Recreational use in dispersed public lands measured using social media data and on-site counts

Fisher, David M.; Wood, Spencer A.; White, Eric M.; Blahna, Dale J.; Lange, Sarah; Weinberg, Alex Benjamin; Tomco, Michael; Lia, Emilia

doi:10.1016/j.jenvman.2018.05.045

Cited by 88 publications

(56 citation statements)

References 31 publications

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“…Traditional approaches rely on visitor intercept surveys and physical technology, such as tube or infrared counters. Based on the results described here and in other research 11,12,17 , we conclude that social media data and related visitation models hold promise for estimating recreation use, especially for large recreation site complexes or collections of recreation resources (e.g., multiple trails within a wilderness area) that are otherwise difficult to monitor. Additionally, social media and other volunteered geographic information provide an opportunity for managers to demonstrate that their recreation monitoring approach is current, practical, and up-to-date with technology.…”

Section: Practical Guidance and Implications For Managerssupporting

confidence: 64%

“…During the study period, we also continued data collection in WWA in the four ranger districts (Mt. Baker, Darrington, Skykomish, and Snoqualmie Ranger Districts) of our existing study area in the MBSNF 17 (Figure 1).…”

Section: Public Land Unitsmentioning

confidence: 99%

“…At each site we installed either a passive infrared pedestrian counter (38 sites) or a time-lapse video camera (4 sites). As in 17 the counters were installed at the first narrow point in a trail and hidden in trees or rocks to capture normal visitor behavior. A recreation count with a date and time stamp was recorded each time the infrared beam was broken.…”

Section: On-site Estimatesmentioning

confidence: 99%

“…Studies spanning an impressive diversity of developed and undeveloped settings have concluded that the popularity of parks is generally mirrored in the popularity of the same destinations on individual social media platforms such as Flickr, Instagram, Sina Weibo, and Twitter [9][10][11][12][13][14][15] (but see 16 ). The consensus emerging from these correlational studies is that data from social media have potential to inform estimates of absolute visitation at specific destinations and for multiple time periods [17][18][19] . To the extent that social media offer additional information across geographic or temporal scales, these data may complement traditional on-site counts in parks and protected areas.…”

Section: Introductionmentioning

confidence: 99%

“…This may be due to differences in who uses various platforms 21,22 , how users share content 23,24 , and changes in the popularity of platforms over time 15,25,26 . Some studies suggest that these issues may be partly overcome by combining data from multiple social media platforms 4,9,12,17,20,27 . However, data quality and interpretability varies across users and service providers 28 , as do restrictions on which, when, and how data can be accessed 29 .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Wood

Winder

Lia

et al. 2020

Preprint

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Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

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Section: Practical Guidance and Implications For Managerssupporting

confidence: 64%

Section: Public Land Unitsmentioning

confidence: 99%

Section: On-site Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Wood

Winder

Lia

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

et al. 2021

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Data‐driven predictions of summertime visits to lakes across 17 US states

et al. 2023

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Using a dataset of more than 51,000 US lakes, we estimated the relationship between summertime lake visits, lake water clarity, landscape features, and other amenities, where visits were estimated with counts of geo-located photographs. Given the size and complexity of our dataset, we used a combination of machine learning techniques, imputation techniques, and a Poisson count model to estimate these relationships. We found that every additional meter of average summertime Secchi depth was associated with at least 7% more summertime lake visits, all else equal. Second, we found that lake amenities, such as beaches, boat launches, and public toilets, were more powerful predictors of visits than water clarity. Third, we found that visits to a lake were strongly influenced by the lake's accessibility and its distance to nearby lakes and the amenities the nearby lakes offered. Our research highlights the need for (1) a better understanding of how representative social media data are of actual recreational behavior, (2) the development of best practices to account for nonrandom patterns in missing natural feature data, and (3) a better understanding of the potential endogeneity in the lake visit-water quality relationships.

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Recreational use in dispersed public lands measured using social media data and on-site counts

Cited by 88 publications

References 31 publications

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

Data‐driven predictions of summertime visits to lakes across 17 US states

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