Up-to-date information about human-nature interactions are urgently needed to inform sustainable land use planning and nature conservation. Large amounts of content-rich geographic data are produced continuously by users of different social media platforms across the globe containing information about the whereabouts and activities of people. Such data, combined with other sources of data, have potential to provide new and useful information about human presence, activities, observations and movements at different spatial and temporal scales. Despite many examples in other fields, location-based social media data have not been widely used in nature conservation. This work aims to understand the potential and biases in geographic social media data in order to inform conservation-related decision making across scales. Main objectives of the work are to 1) extract meaningful patterns related to human-nature interactions in green areas from location-based social media, while 2) understanding the biases and limitations of the data. Firstly, the aim is to position location-based social media data among other sources of user-generated geographic information and to identify the useful elements and limiting factors of using such data in conservation science. Secondly, the aim is to understand who the data represents in order to derive further information about green area users. Lastly, user-generated data is combined and contrasted with other data sources to understand the spatial and temporal patterns of human actions, and potential threats in areas of high conservation value at regional and global scales.
Understanding patterns of human-nature interactions is crucial
for sustainable land use planning and nature conservation
(Venter et al., 2016). However, spatially and temporally
accurate data on threats affecting biodiversity persistence are
lacking
Spatial data generated by non-experts have recently become
a valuable resource both in academia and the society in addition
to more traditional data produced by scientists and other
authorities
Social media, in general, refers to computer-based applications used for networking and sharing digital content. Here, social media data refers to the spatial attributes (location), temporal attributes (time), and relevant content (text, photos, and video) generated by users of different social media conservation-related decision making at different spatial scales, while 2) understanding the biases in location-based social media data. The work includes the development of automated workflows for data processing and analysis, comparisons between user-generated data and official datasets, and accounting for gaps, inaccuracies and bias in the user-generated data at different spatial scales. The main objectives and related questions are the following: • Analyzing the potential and limitations of different social media data for studying human activities in green areas: What kind of information do we get from social media, what biases are included in the data and how is it useful for studying human activities in green areas? How does social media data compare with other information sources from focus areas? • Understanding the park visitors: Whose views and observations are presented in user-generated content from national parks? Where do the visitors come from and how do they move within and between green areas? • Mapping conservation opportunities and threats: Can we characterize national parks and national park visitors based on social media data? Can we identify human pressure on the environment from social media data? What tradeoffs between nature recreation and nature conservation can we discover on a regional/global scale? 2
Data and tools related to the information age
Location-based social media data is often discussed in the context of Volunteered Geographic Information (VGI). The concept VGI, coined by Goodchild in 2007, is widely used to describe geographic datasets generated by non-experts. Vast amounts of spatial data are continuously created in collaborative projects such as the OpenStreetMap, social networks such as Twitter and other location-aware platforms on the web which host user-generated content. However, the term VGI does not fully capture the nature of more spontaneously generated data (See et al., 2016) such as tweets and Flickr photos which have originally been shared for other purposes than mapping and research. These passively shared data evidently require special consideration related to the ethical use of data, and representativeness of the results. Thus, there is a need to further position social media among other sources of user-geographic information and authorative data sources.
Social media data has been used in many application fields of
geography to study spatial phenomena, especially in the urban
context. The study of population dynamics in cities (Longley &
Adnan, 2016; Steiger et al., 2015), spatial diffusion
However, examples in environmental studies, especially in
conservation science are still limited (Di Minin, Tenkanen &
Toivonen, 2015). Studies focusing more on human-nature
interactions include the quantification of visitation rates (Wood
et al., 2013; Levin, N., Kark, S. and Crandall, 2015),
assessment of cultural ecosystem services and people’s
interests (Richards & Friess, 2015; Roberge, 2014), and the
extraction of species data
Existing studies are often limited to only a single social media
platform, and lack comparisons and validation against other
data sources. Most studies using social media data for
environmental studies rely solely on one platform (mostly
Flickr). Flickr might be the most suitable platform when
looking at biodiversity features, while Instagram or other
available data sources might reflect better the activities present
in the area of interest
A critical approach is needed when using social media as a
source of information
The study sites of this research consists of green area networks at different scales; urban green areas from the city of Helsinki, Finland, individual protected areas from Finland and South Africa and the global protected area network and key biodiversity areas worldwide. National parks with regular visitor monitoring schemes provide a test environment for comparing social media user counts and content to official statistics.
Main material for the study consist of openly shared locationbased social media posts from different social media platforms including (but not restricted to) Instagram, Twitter, and Flickr. Data is retrieved from platform APIs using existing packages and custom-made scripts in the Python Programming Language.
Social media data are used in conjunction with, and contrasted to official visitor statistics, surveys and other available data from the focus areas. Official visitor statistics and visitor survey data from national park authorities are used together with social media content from different platforms. Global analysis from projected areas and key biodiversity areas is done using additional data from the International Union for the Conservation of Nature (IUCN), and Birdlife International.
The first part of the study is focused on the potential and limitations of social media data for environmental studies through analyzing the different components of the data in terms of precision, accuracy and fit-for-purpose. Accuracy and precision of spatial and temporal information are assessed trough methods of data exploration and comparisons to ancillary data sets. Manual and automated content analysis of texts and images is used to explore if the content shared from green areas is thematically meaningful for green area management and conservation, and to produce further analyses of the spatial and temporal patterns of observed content categories (for example related to a specific activity or species within a national park). Results are compared with existing information about park visitation from case study sites with existing reference data. For example, activities revealed from social media photo content are compared with surveyed activities in a case study from a Finnish National Park.
Understanding bias in social media data includes the analysis
of age- and language groups as well as the differentiation
between data generated by locals and visitors. Questions related
to visitors’ social media usage are conducted in selected
national parks in Finland in order to find out the proportion of
national park visitors who are active in social media and to link
this information to demographic background variables.
Furthermore, different platforms are compared in terms of their
information content. Based on earlier findings from Kruger
National park in South Africa, Flickr contains more
information related to biodiversity features, whereas Instagram
posts portray more often human activities
The last sections of the work apply the earlier findings for answering questions related to conservation opportunities and threats in the global protected area network and key biodiversity areas using a combination of user-generated and official data sources.
The work has potential to bridge the gap between recent advances in social media data analytics and information needs in conservation science. The work will likely reveal new information about spatial and temporal patterns of human activities in green areas worldwide, especially from areas with no systematic visitor monitoring in place.
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