Using social media to quantify nature-based tourism and recreation

By Spencer A. Wood, A. Guerry, J. Silver, Martin Lacayo. Scientific Reports, 2013

687 citations recorded by the source corpus.

Scientists have traditionally studied recreation in nature by conducting surveys at entrances to major attractions such as national parks. This method is expensive and provides limited spatial and temporal coverage. A new source of information is available from online social media websites such as flickr. Here, we test whether this source of “big data” can be used to approximate visitation rates. We use the locations of photographs in flickr to estimate visitation rates at 836 recreational sites around the world and use information from the profiles of the photographers to derive travelers' origins. We compare these estimates to empirical data at each site and conclude that the crowd-sourced information can indeed serve as a reliable proxy for empirical visitation rates. This new approach offers opportunities to understand which elements of nature attract people to locations around the globe and whether changes in ecosystems will alter visitation rates.

Subfields: Economics and Econometrics, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/77c15c6bb50572919fc32963a41b144a9949a7f6

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Citations in the corpus

Walk on the Wild Side: Estimating the Global Magnitude of Visits to Protected Areas (2015, background): “Large-scale assessments have been restricted to regional or biome-specific investigations [4–8] (but see [9]).”
Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China (2020, methodology): “Further, with regard to data quality, for example, Wood, Guerry, Silver, and Lacayo (2013) demonstrate that Flickr data is a reliable data source for estimating tourist attraction visit rates, Girardin et al. (2008) compare Flickr and actual ticket data to show that Flickr data can be used as an…”
Nature Contact and Human Health: A Research Agenda (2017, background): “Similarly, social media data can help quantify visits to natural areas and behavior patterns within those areas (Sessions et al. 2016; Wood et al. 2013).”
The Geography and Importance of Localness in Geotagged Social Media (2016, background): “, [38,44,49]) now regularly use social media VGI to better understand phenomena of interest ranging from social unrest and emergencies [6,24,43] to disease tracking [22,27,42].”
Mapping the global value and distribution of coral reef tourism (2017, background): “Known as photo-user-days (PUD), these represent the total number of days, across all users, that at least one photograph was taken in a given area [44].”
Geolocated social media as a rapid indicator of park visitation and equitable park access (2018, background): “On-site counts from a subset of parks in NYC suggest that Flickr and Twitter postings are reliable indicators of visitation (approximately 58–76% correlated with a small sample of on-site visitor counts) as concluded by other studies (e.g., Dunkel, 2015; Keeler et al., 2015; Sessions et al., 2016; W”
Benefit relevant indicators: Ecosystem services measures that link ecological and social outcomes (2018, positive): “Direct engagement and outreach with communities and community groups, along with social media and surveys, can be used to identify and determine the size of affected communities (Reed, 2008; Bright et al., 2003; Wood et al., 2013).”
When, Where, and How Nature Matters for Ecosystem Services: Challenges for the Next Generation of Ecosystem Service Models (2017, background): “Social media likewise opens up new avenues for data mining to geolocate ES use or beneficiaries (Wood et al. 2013, Sonter et al. 2016).”
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “With the advent of big data, other branches of geography have also shown great interest in utilizing big data for addressing social (human geography) and environmental (physical geography) questions (e.g., Crampton et al. 2013, Goodchild 2013, Kitchin 2013, Steed et al. 2013, Wood et al. 2013).”