The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Using social media to quantify nature-based tourism and recreation
Open accessAbstract
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.
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Humans and Nature: How Knowing and Experiencing Nature Affect Well-Being
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Application of Tracking Technologies to the Study of Pedestrian Spatial Behavior∗
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Nature and mental health: An ecosystem service perspective
Bratman, G. N., Anderson, C. B., Berman, M. G., Cochran, B., de Vries, S., Flanders, J., Folke, C., Frumkin, H., Gross, J. J., Hartig, T., et al. (2019) · Science Advances · doi:10.1126/sciadv.aax0903 · Cited by 2,001
Nature Contact and Human Health: A Research Agenda
Frumkin, H., Bratman, G. N., Breslow, S. J., Cochran, B., Kahn Jr, P. H., Lawler, J. J., Levin, P. S., Tandon, P. S., Varanasi, U., Wolf, K. L., et al. (2017) · Environmental Health Perspectives · doi:10.1289/ehp1663 · Cited by 1,237
“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).”
BackgroundSelf-citationWhat can big data and text analytics tell us about hotel guest experience and satisfaction?
Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2014.10.013 · Cited by 953
Geospatial big data handling theory and methods: A review and research challenges
Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., et al. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.10.012 · Cited by 749
Advancing Urban Ecology toward a Science of Cities
McPhearson, T., Pickett, S. T. A., Grimm, N. B., Niemelä, J., Alberti, M., Elmqvist, T., Weber, C., Haase, D., Breuste, J., & Qureshi, S. (2016) · BioScience · doi:10.1093/biosci/biw002 · Cited by 731
Metadata
- Authors
- Wood, S. A., Guerry, A. D., Silver, J. M., & Lacayo, M.
- Venue
- Scientific Reports
- Published
- 2013-10-17
- Volume
- 3
- Issue
- 1
- Pages
- 2976-2976
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2084296691
- Cited by
- 720
- References
- 15
- FWCI
- 62.17
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
