Department of Natural Resources and Environmental Management, University of Haifa, Haifa, Israel; Natural Resources and Environmental Research Center, University of Haifa, Israel.
Institute of Geography and Geology, Universität Greifswald, Germany; Department of Strategic Management, Marketing and Tourism, Faculty of Business and Management, University of Innsbruck, Austria.
J Environ Manage. 2020 Jun 1;263:110418. doi: 10.1016/j.jenvman.2020.110418. Epub 2020 Mar 18.
Social media data are increasingly utilised as a low-cost alternative to visitor surveys in characterising nature-based recreation. However, the information available on individual users is limited and typically does not include provenance, restricting the potential applications and impact of the data. Here we investigate a methodology to estimate social media visitors' home locations at various spatial scales and apply it to the entire network of national parks in Germany. We compare predicted visitor provenance to representative onsite survey data and explore group-specific spatial and temporal patterns of recreation as characterised by users' geotagged photographs. Results show that photograph metadata can be used to assign home locations with accuracies between 62 and 89% depending on spatial scale implemented. Said social media-based predictions are reasonably well representative of the surveyed visitor structure in German national parks with Flickr visitor-days composed of 19% local, 62% non-local German and 19% international visits.
社交媒体数据越来越多地被用作描述基于自然的娱乐活动的低成本替代方法，而不是访客调查。然而，关于单个用户的信息是有限的，通常不包括原籍国，这限制了数据的潜在应用和影响。在这里，我们研究了一种在不同空间尺度上估计社交媒体访问者原籍国的方法，并将其应用于德国的整个国家公园网络。我们将预测的访问者原籍国与具有代表性的现场调查数据进行了比较，并通过用户的地理标记照片探索了按用户组划分的特定时空娱乐模式。结果表明，照片元数据可用于分配原籍国，具体的准确率取决于实施的空间尺度，在 62%至 89%之间。基于社交媒体的预测与德国国家公园调查访客结构具有相当的代表性，Flickr 访客日中，19%为本地访问，62%为非本地德国访问，19%为国际访问。
