Citizen Scientists together with an increasing access to technology provide large datasets that can be used to study e.g. ecology and biodiversity. Unknown and varying sampling effort is a major issue when making inference based on citizen science data. In this paper we propose a modeling approach for accounting for variation in sampling effort due to accessibility. The paper is based on an illustrative case study using citizen science data of moose occurrence in Hedmark, Norway. The aim is to make inference about the importance of two geographical properties known to influence moose occurrence; terrain ruggedness index and solar radiation. Explanatory analysis show that moose occurrences are overrepresented close to roads, and we use distance to roads as a proxy for accessibility. We propose a model based on a Bayesian Log-Gaussian Cox Process specification for occurrence. The model accounts for accessibility through two functional forms. This approach can be seen as a thinning process where probability of thinning, i.e. not observing, increases with increasing distances. For the moose case study distance to roads are used. Computationally efficient full Bayesian inference is performed using the Integrated Nested Laplace Approximation and the Stochastic Partial Differential Equation approach for spatial modeling. The proposed model as well as the consequences of not accounting for varying sampling effort due to accessibility are studied through a simulation study based on the case study. Considerable biases are found in estimates for the effect of radiation on moose occurrence when accessibility is not considered in the model.

公民科学家以及越来越多的技术获取者提供了可用于研究例如生态和生物多样性的大型数据集。在基于公民科学数据进行推理时，未知而变化的采样工作是一个主要问题。在本文中，我们提出了一种建模方法来解决由于可访问性导致的采样工作量的变化。本文基于一个示例性案例研究，该案例使用了挪威Hedmark的麋鹿发生的公民科学数据。目的是推断已知影响驼鹿发生的两个地理属性的重要性；地形坚固性指数和太阳辐射。解释性分析表明，在道路附近驼鹿的发生率过高，并且我们以到道路的距离作为可访问性的代理。我们提出了一个基于贝叶斯对数-高斯考克斯过程规范的模型。该模型通过两种功能形式说明了可访问性。这种方法可以看作是细化过程，其中细化（即未观察到）的概率随距离的增加而增加。对于驼鹿案例，使用到道路的距离。使用集成的嵌套拉普拉斯逼近法和随机偏微分方程方法进行空间建模的计算效率高，完整的贝叶斯推理。通过基于案例研究的模拟研究，研究了所提出的模型以及由于可访问性而没有考虑到变化的抽样工作的后果。