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Revealing Cultural Ecosystem Services through Instagram Images: The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance

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With the prevalence of smartphones, new ways of engaging citizens and stakeholders in urban planning and govern-ance are emerging. The technologies in smartphones allow citizens to act as sensors of their environment, producing and sharing rich spatial data useful for new types of collaborative governance setups. Data derived from Volunteered Geographic Information (VGI) can support accessible, transparent, democratic, inclusive, and locally-based governance situations of interest to planners, citizens, politicians, and scientists. However, there are still uncertainties about how to actually conduct this in practice. This study explores how social media VGI can be used to document spatial tendencies regarding citizens' uses and perceptions of urban nature with relevance for urban green space governance. Via the hashtag #sharingcph, created by the City of Copenhagen in 2014, VGI data consisting of geo-referenced images were collected from Instagram, categorised according to their content and analysed according to their spatial distribution patterns. The results show specific spatial distributions of the images and main hotspots. Many possibilities and much potential of using VGI for generating, sharing, visualising and communicating knowledge about citizens' spatial uses and preferences exist, but as a tool to support scientific and democratic interaction, VGI data is challenged by practical, technical and ethical concerns. More research is needed in order to better understand the usefulness and application of this rich data source to governance.
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Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 1
Urban Planning (ISSN: 2183-7635)
2016, Volume 1, Issue 2, Pages 1-17
Doi: 10.17645/up.v1i2.609
Article
Revealing Cultural Ecosystem Services through Instagram Images:
The Potential of Social Media Volunteered Geographic Information for
Urban Green Infrastructure Planning and Governance
Paulina Guerrero 1,2, Maja Steen Møller 1,*, Anton Stahl Olafsson 1 and Bernhard Snizek 3
1 Department of Geosciences and Natural Resource Management, University of Copenhagen, 1958 Frederiksberg C,
Denmark; E-Mails: paulinaguerrero1@gmail.com (P.G.), masm@ign.ku.dk (M.S.M.), asol@ign.ku.dk (A.S.O.)
2 Institute of Environmental Planning, Leibniz Universität Hannover, 30060 Hannover, Germany;
E-Mail: guerrero@umwelt.uni-hannover.de
3 metascapes.org, 1752 Copenhagen, Denmark; E-Mail: bs@metascapes.org
* Corresponding author
Submitted: 1 March 2016 | Accepted: 5 May 2016 | Published: 6 June 2016
Abstract
With the prevalence of smartphones, new ways of engaging citizens and stakeholders in urban planning and govern-
ance are emerging. The technologies in smartphones allow citizens to act as sensors of their environment, producing
and sharing rich spatial data useful for new types of collaborative governance set-ups. Data derived from Volunteered
Geographic Information (VGI) can support accessible, transparent, democratic, inclusive, and locally-based governance
situations of interest to planners, citizens, politicians, and scientists. However, there are still uncertainties about how to
actually conduct this in practice. This study explores how social media VGI can be used to document spatial tendencies
regarding citizens’ uses and perceptions of urban nature with relevance for urban green space governance. Via the
hashtag #sharingcph, created by the City of Copenhagen in 2014, VGI data consisting of geo-referenced images were
collected from Instagram, categorised according to their content and analysed according to their spatial distribution
patterns. The results show specific spatial distributions of the images and main hotspots. Many possibilities and much
potential of using VGI for generating, sharing, visualising and communicating knowledge about citizens’ spatial uses and
preferences exist, but as a tool to support scientific and democratic interaction, VGI data is challenged by practical,
technical and ethical concerns. More research is needed in order to better understand the usefulness and application of
this rich data source to governance.
Keywords
cultural ecosystem services; e-governance; geosocial mapping; green space governance; spatial analysis; VGI
Issue
This article is part of the issue Volunteered Geographic Information and the City, edited by Andrew Hudson-Smith
(University College London, UK), Choon-Piew Pow (National University of Singapore, Singapore), Jin-Kyu Jung (University of
Washington, USA) and Wen Lin (Newcastle University, UK).
© 2016 by the authors; licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu-
tion 4.0 International License (CC BY).
1. Introduction
It is widely recognized that the presence of urban na-
ture is indispensable for a well-functioning and hospi-
table city (Beatley, 2011). Today, urban nature or ur-
ban ecosystems are often conceived in relation to the
concept of green infrastructure (GI). GI is a planning
approach which links all types of urban nature together
in a network which provides numerous benefits, or
ecosystem services, such as: offering a recreational role
in everyday life, playing an important part in conserv-
ing biodiversity, adding to the cultural identity of a city,
easing and improving the environmental quality of the
city, and providing natural solutions to technical chal-
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 2
lenges such as sewage treatment in cities (Andersson
et al., 2014; Braquinho et al., 2015; Lovell & Taylor,
2013). It is also generally understood and scientifically
proven that GI in cities offers health benefits such as al-
leviating mental, physical and social pressureas well
as being associated with economic benefits (Secretariat
of the Convention on Biological Diversity, 2012; Tzoulas
et al., 2007). The health benefits, aesthetic enjoyment
and recreational opportunities of GI can be conceived
as Cultural Ecosystem Services (CES) (Andersson, Ten-
gö, McPhearson, & Kremer, 2015; Millennium Envi-
ronmental Assessment, 2005). Thus, CESs connect na-
ture to human values and behaviour and can act as
gateways for improving urban sustainability (Anders-
son et al., 2015). However, the perceptions of CESs are
likely to differ as they are dependent on the social con-
text and personal values compared to, for example, the
market or scientifically defined recognition of the value
of food production or the carbon dioxide intake of a
tree (DeFries et al., 2005). While some ecosystem ser-
vice categories are more tangible which facilitates their
economic and biophysical valuation, CES values are
more difficult to measure and often call for the use of
more holistic and innovative approaches and methods
(Gómez-Baggethun et al., 2013).
However, as beneficial as GI is, it faces constant
competition for inclusion in urban planning and deci-
sion-making. Competition for space, adequate valua-
tion, and prioritisation on political agendas are just some
examples of the actual and future hurdles (Braquinho et
al., 2015). According to Mussachio (2013), there is cur-
rently a need to further identify the relationship be-
tween ecosystem provisions, human values and percep-
tions. In other words, citizens must connect with their
urban nature for landscape sustainability to genuinely
take root in a city (Andersson et al., 2015; Mussachio,
2013). Hence, cities will have to innovate and find ways
to incrementally and aptly value urban nature, as well as
better understand the complexity of ecosystems and
how citizens are already experiencing the nature availa-
ble to them. An enhanced understanding of the distribu-
tion of valuable urban GI features as perceived by citi-
zens may be the key to the improved maintenance and
management of natural resources. This, however, re-
mains challenging particularly when it comes to CESs
(Casalegno, Inger, DeSilvey, & Gaston, 2013) as they do
not come in easily tangible measures, but are rather de-
pendent on individual perceptions. The use of technolo-
gies such as social media and smartphones may repre-
sent a way around these challenges as they create
interactive channels for broad civic participation and
new ways to deliver valuable public and scientific infor-
mation (Brown & Kyttä, 2014; Linders, 2012).
Volunteered Geographic Information (VGI), which is
defined as the use of a range of technologies to create,
assemble, and disseminate geographic information
(Goodchild, 2007), makes up the dataset for this study.
These data are voluntarily provided by individuals and
may come from social media services, wikis, and other
media, and are, therefore, often associated with Citi-
zen Science (Jiang & Thill, 2015). This individualised and
dynamic information represents a notable shift in the
“content, characteristics and modes of geographic in-
formation creation, sharing, dissemination and use”
(Sui, Goodchild, & Elwood, 2013, p.9). With this data
source in mind, engaging citizens in governance set-ups
using modern technology should not be too complicat-
ed: in this example from Copenhagen, an Instagram
hashtag (#) campaign created more than 50,000 re-
sponses online on Instagram.
Instagram is an online mobile application focused
on sharing photographs and providing a platform for
social networking. Instagram enables its users to share
pictures taken with a smartphone camera publicly with
a hashtag (#), if the user wishes. Instagram is owned by
Facebook© and is forming a global community that
shares more than 60 million photos every day (Insta-
gram, 2016a, 2016b).
The challenging part really appears to be the act of
translating such data into useful, scientifically reliable
results. This paper explores such possibilities with a
particular focus on CES patterns.
1.1. Study Aim
This study explores how harvesting, analysing and in-
terpreting user-generated geographic urban nature
images stemming from social media can potentially
add to a modern GI governance set-up based on digital
data sharing. Thus, the study aims to demonstrate an
innovative approach to analysing the character of dif-
ferent urban nature areas as represented by non-
experts. This approach might be helpful for under-
standing how urban ecosystems are used and may also
add to inclusive governance by visualising and attrib-
uting cultural ecosystem services to GI. In the follow-
ing, we demonstrate our approach to harvesting and
analysing VGI data from the Instagram API through the
hashtag #sharingcph.
This is achieved by studying: firstly, whether shared
Instagram images may be used to obtain information
about urban nature in a city; secondly, by investigating
spatial patterns of shared images which deal with urban
nature; thirdly, by showing how this new type of spatial
data relates to the formal GI in terms of public green and
blue spaces, and; finally, to discuss the future potential
role of social media VGI for supporting urban planning
and the promotion of CES in a city (i.e., e-governance).
1.2. Volunteered Geographic Information and
E-Governance
Due to rapidly developing information and communica-
tion technologies, the opportunities for broad stake-
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 3
holder inclusion are becoming more numerous since
these technologies can act as tools to support commu-
nication between government and citizens. Today,
most citizens in the western world have access to the
Internet and thanks to devices such as smartphones
with online access and embedded sensors, the genera-
tion of data is unprecedented (Batty, 2013; World
Bank, 2016). This shift is adding new ways and perspec-
tives to knowledge sharing and knowledge gathering
that can support the development of ideas and prac-
tices regarding urban planning and governance. Online
and smartphone applications have the potential to act
as media for transparent, democratic, inclusive and sit-
uation-based participatory processes of interest to
planners, citizens/users, politicians and scientists.
Due to many technological advances such as ubiqui-
tous smartphones and free applications, our societies
are currently in a situation where we have the ability to
“keep track of where everything (and everyone) is in real
time” (Sui et al., 2013, p. 3). These advances, as poign-
antly expressed by Sui et al. (2013) and Johnson and
Sieber (2013), have “unleashed the potential of a geog-
rapher within everybody” (Sui et al., 2013, p. 9) and cre-
ated a society which can “act as sensors of their envi-
ronment” (Johnson & Sieber, 2013, p. 66) or social sensing
as Liu et al. (2015) puts it. Geo-referenced citizen science
is part of the big data phenomenon, which has experi-
enced explosive growth in the past few years and is
“transforming all aspects of governments, businesses,
education and science” (Sui et al., 2013, p. 3). The major-
ity of this big data information is “data pertaining to ac-
tivities that humans are intimately involved with”, i.e.
everyday actions of personal value (Batty, 2013, p. 275).
Several services such as Twitter and Instagram combine
geo-referenced images and short texts. Via Application
Programming Interfaces (APIs), anyone can access these
images and perform text and spatial analysis.
VGI is an information rich resource, which is public-
ly available and is shared directly by users thereby cre-
ating an enormous database (Goodchild, 2007; Jiang &
Thill, 2015). Clearly this information is valuable for
branding and marketing purposes, and has been used
in research, e.g., tourism, disaster relief and transpor-
tation planning (Damiano, Pau, & Lehtovuori, 2015;
García-Palomares, Gutiérrez, & Mínguez, 2015; Roche,
Propeck-Zimmerman, & Merikskay, 2011; Sui et al.,
2013), but it is also interesting for a broad base of so-
cial, spatial and behavioural sciences as it often links
experiences with time and place. Its applications are
just beginning to unfold and explorative research, such
as this study, is harnessing this potential. Urban plan-
ners and governments are looking to incorporate new
technological trends, and VGI not only provides an op-
portunity to connect and communicate with citizens,
but this data can be further analysed to investigate be-
haviours, trends and issues which arise, or are already
present, in a city (Tasse & Hong, 2014).
When governments connect with the VGI commu-
nity it can result in a mutually beneficial relationship
between governments and citizens and can in turn
“support greater transparency, efficiency, and effec-
tiveness of government services” (Johnson & Sieber,
2013, p. 65). The concept of e-governance deals with
this type of interaction and is defined as technology-
mediated relationships between citizens, government
and businesses facilitation, i.e. communication, policy
evolution, and the democratic expression of the will of
citizens (Marche & McNiven, 2003; Stock, 2011). E-
governance situations range from citizens influencing a
government by delivering information that helps it to
be more responsive and reflective, to government act-
ing as a facilitator for citizens’ actions and to situations
where citizens self-organise and co-produce informal
arrangements without the government playing an ac-
tive role (Linders, 2012). Cities can connect with VGI
communities through formal or informal processes and
may involve tools and mechanisms that allow citizen
participation (Johnson & Sieber, 2013).
1.3. Social Media VGI and Cultural Ecosystem
Services (CES)
A special type of VGI is geo-referenced social media da-
ta originating from sources such as Twitter, Facebook
or Instagram, which is sometimes referred to as ambi-
ent geospatial information (“AGI”) (Stefanidis, Crooks,
& Radzikowski, 2013). According to Batty (2013), in-
herent and intimate personal value is attached to what
is shared via social media (Batty, 2013; Jiang & Thill,
2015). Thus, social media VGI reflects a connection and
a shared experience with one’s surroundings, while an
additional strength is that it comes directly from citi-
zens themselves (Johnson et al., 2013). VGI is creating a
new medium for communicating information that cir-
cumvents traditional paths and which can help to fill a
gap in available data. An example of this is social media
VGI data consisting of digital photographs with geo-
tags and related semantics or tags. However, the ability
to quantify or even conceptualize VGI remains limited
(Feik, Roche, & Sui, 2013). As such, the possibilities for
analysis rest with innovative and evolving methods.
Examples of such innovative methods are studies
which illustrate how social media data from non-experts
can be mined (Feick et al., 2013), and studies which link
CESs to VGI stemming from social media to map and re-
flect these services (Casalegno et al., 2013; Leetaru,
Wang, Cao, Padmanabhan, & Shook, 2013; Pastur, Peri,
Lencinas, García-Llorente, & Martín-López, 2016). Hence,
recent studies have shown that geo-tagged online imag-
es provide an effective metric for mapping the key com-
ponents of CESs, and that the concept of image sharing
contains an attached value that can be spatially analysed
(Casalegno et al., 2013; Pastur et al., 2016).
However, so far, to our knowledge, no studies have
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 4
explored the potential of linking social media VGI to
CESs in an urban setting. Combining the fact that cities
and urban CESs can be considered drivers for environ-
mental awareness (Andersson et al., 2015) and the fact
that most VGI originates from urban settings (Haklay,
2013), social media VGI is, thus, a data source which is
rich in spatial, temporal, quantitative and qualitative
information the application of which to urban planning
demands to be explored (Casalegno et al., 2013; Dami-
ano et al., 2015; Pastur, et al., 2016).
2. Data and Methods
This section presents the geosocial data derived from
Instagram, the methods applied and a classification
based on images, steps in geo-processing and finally
the application of diverse spatial analysis methods.
2.1. Data Acquisition and Study Area
The city of Copenhagen, defined as the administrative
municipalities of Copenhagen and Frederiksberg, acts as
the study area (Figure 1). As of 2015, the city of Copen-
hagen had an urban population of 743,564 inhabitants
and an area of 179.29 km2 (Statistics Denmark, 2015).
In 2014, Copenhagen was named the Green Capital
of Europe by the European Commission. As a conse-
quence of this award, the city of Copenhagen initiated
a “sharing” concept with the purpose of promoting and
communicating sustainable solutions. A Sharing Co-
penhagen office was established to facilitate partner-
ships between privates, organisations and the City of
Copenhagen (Isherwood, 2013). This office launched
the #sharingcph campaign by inviting people to share
images of Copenhagen online on social media, by tag-
ging them #sharingcph. The message was distributed
by events, websites and posters in the city. The
#sharingcph campaign generated more than 50.000
images online (not all with geo-location). The willing-
ness of the citizens to participate led to the wish or
idea that the rich data could be transformed into
something more than just pictures online, but how ex-
actly to do so was unclear to the Sharing Copenhagen
office. The Sharing Copenhagen office expressed a wish
to be able to give the data back to the citizens as well
as an interest in what we, as urban researchers, would
be able to extract from the #sharingcph images (M.
Møller & B. Snizek, personal communication, January
27, 2015). Based on these motivations, we explored
the possibilities of extracting, analysing and applying
the data derived from #sharingcph to urban planning.
All Instagram images tagged with #sharingcph were
retrieved via its API (Instagram, 2016a, 2016b) and
stored in a PostGIS geodatabase (Obe & Hsu, 2015). This
data included links to the images stored on Instagram,
their text, the date they were taken and their geograph-
ical locations. 37,329 Instagram images taken in the pe-
riod July 1st 2012 to March 25th 2015 were retrieved.
Figure 1. The study area consists of the central part of the Copenhagen region, which is defined as the administrative
delineation of the municipalities of Copenhagen (outer dashed line) and Frederiksberg (inner dashed line). Officially
designated blue and green spaces form the central building blocks of the city’s Urban Green Infrastructure. Source:
Municipality of Copenhagen.
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 5
As the search also returned images from outside the
boundary of Copenhagen, only the 22,500 (N) geo-
referenced images located within the study area were
finally selected (Table 1). While 22,500 images make a
very solid data basis, we had to select a smaller sample
of these for a more detailed analysis of image content,
i.e. a categorisation of the images (described below). A
sample size of at least 2,397 images would allow one to
make predictions about this image population with a
95% confidence level, assuming a +/- 2% margin of er-
ror and a standard deviation of 0.5. Therefore, the final
categorised total number of images was 2,572 (n).
According to Statistics Denmark, in 2014, 24% of
Danes were using Instagram, a noticeable increase
from the result of a 2013 survey, which found that only
12% of Danes had an Instagram account (Statistics
Denmark, 2015; YouGov, 2013). The 2013 survey also
states that the average user spends two hours and 55
minutes on Instagram per week (YouGov, 2013). The
population (N) consisted of 1,131 users who contribut-
ed between 1 to 890 images each to the dataset; the
average number of images per user being 17.4. The fi-
nal sample of images (n) was shared by a total of 944
individuals with an average of 2.7 images posted per
user. This low average number of images per user was
retrieved by setting a threshold of max. 50 images per
user, thereby allowing for a more distributed sample in
relation to the number of users.
2.2. Categorisation of Images
A reliable image categorisation had to be carried
out to identify the shared urban nature within the city.
An attempt to categorise the images automatically
based on related #tags was conducted, but it did not
produce a satisfactory outcome compared to a manual
classification of the image content. Hence, a manual
classification of the images was performed instead. As
Hu, Manikonda and Kambhampati (2014) highlighted,
determining the relevant image content categories is a
challenge as images contain richer features compared
to text. Since we wanted to study urban nature, we
chose to apply a framework of lay people’s nature def-
initions based on Buijs and Volker’s Dimensions of the
Prototypicality of Nature (Buijs, 2009). This category-
scheme aims to be inclusive and incorporate the many
ways in which nature is defined, perceived and inter-
preted by lay people (Buijs, 2009). Buijs and Volker's
categories are: (1) Elements, (2) Spontaneous nature,
(3) Productive Nature, (4) Designed Nature, and (5)
Domesticated Nature (Buijs, 2009). We added a sixth
category Biocultural Nature in order to cover situations
with images of a visible human-nature interaction, such
as nature-based recreation (Figure 2).
A web-based categorising interface was developed,
which made it possible to categorise images into the
categories mentioned fairly quickly simply by clicking
on one button per image. The interface is designed to
include the image that was posted, a map of the loca-
tion where it was uploaded and the semantics associ-
ated with the post (i.e., in order: image-map-
semantics-buttons). To be able to filter away the imag-
es that were not representative of nature and give the
person conducting the classification the option to
choose from the images which were not of nature or
did not fit into any class, the classes (1) Not an Urban
Nature Image, and (2) I Don’t Know were added.
Figure 3 is an example of the online interface; in
this case, the selected category was Designed Nature.
A three-step hierarchical how-to guide was pro-
duced to further elucidate the categorisation process
(see Table 2). Two researchers then hand-categorised
the pictures via the online medium according to the
previously mentioned categorisation system. Based on
this categorisation, the categorised sample size (n) was
obtained.
In order to conduct an assessment of the reliability
of this categorisation, two external researchers were
informed about the categorisation scheme and were
given the hierarchical guide and asked to categorise
498 of the pictures which had been randomly selected
and previously categorised. To achieve a 95% confi-
dence level and a confidence interval of 4, a total of
487 images had to be assessed; thus, the 498 images
that were categorised for the assessment is above the
required sample. This reliability assessment returned a
73.1% match with the previous categorisation, leaving
26.9% in disaccord. The majority of the images that
were in disaccord (41% or 54 of the 498 images) were
not categorised as urban nature, but had been catego-
rised as green in the reliability assessment round. With
73.1% of the images categorised under the same cate-
gory, this indicates that while the categorization
scheme was of use for this data set, individual interpre-
tation in any manual categorization will always play a
role and will never be exact. Additionally, the number
of categories could be a hindrance as this creates more
options and in turn more variability.
Table 1. Basic description of Instagram images, number of users who have shared the images (i.e. Instagram users), and
the range of images per user in the original, geo-referenced, and final sample of categorised images.
Harvested
Geo-referenced in City
(study area)
Sample size of categorised
images
Number of images
37,329
22,550 (N)
2,572 (n)
Number of users
1,173
1,131
944
Images posted per user
Min = 1, Max = 893
Min = 1, Max = 890, Avg = 17.4
Min = 1, Max = 50, Avg = 2.69
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 6
Figure 2. The used image categorization classifies urban nature into six categories based on Dimensions of Nature.
Examples of image content are shown below for each urban nature category. Adapted from Buijs (2009).
Figure 3. Example of the categorization interface showing the image, the location it was taken in, its text and the eight
categorization options.
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 7
Table 2. A three-step hierarchical how-to guide was produced to clarify the categorisation process.
Step
Hierarchy Guide for Urban Nature
Categorisation
Elaboration and general examples
1
Identify main focus of image and categorise
based on Urban Nature or Not Urban Nature
2
When multiple options are possible, more
weight is given to the main focus of the
image.
3
Use location and tagged semantics as
secondary support for classification.
An image of an urban scene with slight vegetation present
(i.e., leaves) with semantics relating to the presence of
the vegetation is urban nature, e.g., #leaf, #autumn.
Table 3. Categorisation result of the content analysis.
Description
Categorised sample (n=2,572)
Urban Nature images (n=874)
Image Content
Not Urban Nature, 64.8%
Designed Nature, 42.6%
The Elements, 27.2%
Biocultural, 13.7%
Spontaneous, 9.8%
Domesticated, 3.9%
Productive, 2.7%
Urban Nature, 34%
Unable to be categorised, 1.2%
2.3. Spatial Data Analyses
Spatial analyses were only performed on the sample
size of 2,572 images (n). The category, I Don´t Know
representing 1.2% of the data, was omitted from the
spatial analyses.
The data points, i.e., images, were processed into
and analysed via GIS. A spatial calculation (spatial join)
and visualisation was conducted in order to observe
and compare the distribution of urban nature images
in relation to all images and in relation to the official
green infrastructure (with a 50m buffer to include im-
ages taken in border zones with a view of the urban
nature site). Further, a hotspot analysis was conducted
to reveal clusters of images. In this analysis, the radius
was set to 400 metres with a threshold value of nine
images; hence, all image clusters with more than nine
images were considered a hotspot. Finally, a distance
analysis was performed to explore the spatial character
of the data in more detail. The distance of each urban
nature image from the city centrederived as the cen-
troid from the city centre´s boundary polygonwas
calculated with the Hub Distance Tool. This analysis re-
turned a vector layer that connects each point to the
specified central hub. The length of each line was cal-
culated and this data was analysed for frequency at
specific kilometres and a corresponding histogram was
generated. In other words, the analysis returned the
number of images found at specific distances from the
city centre. This facilitated the identification of dis-
tances from the centre where a relatively high or low
number of images had been shared, i.e., peaks and val-
leys of shared urban nature in the city.
3. Results
This section presents the results of the data analyses.
3.1. Categorisation Result of Urban Nature Related
Images
The results of the categorised process reveal that Ur-
ban Nature represents 34% (874 images) of the images
in the sample size (Table 3). The urban nature images
were further classified according to the six perceived
dimensions of nature. The ‘Designed Nature’ category,
which includes parks, urban trees, and canals, repre-
sented almost half (42.6%) of the urban nature images.
Further, almost 1/3 of the images were classified as the
‘Elements’, e.g. sunset, while about 10% of the images
were classified as ‘Biocultural Nature’ (e.g. nature-
based recreation) or ‘Spontaneous Nature’ (e.g. reflec-
tions in puddles). Logically, few images were classified
as ‘Domesticated Nature’ or ‘Productive Nature’. Some
examples of categorised urban nature images are
shown in Figure 4.
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 8
Figure 4. Examples of urban nature images as categorised. Photographer’s Instagram user names in parenthesis (start-
ing from top left): a: Designed (@tbsptrsn), b: The Elements (@might_be_wrong), c: Biocultural (@remosteen) d: Spon-
taneous (@copenhagen_streetlife), e: Domesticatedshowing a lion from the Copenhagen zoo (@mmhenriksen), and
f: Productiveshowing oyster harvest in Copenhagen harbour (@maritimenyttehaver).
Figure 5. The spatial distribution of Urban Nature (filled symbol) and Not Urban Nature images (outline symbol).
3.2. Spatial Patterns of Urban Nature
While Urban Nature images are spatially distributed in
the city and similar in distribution to Not Urban nature
images, there is a pattern at certain locations to create
clusters of images classified as Urban Nature (see Fig-
ure. 5).
Of the total number of images, 44.4% were located
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 9
in green spaces regardless of categorisation, while
63.6% of the urban nature images were located in
green spaces. Thus, the majority of the urban nature
images coincided with the managed green spaces of
the city; these managed green spaces contain the ma-
jority of the shared urban nature images of Copenha-
gen. On the other hand, 36.4% of the urban nature im-
ages, i.e., about one-third, were located outside the
managed urban nature. This provides an interesting
perspective as it shows that Copenhageners also share
and experience urban nature outside designated public
green spaces. Thus, it is of importance for the city to be
aware that this nature is of value and is an asset for the
city’s green infrastructure.
These associations and disassociations with desig-
nated green spaces allow city planners to visualise the
actual patterns of how citizens share images of the
green spaces of the city. Similarly, this relationship be-
tween shared urban nature and designated green
spaces provides insight which may prove valuable for
the management of the urban nature of a city.
3.3. Hotspots of Urban Nature Images
A hotspot analysis was applied to identify areas with a
high number of urban nature images and areas with a
low number. The hotspot analysis returned 19 locations
where more than nine images had been taken. To find
the total number of images located at these spots, the
attributes were selected by either based on the green
spaces layer as borders or on the size of the hotspot.
Two of these locations were found to be clusters which
were probably due to a user uploading various images
indoors, i.e., not at the location where the images had
been taken (see upload error in limitations section).
These two areas, consisting of a total of 28 images, were
thus omitted. The top ten clusters with the highest
number of images were then selected and individually
analysed to identify their specific location and the num-
ber of images at each location. Noticeably, the top ten
identified hotspots correspond to locations which are
designated as green spaces, see Figure 6. The number of
images taken at the top locations ranged from 13 to 33.
3.4. Distance Analysis
To analyse the data further, a distance analysis was con-
ducted. As previously explained, there was an accumula-
tion of 28 images, both of nature and non-nature; which
in order to avoid skewing the spatial location, these im-
ages were excluded as they were clearly not spatially
representative. Figure 7 is a visualisation of the distance
analysis with a radial behaviour of the data with its focal
point at the city centre. As the histogram shows, the dis-
tance analysis facilitates the identification of specific dis-
tances from the centre where a high or low number of
images had been shared, i.e., peaks and valleys of
shared urban nature in the city, see Figure 8.
Figure 6. Top 11 Nature Hotspots relative to public green and blue spaces.
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 10
Figure 7. Images' relation to the city centre of Copenhagen.
Figure 8. Urban Nature images in relation to distance from Copenhagen’s city centre (r = -0.86).
The outcome of this analysis shows that more infor-
mation, i.e., images, regarding urban nature will prob-
ably come from areas near the city centre rather than
from the outer realms of the city. People have an affin-
ity for sharing images from these central parks. Future
research could focus on attempting to determine the
causes of this affinity, which may include accessibility,
park features or leisure use. As the histogram shows,
the peak seen at 2 to 3 km from the centre could also
be explained by the actual green space structure of
Copenhagen’s parks as key green spaces are located
approximately 2 km from the city centre.
4. Discussion
The images from this study constitute a valuable
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 11
source of VGI data and provide relevant information
about the spatial representation of Copenhagen's ur-
ban nature in an Instagram dataset. Of all the aspects
Copenhagen inhabitants could have shared about their
city in #sharingcph, 34% of the images represented Ur-
ban Nature. It also shows that a general mobile appli-
cation such as Instagram can be used to collect VGI
content and present urban nature patterns in a city
(Tasse & Hong, 2014).
Casalegno et al. (2013) showed that CES can be
mapped with geo-referenced social media images and
suggested that shared urban nature images, which
serve as proxies for CESs such as aesthetics or sense of
place, have an attached value and can be used as a tool
for gathering and analysing this information (Casalegno
et al., 2013: Stedman, Beckley, Wallace, & Ambard,
2004). This study supports the conclusion that CESs can
be mapped via shared urban nature images.
This research is related to works of other method-
ologies that utilise images such as Visitor Employed
Photography (VEP), in order to obtain an understand-
ing of people’s perceptions of parks and natural envi-
ronments by interpreting their photographs (Mackay &
Couldwell, 2004). Even though the image-taking meth-
od is controlled in VEP, this VGI method is in line with
the idea that photographs can be analysed to identify a
sense of place, attachment, aesthetics and other fac-
tors pertaining to the human-environment interaction
in natural spaces (Garrod, 2007; Mackay & Couldwell,
2004; Stedman et al., 2004). The analysis of images can
provide valuable information as photographs can be
considered “representations of specific attributes of
various dimensions of (an) experience” (Garrod, 2007,
p. 14). According to Stedman et al. (2004), photographs
offer insight into specific historical, cultural and social
ways of seeing the world and these images can stand-
alone as data sources since they are expressions of the
ideas themselves. In other words, while surveys and in-
terviews can provide great insight, images can capture
certain perspectives, emotions and attitudes such as a
sense of place, aesthetic value and attachment (Sted-
man et al., 2004).
Considering images as proxies for CES, this research
supports the assertion that VGI can be used to identify
places that people share due to the CES offered
(Casalegno et al., 2013; Pastur et al., 2016). The MEA
includes inspiration, aesthetic values, sense of place,
and recreation and tourism among the CES nature pro-
vides (Millennium Ecosystem Assessment, 2005). As
such VGI images, which are free expressions of peo-
ple's perception of nature whether it is the inspiring,
aesthetic or sense of place service provided, can be
used as CES proxies. For example, the top urban
hotspots of this study can be considered green spaces
with high CES. A city authority can use this data as a
driver for protection and investment in these areas as
the information is coming from those benefiting from
the services (Sherrouse, Clement, & Semmens, 2011).
This area of research and methodologies are still
nascent and further explorative research focused on
extended objectives must be explored to make any
definite findings. For example, the fact that a low num-
ber of images were taken in a particular area does not
necessarily mean that no perceived urban nature value
is present, but rather indicates that other variables,
e.g., accessibility issues, might be obstacles (Jiang &
Thill, 2015). Further, it should be noted that spatial dis-
tributions of most activities extracted from social sens-
ing data are positively correlated with population den-
sity (Liu et al., 2015). An overarching key consideration
in visualising and analysing VGI is simply that it high-
lights patterns and informationhere Instagram imag-
esthat are already present (Tasse & Hong, 2014).
44.4% of the total number of shared images was
taken in the green spaces of Copenhagen; furthermore,
63.6% of the green images came from these locations.
This city provides numerous green spaces for its citi-
zens and aims to promote the accessibility of these
spaces for its citizens in order to promote human-
environment interactions. According to 2012 data, 80%
of Copenhageners lived at a distance of 300 metres
from a green area (European Green Capital, 2012).
People have access to the green spaces in their city
and, as this study shows, they share images from these
locations. There is currently a call to incorporate GIS
methods into urban planning as this provides a more
tangible way of representing issues regarding human-
environment interactions (Kabisch, Qureshi, & Haase,
2015). Through the spatial representation of urban na-
ture VGI in Copenhagen, we use GIS to analyse these
interactions and green space social values.
The distance analysis reveals a distinct centrally
based radial-pattern with regards to the VGI data origi-
nating in Copenhagen, i.e., within a 2-3 km radius of
the city centre. Accordingly, this study shows that ur-
ban nature VGI of Copenhagen is not evenly dispersed
throughout the city; there are hotspots and specific
spatial behaviours. As such, spatial patterns in the data
and user-representability, among others (see limita-
tions), need to be addressed and understood if social
media VGI is to be used in decision-making.
While VGI is considered separate from public partic-
ipation geographic information system, or PPGIS, it is
nevertheless a related field (Brown & Kyttä, 2014). VGI
analysed through GIS “research and practice remains
embryonic,” and consequently, this study follows the
call for experimental design in methodology (Brown et
al., 2014). This method of mapping CES based on a
large set of publicly shared images, while notably pas-
sive, i.e., voluntary, answers the call to increase public
participation rates in PPGIS ecosystem services map-
ping (Brown & Kyttä, 2014). The VGI data obtained
from large sets of social media is “understood in the
context of big data” (Sui et al., 2013, p.4). For ES map-
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 12
ping, this is of enormous value as the use of
smartphones and social media applications will in-
crease, which will enhance the quantity and represent-
ability of this data source for future research. As such, by
exploring this field, this study aims to improve our un-
derstanding of the mapping of ecosystem services so
that ecosystem services become more highly valued and
to support green decision-making in urban settings.
The spatial representation of CES shows the locali-
sation of highly valued ecosystems, as well as, the iden-
tification of “critical focal areas for cultural services
management” (Plieninger, Dijks, Oteros-Rozas, & Biel-
ing, 2013, p. 119). Additionally, the ability to map cul-
tural in addition to the provisioning and regulating
services of a city gives a more complete picture of ES,
as well as a comparison of the ecosystem services at
play (Plieninger et al., 2013). Interestingly, Pastur et al.
(2016) also indicates that social media image data may
potentially help to spatially visualise and monitor the
medium and long-term conditions and trends of CES
(Pastur et al., 2016). In the same way as remote sens-
ing helps to identify critical areas of land use change
that affect provisioning and regulatory ES, with this da-
ta we are also able to monitor changes in cultural eco-
system services by social sensing (Liu et al., 2015).
Another advantage of using geo-referenced images
is that they offer a means of determining CES values
that are hard to capture with just words such as aes-
thetics or sense of place (Pastur et al., 2016; Stedman
et al., 2004). Integrating CESs into urban planning has
been problematic due to their intangibility, complex re-
lationships with biophysical variables and the difficulty
connected with attributing values (Pastur et al., 2016).
This research illustrates that VGI from social media
provides information, in many cases unarticulated but
present, about a city’s urban nature and its CESs. This
research complements other studies which propose
methods to integrate and value CESs in decision-
making processes (Casalegno et al., 2013; Pastur et al.,
2016; Plieninger et al., 2013).
4.1. VGI Use for Urban Planning and E-Governance
This study seeks to provide insights into addressing the
potential of using social media VGI for the assessment
of CES in urban planning and governance.
In a time where a growing number of cities around
the world comply to open data politics following con-
cepts such as ‘Smart Cities’ and more citizens navigate
and interact online, an increasing interest of mining
and understanding and using these digital data is seen
in science, politics and planning (Huijboom & Van den
Broek, 2011; Kitchin, 2014). VGI as a volunteered, in-
formation-rich data source can help to illuminate the
(nature) pulse of a city, i.e., what, where and how ur-
ban nature is ‘shared’. Such information could be use-
ful for urban planners who may be able to use it as a
driver for development or maintenance projects as
they could gain better understanding of how citizens
‘react’ to e.g. urban nature. The information holds rel-
evance in planning and design processes because it
provides a potential plethora of information regarding
local and detailed knowledge about spatial conditions
and characters as well as social connections. Such in-
formation can be used to better understand city dy-
namics, i.e. uses and preferences in given urban spaces
(Seeger, 2008). Another outcome of VGI when inte-
grated in planning and politics is that citizens may be
empowered by ‘sharing’ if they thereby become in-
volved in solutions to better understand, protect, and
develop their environment. Relating the use of VGI to
the urban CES framework may allow planners, not only
to value urban nature more effectively, but also to ad-
equately plan and protect a city's biodiversity and its
citizens’ well being.
VGI is a means of communication that is just start-
ing to be used to create new responsive relationships
between governments and citizens and it may lead to
an increased level of citizen participation in decision-
making (Johnson & Sieber, 2013). According to the
2014 UN E-government Report, both developed and
developing countries are at the decisive point of em-
bracing the potential role that mobile interaction will
play in people's everyday lives (United Nations E-
government Survey, 2014). In terms of social media,
there is a wealth of information which is already being
interpreted and used by its creators, i.e., the citizens
themselves. This provides a cost-effective way for gov-
ernments to engage with citizens in “e-decision making
and co-creation of service” (United Nations E-
government Survey, 2014). When properly planned and
structured, VGI data allows governments to react to citi-
zens’ values and concerns (Johnson & Sieber, 2013).
As a dataset like this represents a quite abstract or
‘free’ approach as to what can be shared within the
#sharingcph theme, one could imagine that similar
campaigns could be targeted at more specific themes
or places. In such cases, pre-defined hashtags could be
used to getting closer to an auto-categorization of the
images into themes or clusters. A simple example could
be #cphpark with the sub hashtags #like or #dontlike,
which would already classify images into positive or
negative categories.
The concept of facilitated VGI (f-VGI) is a variation
of VGI that may be of interest to planners or others
who wish to get input to a predefined topic or area. F-
VGI is a way to operationalize and focus VGI data into
public participation in planning. As explained by Seeger
(2008), an f-VGI process is facilitated by e.g. a planning
professional, a local organization or government in or-
der to feed VGI into a pre-established planning or de-
sign process (Seeger, 2008). The #sharingcph campaign
can be understood as a form of f-VGI, as it was devel-
oped and promoted by the City of Copenhagen. How-
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 13
ever, as mentioned in the beginning of this paper, the
team behind the campaign did not plan for any particu-
lar way of feeding the data into further processes re-
garding urban planning and design. This ‘freely’ formu-
lated campaign made the dataset both interesting and
challenging to work with because inputs were it not
guided in any particular direction (other than “share
your city”).
The data is characteristic by being differentiated as
each photo with the #sharingcph tag represents an in-
dividual person’s experience somewhere in the city. In
combination with other datasets that e.g. inform on
participants’ geo-social backgrounds, VGI holds poten-
tial as it can seemingly reach a broad audience as well
as new user groups that are often weakly represented
(Seeger, 2008).
In this study we focused particularly on the spatial
references to map out the data while the content of
the photographs and # semantics were used to back-up
the understanding of the visual content. The temporal
aspects of the data were not in focus of this study, but
could be important in other studies when exploring use
of green spaces in different seasons/over time.
VGI data can consist of highly refined, differentiat-
ed, and personal impressions from participants who
share it. The challenge is to find suitable ways to ana-
lyse such data and to evaluate the impact of different
levels of facilitation or steering of processes where VGI
feeds in and ways to harness VGI data in combination
with other datasets. VGI data could be corroborated by
other established data collection methods such as sur-
veys and interviews to create a more robust data set
(Pastur et al., 2016). The distinct data sources can
complement each other as the VGI has valuable
strengths in that it provides large, spatially referenced
and unbiased information, i.e., no potential bias trig-
gered by interviewers (Pastur et al., 2016). Currently, in
the US, younger age groups tend to use Instagram the
most (Duggan, Ellison, Lampe, Lenhart, & Madden,
2015). Therefore, while not yet representative of the
general public, this highlights a potential strength, as it
is often difficult to involve this demographic group in
governmental decision-making processes.
This study coincides with previous research on VGI
and suggests that planners can use this data to: 1) un-
cover already present CES patterns, 2) plan and moni-
tor future changes, 3) aid in the management and pri-
oritisation of green spaces and, 4) establish efficient
and effective communication with citizens (Casalegno
et al., 2013; Johnson & Sieber, 2013; Pastur et al.,
2016; Tasse & Hong, 2014).
4.2. Limitations
The use of geo-referenced, freely available social me-
dia data as a proxy for studying spatial trends in dis-
tinct fields is a rapidly growing and interesting field
(Casalegno et al., 2013; Leetaru et al., 2013; Pastur et
al., 2016; Tasse & Hong, 2014; Tuhus-Dubrow, 2014).
However, questions relating to the limitations and reli-
ability of this data source cannot be ignored.
This study focused on identifying spatial patterns in
the data and did not aim to study individual users.
Thus, because these data are publicly available, the is-
sue of anonymity and privacy arises. It was also consid-
ered that some users might not be aware of the privacy
settings or the sharing of their locations; thus, neither
specific user data, nor analyses of such were generat-
ed. As no information was gathered concerning the us-
ers, no socio-economic data exist, which makes it diffi-
cult to assess representability in detailwhich is a
limitation of this study.
Currently, the demographic limitations of the data
are quite noticeable and as such, it is not entirely rep-
resentative of a population. As previously mentioned,
in Denmark, only 24% of the population had an Insta-
gram account in 2014 (Wijas-Jensen, 2014). Additional-
ly, when working with VGI, it is wise to bear in mind
that empirical research often involves ‘participation in-
equality’ with some participants contributing far more
than others (Haklay, 2013). Areas with higher popula-
tion densities or greater levels of outdoor activity also
reflect higher geographical citizen science participation
(Haklay, 2013). Tech-savvy and higher income groups
are generally over-represented (Damiano et al., 2015).
So while the demographics of social media and VGI
participation are currently skewed, it is expected that
with the predictable increase in the use of smartphones
and social media, this medium of communication will
indeed become more popular and more inclusive
(Damiano et al., 2015).
State of the art and software have a high spatial re-
liability (Leetaru et al., 2013). However, sometimes
there is a discrepancy between the location where an
image was taken and where it was uploaded. This may
be explained by people taking pictures, but waiting un-
til later to upload and share them (Damiano et al.,
2015) or perhaps their devices not being able to upload
immediately due to, e.g. network problems. Unfortu-
nately, no specific studies were found that analysed
this problem. To counter this issue, the categorisation
contained a map that showed the location of the imag-
es. This issue was seldom isolated in our dataset. How-
ever, regarding specific spatial analyses in this study,
clusters of noticeable erroneous Instagram images with
upload location errors that would skew the data were
individually analysed and if needed omitted (see
hotspot methodology for further information).
The data quality and locational accuracy of VGI for
CES representation must be analysed and improved
upon. This includes identifying location upload errors,
such as identical, numerous uploads from an indoor lo-
cation (see methodology section) or distance differ-
ence (i.e., range) of image to an actual feature. For ex-
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 14
ample, this research made sure to view each image in a
detailed content and spatial context such as 'green im-
ages located within green spaces' to ensure that only
nature images were included. However, for the general
population this was not implemented as there were
not spatial analysis based on the population, N, and er-
rors are to be expected. Albeit, the locational accuracy
is an issue in the field of georeferenced image analysis.
Solutions such as “integrating the (actual) location into
the image assignment” (i.e., computing the distance
difference) have been mentioned (Sun, Fan, Bakillah, &
Zipf, 2013). For now, concepts such as meticulous ob-
servation of images, a sample tests to find error fac-
tors, clear categorization and boundary setting are ini-
tial solutions to overcome location errors. Where
possible, buffers were included to partly compensate
for some of the distance range discrepancy.
This data set was obtained solely based on the
#sharingcph hashtag which, as previously described,
was promoted by the City of Copenhagen as a part of
its EU Green City campaign. This specific selection facil-
itated a focus on urban planning due to the city sharing
motivation behind the campaign and hashtag. Fur-
thermore, this study aimed to incorporate the e-
governance and urban planning potential for cities
from the outset. As such, the #sharingcph hashtag
combined both the city planner’s involvement, i.e.,
promotion of the hashtag, and the VGI aspect. Howev-
er, the analysis focused on urban nature CESs so the
hashtag of choice could have been simply #nature, or
another related hashtag. Undoubtedly, scaling-up the
hashtag to include more general terms would give this
study a distinct focus, and it would also provide a large
data set with interesting potential for CES analysis.
5. Conclusion
This explorative study shows that urban nature is in-
deed shared in a city, with 34% of shared images of the
city representing urban nature. Additionally, the use of
social media VGI to obtain this information and spatial
knowledge of the city is a field that is currently grow-
ing; this study provides input to this research area.
As the name implies, an important feature of VGI
data is its spatial content which when adequately ana-
lysed can provide trends and patterns about a city; in
this study its urban nature. This rich data source, ob-
tained directly from citizens, can be analysed to identi-
fy shared urban nature spatial trends and patterns. The
results reveal specific behaviour in this data, i.e.,
hotspots and centrally based radial dispersion
throughout the city. Additionally, 44.4% of the general
images were taken at managed green spaces and ur-
ban nature images show a 63.6% alignment with these
green spaces. The spatial tendencies of this data coin-
cide with the official green spaces, yet there exists
shared urban nature images, 36.4%, that are found in
non-official green spaces. This study shows that the spa-
tial patterns of VGI data are valuable and rich in infor-
mation about urban nature and human-environment in-
teractions, yet it is critical to first understand the data’s
spatial distribution in order to make further assump-
tions about its meaning. Urban planners can use urban
nature VGI to promote CES in a city. The data helps to
understand the value and interaction of humans and
nature in a city and may act as a direct conduit for par-
ticipation and communication between citizens and
government.
Finally, as this data-set is very context specific, we
would like to stress the importance of conducting fu-
ture studies which attempt to determine what moti-
vate Instagram users share urban nature images. This
would include identifying the specific qualities that
lead to the sharing of specific urban nature images (i.e.,
park accessibility, design configuration, presence of
water, etc.), which is key in order to be able to utilise
this source of information in city planning and govern-
ance. While some research exists regarding motiva-
tions and psychological reasons as to why people
share, i.e., in order to share a personal cause, further
research is needed in this area to determine, e.g. why a
certain park feature has been shared, the significance
of time availability or the novelty of urban nature with
regards to picture sharing.
Acknowledgements
The authors would like to thank Dr. Reinhard Böcker
for his guidance, the City of Copenhagen’s Sharing Co-
penhagen office for their collaboration and the thou-
sands of Instagrammers who provided the data for this
research. This project received funding from GREEN
SURGE, EU collaborative project, FP7-ENV.2013.6.2-5-
603567.
Conflicts of Interest
The authors declare no conflict of interests.
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About the Authors
Paulina Guerrero has a double-degree master's in Environmental Science from the University of Co-
penhagen and the University of Hohenheim, Germany. She is currently working on her PhD at the
University of Hannover in the EU funded PlanSmart Project analyzing ecosystem services and solu-
tions for urban water challenges. She is interested in studying human-nature interactions through
spatial modeling and citizen science participation.
Urban Planning, 2016, Volume 1, Issue 2, Pages 1-17 17
Maja Steen Møller has a master’s degree in Landscape Architecture from the University of Copenha-
gen. She currently holds a position as PhD student within the EU FP7 Project GREEN SURGE based at
University of Copenhagen. Her research interests include urban green space governance and the use
of digital tools to understand i.e. uses, flows and needs and to facilitate participation and collabora-
tion related to preservation and development of urban green/blue commons.
Anton Stahl Olafsson is Assistant Professor, PhD at the University of Copenhagen with a MSc in Ge-
ography. His research interests include GIS and spatial analyses in relation to sustainable planning
and management of cities, landscapes, green infrastructure, outdoor recreation, and non-motorised
transport. Socio-environmental issues or the linkages between people, society, environment and na-
ture are the core focus of his research. At the moment he acts as project manager in the EU collabo-
rative project GREEN SURGE.
Bernhard Snizek holds a PhD in active transport modeling from the University of Copenhagen and is
the CEO of Copenhagen-based metascapes.org.
... These clusters were compared between all four datasets by the number of observations and the spatial dispersion. Guerrero et al. [25] also analyzed geolocated images on social media. Their strategy was to simplify the complexity of the images by first categorizing them and subsequently applying a hotspot analysis and a distance analysis (measured distance to the city center). ...
... Another gap that we identified is quick assessment of the participants' contributions, which bears the opportunity to provide immediate feedback to each participant. Many authors admitted that some of their analyses required long computation times and were not aimed at a technical implementation in the tool ( [25,32]). By providing a quick assessment, the quality of the participatory planning process will be enhanced by educating citizens in planning and GIS ( [27,28]). ...
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Context Although there is a need to develop a spatially explicit methodological approach that addresses the social importance of cultural ecosystem services for regional planning, few studies have analysed the spatial distribution on the cultural ecosystem services based on social perceptions. Objective The main objective of this study was to identify cultural ecosystem service hot-spots, and factors that characterize such hot-spots and define the spatial associations between cultural ecosystem services in Southern Patagonia (Argentina). Methods The study was carried out in Southern Patagonia (243.9 thousand km2) located between 46° and 55° SL with the Andes mountains on the western fringe and the Atlantic Ocean on the eastern fringe of the study area. The study region has a range of different vegetation types (grasslands, shrub-lands, peat-lands and forests) though the cold arid steppe is the main vegetation type. We used geo-tagged digital images that local people and visitors posted in the Panoramio web platform to identify hot-spots of four cultural ecosystem services (aesthetic value, existence value, recreation and local identity) and relate these hot-spots with social and biophysical landscape features. Results Aesthetic value was the main cultural service tagged by people, followed by the existence value for biodiversity conservation, followed by local identity and then recreational activity. The spatial distribution of these cultural ecosystem services are associated with different social and biophysical characteristics, such as the presence of water bodies, vegetation types, marine and terrestrial fauna, protected areas, urbanization, accessibility and tourism offer. The most important factors are the presence of water in Santa Cruz and tourism offer in Tierra del Fuego. Conclusions Our results demonstrate that this methodology is useful for assessing cultural ecosystem services at the regional scale, especially in areas with low data availability and field accessibility, such as Southern Patagonia. We also identify new research challenges that can be addressed in cultural ecosystem services research through the use of this method.