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

Using a dataset of more than 51,000 US lakes, we estimated the
relationship between summertime lake visits, lake water quality, landscape
features, and other amenities, where visitation was estimated using counts
of geolocated 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 summer-time Secchi depth was
associated with at least 7% more summer-time 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
quality. 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 non-random patterns in missing natural feature data, and 3) a
better understanding of the potential reverse causality in the lake
visit-water quality relationships.