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. 2017 Dec 14;7(1):17615.
doi: 10.1038/s41598-017-18007-4.

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas

Henrikki Tenkanen  1 , Enrico Di Minin  2   3 , Vuokko Heikinheimo  2 , Anna Hausmann  2 , Marna Herbst  4 , Liisa Kajala  5 , Tuuli Toivonen  6
Affiliations

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas

Henrikki Tenkanen et al. Sci Rep. .

Abstract

Social media data is increasingly used as a proxy for human activity in different environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media-based monthly visitation patterns match relatively well with the official visitor counts. However, there were considerable differences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identified potential reasons why social media data and visitor statistics might not match: the geography and profile of the park, the visitor profile, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
The locations of the national parks in Finland (a) and South Africa (b) where the size of the circle indicates the number of tourists visiting the given park in 2014. Numbered locations show the five most visited national parks in Finland and in South Africa. The photos show examples of the differences in the type of nature-based tourism between the two countries: a more charismatic wildlife and landscape viewing focus in South Africa, and a higher variety (e.g. landscape seasonality, activities) of attractions in Finland. Photo credits: Anna Hausmann, Tuuli Toivonen, Henrikki Tenkanen. Figure has been created with Matplotlib v2.02, Geoplot v0.0.3 and Geopandas v0.2.1 modules in Python 3.5.3 programming language (https://www.python.org/) under the PSF License (docs.python.org/3/license.html) using openly available World Borders Dataset (unmodified) from http://thematicmapping.org/downloads/world_borders.php under Creative Commons BY-SA licence (https://creativecommons.org/licenses/by-sa/3.0/).
Figure 2
Figure 2
Park popularity comparisons between official visitors and social media data from different platforms reveal that in general social media data matches well with the park popularity having Spearman’s rank correlation 0.75 in South Africa and 0.84 in Finland. There are differences between platforms: e.g. in South Africa Instagram predicts the 4 most popular parks correctly whereas Twitter only 2. Value 1 corresponds to the most visited park based on official statistics (x-axis) and social media (y-axis), and values 21 (South-Africa) and 35 (Finland) correspond to the least visited park accordingly. Figure has been created with Matplotlib v2.02 and Pandas v0.19.2 modules in Python 3.5.3 programming language (https://www.python.org/) under the PSF License (docs.python.org/3/license.html).
Figure 3
Figure 3
Comparison between official visitor statistics and social media data in 21 national parks in South Africa. The lines of the individual platforms show that Flickr is always the least significant in terms of volume of the data, whereas Instagram is often the most data-rich platform. Figure has been created with Matplotlib v2.02 and Pandas v0.19.2 modules in Python 3.5.3 programming language (https://www.python.org/) under the PSF License (docs.python.org/3/license.html).
Figure 4
Figure 4
Comparison between official visitor statistics and social media data in 35 national parks in Finland. The plots (in Figs 3 and 4) are in a descending order based on the number of visitors in the parks. Plots reveal that social media data tend to perform in a robust manner in the more visited parks, whereas in the least popular parks the patterns differ significantly. Figure has been created with Matplotlib v2.02 and Pandas v0.19.2 modules in Python 3.5 programming language (https://www.python.org/) under the PSF License (docs.python.org/3/license.html).
Figure 5
Figure 5
Boxplots reveal that Instagram performs best in estimating the monthly visitors when measured with Pearson’s correlation coefficients between official visitor statistics and social media user-days. The performance of Instagram is slightly better and more robust in South Africa than in Finland, having a 70% median correlation. This figure is based on parks (N = 36) where the data is not temporally autocorrelated. Result with all parks is presented in Supplement S2. Figure has been created with Matplotlib v2.02 and Pandas v0.19.2 modules in Python 3.5.3 programming language (https://www.python.org/) under the PSF License (docs.python.org/3/license.html).
Figure 6
Figure 6
Social media data tend to work better in more visited parks (top row) and in parks having higher number of social media user-days (bottom row) which is shown here by comparing the Pearson correlation coefficients (between social media and official visitor statistics, see Figs 3 and 4) against log-transformed number of official visitors and social media user-days. This trend is visible particularly with Instagram data and all platforms combined, and weaker or non-existing with Twitter and Flickr. This figure is based on parks (N = 36) where the data is not temporally autocorrelated. Result with all parks is presented in Supplement S2. Figure has been created with Matplotlib v2.02 and Pandas v0.19.2 modules in Python 3.5 programming language (www.python.org/) under the PSF License (docs.python.org/3/license.html).
Figure 7
Figure 7
Conceptualisation of the potential reasons explaining the differences between the social media user-days and the official visitor statistics. We identified a number of park-specific reasons in stakeholder workshops (one in Finland and two in South Africa). Here we classified them into four main categories that could help to explain why the patterns derived from social media does not always match well the official visitor statistics. Figure was created in CorelDRAW Graphics Suite version X8 (www.coreldraw.com/en/). Illustrations were drawn by Tuuli Toivonen.
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