We propose a method which uses Flickr tags to predict a wide variety of environmental features, such as climate data, land cover categories, species occurrence, and human assessments of scenicness. The role of Flickr tags in our method is two-fold. First, we show that Flickr tags capture information which is highly complementary to what is found in traditional structured environmental datasets. By combining Flickr tags with traditional datasets, we can thus make more accurate predictions than is possible using either Flickr tags or traditional datasets alone. Second, we propose a collective prediction model which crucially relies on Flickr tags to define a neighbourhood structure. The use of a collective prediction formulation is motivated by the fact that most environmental features are strongly spatially autocorrelated. While this suggests that geographic distance should play a key role in determining neighbourhoods, we show that considerable gains can be made by additionally taking Flickr tags and traditional data into consideration.

中文翻译：

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从社交媒体预测环境：一种集体分类方法

我们提出了一种使用 Flickr 标签来预测各种环境特征的方法，例如气候数据、土地覆盖类别、物种出现和人类对风景的评估。Flickr 标签在我们的方法中的作用是双重的。首先，我们展示了 Flickr 标签捕获的信息与传统结构化环境数据集中的信息高度互补。通过将 Flickr 标签与传统数据集相结合，我们可以做出比单独使用 Flickr 标签或传统数据集更准确的预测。其次，我们提出了一个集体预测模型，该模型主要依赖 Flickr 标签来定义邻域结构。使用集体预测公式的动机是大多数环境特征在空间上具有很强的自相关性。