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RESEARCH ARTICLE
Quantifying tourism booms and the
increasing footprint in the Arctic with social
media data
Claire A. RungeID
1
*, Remi M. Daigle
2
, Vera H. Hausner
1
1Arctic Sustainability Lab, Department of Arctic and Marine Biology, UiT The Arctic University of Norway,
Tromsø, Norway, 2De
´partement de biologie, ’Universite
´Laval, Que
´bec, Canada
*claire.runge@uqconnect.edu.au,twitter@Claire_Runge
Abstract
Arctic tourism has rapidly increased in the past two decades. We used social media data to
examine localized tourism booms and quantify the spatial expansion of the Arctic tourism
footprint. We extracted geotagged locations from over 800,000 photos on Flickr and
mapped these across space and time. We critically examine the use of social media as a
data source in data-poor regions, and find that while social media data is not suitable as an
early warning system of tourism growth in less visited parts of the world, it can be used to
map changes at large spatial scales. Our results show that the footprint of summer tourism
quadrupled and winter tourism increased by over 600% between 2006 and 2016, although
large areas of the Arctic remain untouched by tourism. This rapid increase in the tourism
footprint raises concerns about the impacts and sustainability of tourism on Arctic ecosys-
tems and communities. This boom is set to continue, as new parts of the Arctic are being
opened to tourism by melting sea ice, new airports and continued promotion of the Arctic as
a ‘last chance to see’ destination. Arctic societies face complex decisions about whether this
ongoing growth is socially and environmentally sustainable.
Introduction
The number of tourists visiting the Arctic has increased dramatically over the past two decades
[1,2], reflecting a rise in tourism globally over the past 50 years [3]. While this could bring
alternative livelihoods to Arctic communities, concerns over the social and environmental sus-
tainability of the rate and scale of the tourism boom are growing across the Arctic [4–6]. Tour-
ism can have both positive and negative impacts on the natural environment and on host
communities. Direct effects of tourism (e.g. transporting, accommodating, and feeding tour-
ists) and the indirect socioeconomic change brought about by the tourism industry (e.g. influx
of seasonal workers) drive increased habitat loss [7], resource use, and carbon emissions [8]
across the world, in addition to the localized consequences of nature-based tourism and recre-
ation activities on the natural environment, such as injury, death, or disturbance of wildlife or
damage to vegetation [9]. Understanding, at a local scale, how and where tourism booms are
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 1 / 14
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OPEN ACCESS
Citation: Runge CA, Daigle RM, Hausner VH
(2020) Quantifying tourism booms and the
increasing footprint in the Arctic with social media
data. PLoS ONE 15(1): e0227189. https://doi.org/
10.1371/journal.pone.0227189
Editor: Wenwu Tang, University of North Carolina
at Charlotte, UNITED STATES
Received: June 14, 2019
Accepted: December 9, 2019
Published: January 16, 2020
Peer Review History: PLOS recognizes the
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https://doi.org/10.1371/journal.pone.0227189
Copyright: ©2020 Runge et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data underlying
the results presented in the study are available
from Flickr (www.flickr.com). To aid decision-
making we have made the code, rasters and maps
of tourism intensity across the Arctic and tables of
distributed across landscapes is crucial for conserving Arctic environments and for managing
impacts on host communities.
A major challenge for planning and managing sustainable tourism growth in the Arctic lies
in the difficulties of mapping where tourists go and how they use landscapes and ecosystems.
Data on spatial visitation and trends is sparse in the Arctic. While statistics on hotel stays and
transport use are now commonly collected by government and tourism management organi-
zations, data on where tourists go during the day and what they do there is rarely collected.
Social media provides a useful source of information on tourist visitation patterns to better
pinpoint the needs of tourists and target actions to manage tourism impacts [10]. Passively
crowdsourced and high resolution information from social media (volunteered geographic
information; VGI) can be used to rapidly generate maps of the multiple destinations visited by
tourists across large areas and over time [11]. Social media data has been well demonstrated to
be useful for mapping and monitoring at a range of spatial scales across the world. Such data
has been used to map the distribution of tourists in protected areas [12–14], and within cities
[11] and can be used to inform a variety of aspects of tourism and landscape management,
including to identify peaks of visitation to attractions [15,16], map environmental impacts [17]
and to estimate the landscape values, nature-based experiences and ecosystem services appre-
ciated by tourists [18–20]. Most of these studies use the social media platform, Flickr, which
has been shown to correlate well with visitor statistics at different scales [12,15,16,21].
Similar to many nature-based tourism destinations in developing countries, parts of the
Arctic in Scandinavia, Iceland, Faroe Islands and Alaska have experienced unprecedented
growth in the number of tourists in recent years, and Greenland, Canada and the Russian Arc-
tic are likely to be the next tourism frontiers [22,23]. Rapid and localized booms in tourism
can overwhelm local capacity (and desire) to host visitors, particularly in small, remote com-
munities such as those found in the Arctic and many parts of the developing world [23–26].
The ability to rapidly identify sites in the early stages of a boom would support better adapta-
tion and planning to pre-emptively address many of the sustainability challenges brought
about by rapid increases in nature-based tourism. It would allow local communities and
national tourism organizations to proactively direct resources to sites where special manage-
ment such as provisioning of restrooms and waste disposal, better signage, safety and disaster
management, parking, and trail maintenance may be required. Social media data has been
demonstrated as an early-warning system for rapidly detecting booms and busts in such
diverse applications as disaster management [27,28] and disease control [29]. Methods for
such ‘event detection’ are rapidly evolving [30,31]. These methods rely on large amounts of
high temporal resolution data (‘big data’). The suitability and limitations of social media data
for detecting events in data-sparse regions has yet to be tested. We investigate whether Flickr
data can be used as an early warning system to detect localized booms in tourism in the Arctic
and similar data-deficient regions.
Tourism growth can influence the spatial use of landscapes in various ways. Here, we dem-
onstrate how the tourist footprint on Arctic landscapes has changed over the past 14 years at a
pan-Arctic scale, and examine the management implications of those changes. We test two
hypotheses drawn from theories of tourism and economic geography [32,33] 1) that tourists
visit the same sites through time (overall footprint is unchanged but magnitude of use at each
site has increased) 2) that tourists have spread throughout the landscape (overall footprint
increased but magnitude of use at any one site is constant). These two patterns of tourism
growth have very different implications for both the social and environmental sustainability of
tourism and the satisfaction of the tourist experience. For instance, if tourists avoid busy areas
and self-segregate across landscapes [34], environmental and social impacts will be more wide-
spread but lower intensity. Alternately, the negative impacts of tourism can be localized by
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 2 / 14
the footprint across time publicly and freely
available for download at doi:10.18710/QEOFPY.
Funding: The work was funded by grants awarded
to VHH from FRAM - High North Research Centre
for Climate and the Environment through the
Flagship MIKON (Project RConnected; https://
www.framcentre.com/) and the Arctic Belmont
Forum Arctic Observing and Research for
Sustainability (Project CONNECT; https://www.
belmontforum.org/). The Norwegian collaboration
was financed by Norwegian Research Council
grant 247474 (https://www.forskningsradet.no/en).
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
channeling tourists into high use sites and away from sensitive communities and ecosystems.
We examine seasonal differences in the spatial patterns of Arctic visitation, and explore how
infrastructure such as roads, airports and ports influences these patterns, with a view to
informing how upcoming infrastructure development could contribute to tourist growth.
Materials and methods
Extraction of data from Flickr
We first extracted geotagged and publicly shared photo metadata for over 2 million photos
from Flickr (www.flickr.com) for the region north of latitude 60˚N. Photo metadata included
location and date that each photo was taken, user id (key coded by Flickr), image URL, Flickr-
and user- generated image tags, and user-generated image title. Data was extracted from the
Flickr API (https://www.flickr.com/services/api/) on 4 December 2017. Due to an issue with
the data download we re-extracted photos for Iceland (bounded by -27˚ to -12˚ longitude and
62˚ to 68˚ latitude) on 11 January 2018. Hourly metrics of the number of photos uploaded to
Flickr globally between to January 1 2004 and December 31 2017 were obtained from the
Flickr API on 07 May 2018. We used the R package ‘flickRgeo’ [35] which provides an R wrap-
per for the Flickr API.
For the purposes of this study we define our study region, ‘Arctic’, as the region bounded
by the Arctic Council AMAP boundaries [36] and confined to areas north of latitude 60˚N.
We constrain the study region using an environmental rather than political definition as the
study is focused primarily on impacts on Arctic landscapes. We excluded photos from the
extracted dataset that were taken outside this study region. We also excluded photos that were
missing urls or geotag coordinates, had null coordinates (0,0), and photos taken prior to Janu-
ary 1 2004, or after December 31 2017. We excluded photos by users with only 1 or 2 photos
within the study region as they are likely to represent people who are just trialing Flickr by
uploading a random photo rather than a photo representing a genuine tourist. These ‘test
users’ account for approximately 36% of users in the Flickr dataset but just 0.95% of photos.
Further details on the choice of 2 photos as a threshold for exclusion are included in S1 Appen-
dix. The final dataset contained a total of 805,684 geotagged photos with metadata from 13,596
unique users.
Mapping the seasonal distribution of Arctic tourism
To map the relative intensity of visitation across the Arctic in summer and winter, we first cre-
ated square spatial grids (rasters) at 10km and 100km resolutions. We then calculated the
photo-unit-days in each grid cell for summer and for winter aggregating data for each season
across all years (2004–2017). We defined the months of May to October as ‘summer’ and
November through April (of the following year) as ‘winter’ (e.g. “winter 2016” includes the
months November and December in 2016 and January through April in 2017). Photo-unit-
days (PUD) is an established metric of tourism visitation [15] that accounts for the biases in
social media data introduced by differences in the number of photographs uploaded by differ-
ent users. For example, three PUD can represent either a site visited on three separate days
throughout the year by a single person, or by three separate people on a single day. This is the
conventional approach for working with this type of social media data because it corresponds
to empirical user data collected by tourism sites that are often based on daily user access fees.
For example, if three users access a park with daily user access fees on the same day, or a single
user accesses that park on 3 separate days, both visitation scenarios appear identical in terms
of empirical visitation rates (i.e. fees collected) as well as PUD.
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 3 / 14
Pan-Arctic trends in the footprint of Arctic tourism over time
To quantify the ‘footprint’ of Arctic tourism (the percentage of the Arctic visited by tourists),
we created a hexagonal spatial grid covering the entire Arctic with a 5km diameter resolution.
We chose this resolution after examining the trade-off between commission errors and com-
puting efficiency (S2 Appendix). We allocated each cell a value of 1 (visited) if any user had
taken a photograph in that cell in a given year (2004–2017) and season (summer, winter), and
0 (unvisited) if not. The number of people using Flickr globally changed over time as the popu-
larity of the platform waxed and waned. Relying on raw metrics of the number of Arctic Flickr
users, photographs or photo-unit-days will thus result in biased estimates of tourism trends
across time. The global and Arctic trends in Flickr use across time and the number of photos
sampled in each year can be found in S3 Appendix. We calculated the footprint in three ways.
Firstly, the Uncorrected Arctic footprint uses all available Flickr records (566205 summer;
228667 winter) and shows the full extent of Flickr users’ footprint, but includes the bias from
the global change in Flickr usage between 2004 and 2017. The Global-bias corrected subsample
removes the global pattern of Flickr usage to represent a less source-biased view of the rise in
the footprint of tourism in the Arctic. This was done by selecting a random sample of Arctic
photos based on the change in number of photos submitted to Flickr globally using 2004 values
as a baseline. For example, the year with the lowest global usage (2004), we kept all the photo
records. If the global Flickr usage doubled in a particular year relative to 2004, we sampled half
of the available records for that particular year. The number of records sampled for each
month can be found in Table S3 Appendix (total 36546 summer records, 12262 winter).
Finally, the Equal sample size also removes the global bias and provides a measure of the
change in the relative footprint per-tourist across time. This was done by randomly selecting
an equal number of photographs for each year from which to calculate the footprint (total
15652 records summer, 4018 records winter). Numbers for 2017 should be treated with cau-
tion and are likely underestimates as we extracted data from the Flickr API on 4 December
2017 and the average lag time between photos being taken and uploaded is 2 weeks, but can be
longer [13].
Modelling the influence of accessibility on seasonal patterns of Arctic
landscape use
In order identify the effect of accessibility on the presence of tourists in different seasons, we
first divided the study region into a hexagonal spatial grid with a 10km diameter resolution.
We chose to use a lower resolution than that used in the footprint analysis above as the large
number of cells at 5km made the models computationally intractable. We then calculated foot-
print in each cell for summer and for winter by allocating these cells a value of 1 if any photo-
graph had been taken in that cell in that season (at any time from 2004 and 2017), and 0 if not,
similar to the Uncorrected Arctic footprint described above. We removed cells that fell within
Russia due to sparse coverage of this region by Flickr data, and from marine areas. We then
modelled the footprint against variables describing the accessibility of each cell: log of distance
to airports, log distance to ports, log distance to populated areas, log distance to road, the
square root of the length of road within a grid cell, whether the cell overlapped any protected
area, and country as a fixed effect. We chose these accessibility variables after examining a
wider set of candidate variables for correlation. The models took the form of a binomial gener-
alized additive model with logit link of footprint as a response variable. In order to account for
spatial autocorrelation we included a fitted thin-plate spline on the variables latitude and longi-
tude of each cell centroid. Smooths, intercept, slope and confidence intervals were estimated
by a restricted maximum likelihood (REML) estimator and methods for large datasets (‘bam’
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 4 / 14
function in R’s mgcv package). The model formula is:
ln Pfootprint
1Pfootprint
!
¼b0þf1ðlongÞ þ f2ðlatÞ þ b1log10 ðairportdistÞ þ b2log10 ðportdistÞ
þb3log10ðpopulateddistÞ þ b4log10 ðroaddist Þ þ b5ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
roadlength
qþb6PA
þg7Country þε
where f
1
,f
2
are fitted thin-plate spline smooth functions on the variables latitude and longitude
of each cell centroid, airport
dist
is the distance to airports, port
dist
is the distance to ports, popu-
lated
dist
is the distance to populated areas, road
dist
is the distance to road, road
length
is the length
of road within a grid cell, and PA is whether the cell overlapped any protected area (0 if false, 1
if true). Random effects are represented by β’s and the fixed effect is represented by .
The location of airports, ports and populated areas, and country boundaries were extracted
from Natural Earth (www.naturalearthdata.com) using the R package ‘rnaturalearth’ [37].
Roads were extracted from Global Roads Inventory Project [38]. Protected area boundaries
were drawn from CAFF [39] and supplemented with data from Protected Planet [40]. We ran
separate models for summer and winter as the presence of snow and ice limits access to rural
areas in winter. The summer model had 90,750 cells with no photos, and 6,554 with photos.
The winter model had 93,990 cells with no photos, and 3,314 with photos.
Local trends (booms and busts) in Arctic tourism
We investigated the suitability of Flickr data to be used to identify local booms and busts in
tourism in the Arctic, a data deficient region. We first divided the landscape into a square grid
cells and calculated the photo-unit-days in each cell for each year. We then performed linear
regression modelling to identify trends in PUD in each cell between 2012 and 2017 and test
their statistical significance. We ran models for cell diameters ranging from 500m to 100km to
examine the effect of data availability on trend detection. We modelled only cells that were vis-
ited in at least two of the six years between 2012 and 2017. This timeframe was chosen as global
Flickr usage remained reasonably constant during this period (Fig A in S3 Appendix).
Unless otherwise stated, all analysis was conducted in R version 3.4.2 [41] using the ‘tidy-
verse’ [42], ‘sf’ [43], and ‘mgcv’ [44] packages. All spatial data was projected to North Pole Azi-
muthal Lambert equal area (EPSG:102017) for analysis. Code associated with the project is
available at doi:10.18710/QEOFPY.
Results
Increase in Arctic summer and winter tourism footprint
We find that the overall footprint of tourism and area used per tourist has increased since 2004
(Fig 1). In the 10 years between 2006 and 2016, the uncorrected footprint increased from
0.066% to 0.385% of the Arctic in summer and 0.015% to 0.173% in winter. After correcting
these figures to account for the increased proportion of tourists captured in the analysis over
that time (i.e. accounting for the rise in Flickr use globally), the footprint increased by 374% in
summer (0.029% of Arctic in 2006, 0.109% in 2016) and 634% in winter (0.006% of Arctic in
2006, 0.036% in 2016). The relative-footprint-per-tourist also increased over that period (sum-
mer: 0.028% of Arctic in 2006 to 0.043% in 2016; winter: 0.007% in 2006 to 0.012% in 2016).
We caution that this metric is not the absolute per-tourist footprint, rather it should be inter-
preted as a relative indication of how the footprint of a fixed (and unquantified) number of
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 5 / 14
tourists has changed across time. These estimates are robust to uncertainty introduced by ran-
dom sampling in the Global-bias corrected and Equal sample size methods (S4 Appendix).
Growth in Arctic visitation over time
The total number of photos on Flickr increased between 2004 and 2017, both globally and
in the Arctic. Global uploads of photos to Flickr steadily increased between 2004 and 2008,
slowed between 2008 and 2012 before a slight upsurge in 2012, plateaued between 2013 and
2015 and has declined slightly since then (Fig A in S3 Appendix). The Arctic represents an
increasing share of Flickr’s yearly photo traffic (Table in S3 Appendix). The number of pho-
tos uploaded in the Arctic shows an exponential growth between 2004 and 2013, and has
remained roughly steady since then (Fig B in S3 Appendix), with this trend overlaid on a
seasonal trend in visitation (Fig C in S3 Appendix). Across the Arctic, July and August were
the most popular months to photograph the Arctic (Fig D in S3 Appendix). Nonetheless, a
large number of users visited during the Arctic winter (Table 1; 29.8% of all photos; 40,272
photo-unit-days, 53.3% of summer PUD). At the extreme, visitation in Greenland is con-
centrated in the summer months (91% of photos). In contrast to the rest of the Arctic, visita-
tion in northern Finland peaks in winter (61.3% of photos). Winter and Christmas are a key
part of the branding of tourism to northern Finland, and places such as ‘Santa Claus’s
Fig 1. Footprint of Arctic tourism. The total footprint of Arctic tourism measured from Flickr data increased between 2004 and 2017 (Uncorrected Arctic footprint,
darker blue), even after adjusting for the global rise in Flickr use during this period (Global-bias corrected, green). The relative footprint per tourist (Equal sample size,
pale blue) also increased slightly over this time. Similar trends are seen in summer and winter, though the tourism footprint in winter is approximately half the
magnitude of that in summer. The footprint is defined as the percentage of 5km hexagonal grid cells within the Arctic region visited by at least one Flickr user per year.
The 2017 decline should be interpreted with caution as it may in part be an artefact of the timing of our data download and the lag between photos being taken and their
being uploaded to Flickr.
https://doi.org/10.1371/journal.pone.0227189.g001
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 6 / 14
village’ in Rovaniemi, Lappland, attract the majority of the visitors to the region (Fig 2).
These metrics of Arctic visitation derived from Flickr show good agreement with official
metrics of tourism visitation (S1 Table), consistent with previous research on social media
data [12,15,21].
Table 1. Seasonal variation in Arctic visitation, estimated from Flickr data (2004 to 2017).
Overall number of
photos (% of all photos)
Summer (% of all
photos for that region)
Winter (% of all
photos for that
region)
Number of photo-
unit-days (PUD)
Summer (% of total
PUD for that region)
Winter (% of total
PUD for that region)
Whole Arctic 805,684 70.2 29.8 115, 775 65.2 34.8
Iceland 377,817 (46.9) 70.0 30.0 51,500 66.4 33.6
Alaskan Arctic 150,325 (18.7) 76.0 24.0 21,903 67.0 33.0
Norwegian
Arctic
122,387 (15.2) 68.2 31.8 20,779 63.7 36.3
Canadian
Arctic
43,466 (5.4) 68.4 31.6 7,713 66.8 33.2
Finnish Arctic 22,164 (2.8) 38.7 61.3 3,716 43.2 56.8
Faroe Islands 20,259 (2.5) 78.1 21.9 2,226 68.0 32.0
Greenland 19,944 (2.5) 91.0 9.0 1,952 78.8 21.2
Swedish Arctic 18,303 (2.3) 53.5 46.5 2,809 56.9 43.1
Svalbard & Jan
Mayen
16,282 (2.0) 71.3 28.7 1,321 72.6 27.4
Russian Arctic 13,321 (1.7) 64.2 35.8 2,228 60.7 39.3
https://doi.org/10.1371/journal.pone.0227189.t001
Fig 2. Seasonal maps of Arctic tourism (2004–2017) displayed at 10km resolution. The guide maps (right) are displayed at 100km resolution. Spatial patterns of
tourism are strongly governed by air, road and sea access, with few tourists venturing far from populated areas in winter. A photo-unit-day value of 14 corresponds to
one Flickr user visiting the cell per year. Country borders are modified from Natural Earth CC PD.
https://doi.org/10.1371/journal.pone.0227189.g002
Quantifying the Arctic tourism footprint with social media data
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Flickr data not suitable as early-warning system
We investigated the potential for Flickr data to be used to identify local trends in tourism over
time. The Arctic is a data-poor region and we found that only a small number of places were
photographed more than once per year (Fig in S2 Appendix). Only 22 10km grid cells were vis-
ited by Flickr users more than 50 times (i.e. once a week) in 2017. Although annual tourism
growth is documented as ranging from 5–20% across the Arctic, due to these data limitations
regression models of trends in visitation between 2012 and 2017 were able to detect significant
trends at just a handful of sites (S1 Fig). Most sites lacked sufficient data to detect trends even
when aggregated to 10km grid cells, a relatively large scale compared to the scale required for
management decisions.
Spatial patterns of Arctic visitation change across the seasons
The spatial pattern of tourists’ landscape use differed between summer and winter. Flickr users
ventured further north and into marine areas to a greater extent during the summer months
(Fig 2). Visitation was influenced by access and often, though not always, concentrated in rec-
ognized tourism hotspots (Fig 2). The main hotspots of tourism fall along coastal roads in Ice-
land, in the fjords and islands of northern Norway, and in protected areas and along roads in
North America (Fig 2). We note that although the size of the Alaskan tourism market eclipses
that of the rest of the Arctic, including Iceland, few cruises travel further north than Anchorage
(61.2˚N), and the majority of this tourism thus falls outside our study region which is bounded
to the south at 60˚N.
Accessibility drives Arctic visitation
We found that accessibility has a significant effect on the distribution of Flickr users
throughout the Arctic, with the presence of tourists decreasing rapidly as distance from
transport infrastructure and populated areas increases, and increasing with the length of
road in a given cell (Table 2). The summer accessibility model explained 47.3% of the devi-
ance in the presence of tourists (adjusted R
2
0.448, AIC 25347). The winter accessibility
model explained 51.4% of the deviance in the presence of tourists (adjusted R
2
= 0.436, AIC
of 14117). The variable square root of length of road in cell had the largest explanatory
power of the accessibility variables (model without this variable had adjusted R
2
0.409 sum-
mer, 0.393 winter, deviance explained 44.7% summer, 48.8% winter). The protected area
term of the winter model was significant (p = 0.000670) in the full model, but not significant
in more parsimonious models. Removal of this term only slightly decreased the deviance
explained (ΔAIC = 9, Δdf = -1.0239, Δdeviance = -10.706, Pr(>Chi) = 0.001117), indicating
that tourists were no more or less likely to visit protected areas than non-protected areas in
winter. All other terms were significant in both models. We removed the variable that had
the least explanatory power, log distance to port, from the summer model, and the winter
model without the protected area term. This reduced the model fit of the summer model
(ΔAIC = 145, Δdf = -1.0881, Δdeviance = -146.57, Pr(>Chi) = 2.066x10
-16
) but had minimal
effect on the winter model (ΔAIC = 25, Δdf = -1.1028, Δdeviance = -27.406), though the
large number of data points in the model meant that this variable was significant at 95%
confidence (2.02x10
-7
). Removal of all other terms substantially decreased the deviance
explained by the models. Visual examination of model residuals did not reveal any residual
spatial autocorrelation in any of the models. Plots of model fit and partial plots of model
variables are included in S5 Appendix.
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 8 / 14
Discussion
The footprint of tourism on the Arctic environment has almost quadrupled over the past
decade, from 0.03% of the Arctic in summer 2006 to 0.11% of the Arctic in summer 2016 (Fig
1), and the winter footprint has increased by over 600%. Despite this dramatic expansion,
large areas of the Arctic still remain free from tourists (Fig 2). Arctic tourists tend to congre-
gate in a handful of highly visited sites (Fig 2) with a long tail of places that are visited only
occasionally, following the power law seen in other parts of the world [45]. The recent tourism
boom across the Arctic has led to widespread concerns over the effects of tourism on Arctic
ecosystems and communities, and the sustainability of Arctic tourism [4,5]. Arctic tourism is
often marketed around ideas of pristine, untouched nature, and the overall growth in tourist
numbers and the concurrent increase in infrastructure to support that growth presents chal-
lenges for maintaining environmental and social sustainability. There are indications that
many popular sites are reaching capacity, and that tourists are beginning to experience disap-
pointment and frustration around the large number of visitors present [46]. The footprint per
tourist has also increased (Fig 1), indicating that tourists are now visiting a wider variety of
places. This may be either due to self-segregation by tourists seeking an undisturbed experi-
ence [46], or the marketing of a wider variety of tourist attractions and nature-based activities
as Arctic tourism matured over the past decade [47].
These patterns of visitation present both advantages for the management of tourists and
challenges for the sharing of the economic benefits of the recent and ongoing Arctic tourism
Table 2. Model coefficients for binomial generalized additive models of the effect of accessibility on the tourist footprint in summer and winter in the Arctic. The
intercept represents Norway, unprotected. The protected area term was not included in the winter model.
Summer Winter
Variable Estimate of coefficient (±standard
error)
z-value Pr(>|z|) Estimate of coefficient (±standard
error)
z-value Pr(>|z|)
(Intercept) 8.720 ±0.619 14.094 <2.00 x
10
−16
7.830 ±0.925 8.468 <2.00 x
10
−16
Norway
Canada -5.707 ±0.632 -9.035 <2.00 x
10
−16
-5.690 ±0.961 -5.923 3.17x 10
−9
Finland -0.474 ±0.159 -2.988 0.0028 0.979 ±0.171 5.728 1.01 x 10
−8
Faroe Islands 5.802 ±22.647 0.256 0.798 -5.557 ±0.970 -5.731 1.00 x 10
−8
Greenland -5.167 ±0.590 -8.757 <2.00 x
10
−16
-4.805 ±0.893 -5.381 7.42 x 10
−8
Iceland -4.082 ±0.624 -6.540 6.15 x 10
−11
-3.712 ±0.914 -4.063 4.85 x 10
−5
Svalbard & Jan Mayen -0.059 ±0.267 -0.221 0.825 -1.524 ±0.390 -3.939 8.18 x 10
−5
Sweden -0.673 ±0.134 -5.007 5.52 x 10
−07
0.022 ±0.150 0.147 0.883
USA (Alaska) -4.682 ±0.630 -7.433 1.06 x 10
−13
-5.647 ±0.954 -5.917 3.28 x 10
−9
Protected area TRUE 0.841 ±0.042 19.919 <2.00 x
10
−16
Square root of length of road in
cell
0.805 ±0.026 31.326 <2.00 x
10
−16
0.574 ±0.023 24.717 <2.00 x
10
−16
Log distance to road -0.278 ±0.019 -14.912 <2.00 x
10
−16
-0.278 ±0.025 -11.217 <2.00 x
10
−16
Log distance to airports -0.220 ±0.027 -8.135 4.13 x 10
−16
-0.337 ±0.034 -9.855 <2.00 x
10
−16
Log distance to ports -0.381 ±0.031 -12.481 <2.00 x
10
−16
-0.229 ±0.038 -5.970 2.37 x 10
−9
Log distance to populated places -0.532 ±0.022 -24.243 <2.00 x
10
−16
-0.603 ±0.028 -21.244 <2.00 x
10
−16
https://doi.org/10.1371/journal.pone.0227189.t002
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 9 / 14
boom. Management of tourists and their impacts is often easier where they congregate in a few
small areas as resources can be allocated to these high priority areas bringing economies of
scale. This is particularly important in small, rural communities where both human and finan-
cial capacity to manage tourist impacts can be limited [24]. Though environmental impacts
can be locally high in these well-visited areas, requiring thoughtful management, aggregation
of tourists in a few small areas can reduce the impacts of tourism across the wider landscape
and channel resources into efficient management at the high use sites to sustain tourist satis-
faction despite crowding. Channeling visitors into such ‘sacrificial sites’ may have net benefits
at the landscape scale in places like the Arctic, where wildlife and vegetation are highly sensi-
tive to disturbance and take a long time to recover from human impacts.
The tourism footprint is strongly associated with access, and is particularly dependent on
the presence of roads and airports. This is not surprising, but important to keep in mind when
planning for growth in tourism. The proposed construction of three transatlantic airports in
Greenland has a high potential of boosting tourism in host communities that do not have suffi-
cient capacity to sustainably manage this growth. The sustainability of tourism in Greenland,
and similar developing places around the world depends heavily on building community
capacity that sustains natural resources and local culture, and that protects vulnerable sites and
species from the expansion of tourists into new locations [6]. The spatial extent of the winter
footprint of tourism is about 40% lower than that in summer, with tourists gathering closer to
airports and towns. Managers therefore need to be especially aware of the expansion of tourists
into vulnerable sites and the greater use of protected areas in the summer season. Ports have
only minimal influence on the presence of tourists on land.
While social media data may be useful for rapidly detecting localized booms in tourism in
highly visited regions [48], our analysis indicates that Flickr data is of limited use for identify-
ing local tourism booms in data deficient regions such as in the Arctic. Low rates of visitation
across most of the Arctic combined with the small proportion of visitors that use Flickr [15]
means that just a handful of Arctic locations are visited by Flickr users more than once a
month (Fig C in S1 Fig). One of the few regions where we detected statistically significant
increases in visitation was the Lofoten islands of northern Norway. There, our analysis con-
firmed qualitative trends already noticed by tourism agencies in the region. Twitter has a
higher user base and may be a better source of fine-scale temporal data in the parts of the Arc-
tic with high-speed mobile data coverage [18,49,50]. Changes in ownership of the social media
platforms and changes to data access rules means social media data from other suitable plat-
forms such as Panoramio and Instagram were no longer freely available to academic research-
ers at the time of analysis. Quantitative analysis of social media data requires specialized
computing and technical skills that are not normally available to local tourism management
agencies. Maintaining dialogue between tourism management bodies and local communities
and tour operators remains the most pragmatic way to detect and respond to fine-scale tour-
ism trends in areas where data and technical capacity are limited.
Conclusions
The recent and rapid increase in the footprint of tourists on the Arctic that we document here
is concerning. Upcoming investments in transport infrastructure in places like Greenland and
the promotion of remote areas of the Arctic as tourist destinations, such as Franz Josef in Rus-
sia, will drive a further expansion of the tourist footprint in this unique part of the world. Des-
tinations are also increasingly been marketed as ‘last chance tourism’ attracting visitors to
venture into previously unexplored areas to experience Arctic ecosystems and species at risk of
disappearing [4,51]. For instance, in Hudson Bay in Canada the small community of Churchill
Quantifying the Arctic tourism footprint with social media data
PLOS ONE | https://doi.org/10.1371/journal.pone.0227189 January 16, 2020 10 / 14
where polar bears spend increasingly more time on shore due to climate change, have experi-
enced a rapid influx of tourists [4]. Wildlife viewing of vulnerable species, such as polar bears,
narwhals and beluga whales, is putting additional pressure on species threatened by climate
change [52,53]. Our high resolution and seasonal maps of Arctic tourism allow tourist man-
agement bodies and environmental organizations to pinpoint the places visited by tourists and
the relative magnitude of visitation across the Arctic and to detect landscape-wide trends in
visitation that need to be managed. Strategic and thoughtful assessment of whether this ongo-
ing growth in Arctic tourism is sustainable or desirable for Arctic ecosystems and communi-
ties is urgently required.
Supporting information
S1 Appendix. Sensitivity analysis of photo exclusion threshold.
(PDF)
S2 Appendix. Sensitivity analysis of resolution.
(PDF)
S3 Appendix. Global and Arctic Flickr trends.
(PDF)
S4 Appendix. Uncertainty around footprint estimates.
(PDF)
S5 Appendix. GAM model performance.
(PDF)
S1 Fig. Annual maps of tourism growth.
(PDF)
S1 Table. Comparison with official visitor statistics.
(PDF)
Author Contributions
Conceptualization: Claire A. Runge, Vera H. Hausner.
Data curation: Claire A. Runge.
Formal analysis: Claire A. Runge, Remi M. Daigle.
Funding acquisition: Vera H. Hausner.
Methodology: Claire A. Runge, Remi M. Daigle.
Software: Remi M. Daigle.
Visualization: Claire A. Runge, Remi M. Daigle.
Writing – original draft: Claire A. Runge.
Writing – review & editing: Claire A. Runge, Remi M. Daigle, Vera H. Hausner.
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