Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data.

Area of Science:

• Environmental Science
• Ecology
• Data Science

Background:

• Conductivity is a crucial metric for assessing aquatic ecosystem health.
• Large datasets from the United States Environmental Protection Agency's National Lakes Assessment require efficient analysis methods.

Purpose of the Study:

• To model lake conductivity using spatial indexing for big data analysis.
• To identify key environmental and anthropogenic factors influencing lake conductivity.
• To compare the efficiency of spatial indexing with traditional methods.

Main Methods:

• Utilized spatial indexing to fit spatial statistical models to extensive lake conductivity data.
• Incorporated various spatial covariance structures, random effects, and other complex features.
• Applied the developed model to predict conductivity across numerous lakes in the contiguous United States.

Main Results:

• Identified strong relationships between lake conductivity and calcium oxide rock content, crop production, human development, precipitation, and temperature.
• Spatial indexing models produced results nearly identical to traditional methods.
• Achieved a significant speed improvement, with spatial indexing being approximately 50 times faster.

Conclusions:

• Spatial indexing is a flexible, efficient, and powerful approach for analyzing big environmental data, specifically lake conductivity.
• The method is readily available in the spmodel R package, facilitating broader application in ecological studies.