A Hexagonal Framework to Assess and Visualize Bathymetric Data Coverage

Geospatial inventories and identification of gaps in bathymetric data coverage often rely primarily on the absence or presence of data—if depth measurements exist in a particular location, that area is classified as “mapped”. These assessments typically analyze coverage at a fixed resolution, without considering how characteristics such as depth, density or recency of the source data may change across spatial scales. Consequently, these methods have the potential to misrepresent coverage. Additionally, when applied to the Great Lakes, analysis is usually segregated between Canada and the United States. To help address these shortcomings, the Great Lakes Observing System (GLOS) has developed an innovative method to assess bi-national bathymetric data coverage in the Great Lakes based on hierarchical hexagonal spatial indexing. This approach considers variables such as water depth and data density, resulting in an accurate spatial depiction of bathymetric coverage extensible across multiple spatial scales. This approach is particularly relevant across large areas where the characteristics of data can vary significantly due to different acquisition methods, survey dates and bottom morphologies.