by sociocultural factors. For example, are there
spikes of reports aer Hollywood attention is given to movies or TV shows on aliens? Are some cultures more
likely to see UAPs, because of their belief systems? Have some U.S. regions/places been given more attention to
historical UAP sighting reports? ere is no question that geography and “place” inuence people’s belief systems
and behavior. In some places, the expectation of what you are supposed to see may inuence what you actually
see. In a process termed motivated perception, people may bias their perceptions to arrive at expected conclu-
sions that meet their goals or oer rewards 48,49. If your goal is to see a UAP, you may very well see one given the
opportunity. However, it is important to point out that there are many sighting experiences which people are
reluctant to report. ere are many who fear stigmatization and attacks from the public, and others who previ-
ously had no belief in UAPs, but had an experience that convinced them of the opposite.
We approach this problem with caution, because of both the complexity of the topic and the sensitivity of
available data. e U.S. Government position is that “UAP clearly pose a safety of ight issue and may pose a
challenge to U.S. national security” 4. For national security issues, uncertainties and unknowns are never good,
and it is the job of intelligence eorts to minimize the unknowns. Regardless of what people are seeing, and
whether they are military pilots, civilian pilots, or general bystanders, there is a potential threat. at threat grows
as our uncertainties grow. Although based on a noisy, crowd sourced dataset, our results can provide a context
for how sighting reports of unidentied objects vary in space, the factors linked to these, and may oer a step
towards understanding these threats.
is problem is relevant on many fronts, including anthropological and sociological (i.e., understanding the
human/social experience). e stigma given to this area of research, if it is explored scientically, should be over.
We make no hypotheses about what people are seeing, only that they will see more when and where they have
opportunity to. e question remains, however, as to what these sighting reports are of. Further examination of
regions where the model performs poorly, temporal trends, and reported details of each reported sighting may
help further elucidate this.
Data availability
e data that support the ndings of this study are available online from the National UFO Reporting Center
(NUFORC) at https:// nuforc. org/; however, these data are not geocoded. Geocoded data are available from the
authors upon reasonable request.
Received: 25 July 2023; Accepted: 8 December 2023
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Acknowledgements
We would like to acknowledge the work of the National UFO Reporting Center (NUFORC) in collating and
making available the reports of UAP sightings. SB would like to thank Nick Malins for discussions that helped
dene both the study questions and analysis.
Author contributions
R.M., S.B., and S.K. are responsible for Conceptualization S.B. and R.M. designed the Methodology R.M., S.B.,
and S.K. are responsible for Investigation: RMM, SCB, SMK R.M., S.B. are responsible for Visualization S.K. is
responsible for Funding acquisition R.M. and S.B. are responsible for Project Administration S.K. is responsible
for Supervision R.M., S.B., and S.K. are responsible for Writing – original dra R.M., S.B., and S.K. are responsible
for Writing – review & editing.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 023- 49527-x.
Correspondence and requests for materials should be addressed to R.M.M.
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