The ubiquitous use of the internet and social media has provided social and spatial scientists with a wealth of data from which inferences about landscape preferences can be gained. These data are increasingly being used as an alternative to data collected from surveys of recreationists. While the rapidly growing body of research using social media is impressive, little work has been done to compare the image content of social media to preferences elucidated via more traditional methods. We compare the landscape features derived through a computer vision algorithm used to analyze social media photographs with preferences derived through a traditional on-site intercept survey. We found that landscape features identified through the computer vision algorithm were, by and large, significantly different compared to landscape features that park users said improved their recreational experiences. Additionally, we did not find substantial differences in landscape preferences between visitors who share photographs of their park visit on social media and those who do not. We suggest a diversity of data sources and analytical methods should be used in a complementary and comparative way. Our analysis here suggests both surveys and social media images can provide important insights about landscape preferences, but neither in isolation is perfect.

互联网和社交媒体的普遍使用为社会和空间科学家提供了丰富的数据，从中可以推断出景观偏好。这些数据越来越多地被用作从娱乐者调查中收集的数据的替代方案。虽然使用社交媒体的快速增长的研究令人印象深刻，但很少有工作将社交媒体的图像内容与通过更传统方法阐明的偏好进行比较。我们将通过用于分析社交媒体照片的计算机视觉算法得出的景观特征与通过传统现场拦截调查得出的偏好进行比较。我们发现通过计算机视觉算法识别的景观特征大体上是 与公园用户所说的改善了他们的娱乐体验的景观特征相比，明显不同。此外，我们没有发现在社交媒体上分享公园参观照片的游客与不分享公园照片的游客之间的景观偏好存在显着差异。我们建议应以互补和比较的方式使用多种数据来源和分析方法。我们在此的分析表明，调查和社交媒体图像都可以提供有关景观偏好的重要见解，但两者都不是孤立的。我们建议应以互补和比较的方式使用多种数据来源和分析方法。我们在此的分析表明，调查和社交媒体图像都可以提供有关景观偏好的重要见解，但两者都不是孤立的。我们建议应以互补和比较的方式使用多种数据来源和分析方法。我们在此的分析表明，调查和社交媒体图像都可以提供有关景观偏好的重要见解，但两者都不是孤立的。