Conductivity is an important indicator of the health of aquatic ecosystems. We model large amounts of lake conductivity data collected as part of the United States Environmental Protection Agency’s National Lakes Assessment using spatial indexing, a flexible and efficient approach to fitting spatial statistical models to big data sets. Spatial indexing is capable of accommodating various spatial covariance structures as well as features like random effects, geometric anisotropy, partition factors, and non-Euclidean topologies. We use spatial indexing to compare lake conductivity models and show that calcium oxide rock content, crop production, human development, precipitation, and temperature are strongly related to lake conductivity. We use this model to predict lake conductivity at hundreds of thousands of lakes distributed throughout the contiguous United States. We find that lake conductivity models fit using spatial indexing are nearly identical to lake conductivity models fit using traditional methods but are nearly 50 times faster (sample size 3,311). Spatial indexing is readily available in the spmodel R package.

电导率是水生生态系统健康状况的重要指标。我们使用空间索引对作为美国环境保护署国家湖泊评估的一部分收集的大量湖泊电导率数据进行建模，空间索引是一种将空间统计模型拟合到大数据集的灵活而有效的方法。空间索引能够适应各种空间协方差结构以及随机效应、几何各向异性、分区因子和非欧几里得拓扑等特征。我们使用空间索引来比较湖泊电导率模型，结果表明氧化钙岩石含量、农作物产量、人类发展、降水和温度与湖泊电导率密切相关。我们使用该模型来预测分布在美国本土的数十万个湖泊的湖泊电导率。我们发现使用空间索引拟合的湖泊电导率模型与使用传统方法拟合的湖泊电导率模型几乎相同，但速度快了近 50 倍（样本大小 3,311）。spmodel R包中可以轻松使用空间索引。