Sounding Labels and Scale for Bathymetric Data Generalization in Nautical Cartography

Abstract

This work presents a bathymetric data generalization algorithm based on depth labels rendered at scale. It aims to facilitate the final cartographic sounding selection for chart portrayal through the process referred to as hydrographic sounding selection. Currently, automated algorithms for hydrographic soundings selection rely on radius-and grid-based approaches; however, their outputs contain a dense set of soundings with a significant number of cartographic constraint violations, thus increasing the burden and cost of the subsequent, mostly manual, cartographic sounding selection. As technology improves and bathymetric data are collected at higher resolutions, the need for automated generalization algorithms that respect the constraints of nautical cartography increases, where errors in the hydrographic sounding selection phase are carried over to the final product. Thus, we propose a novel label-based and shoal-biased, generalization algorithm that utilizes the physical dimensions of the symbolized depth values at scale to avoid the over-plot of depth labels. Moreover, we define validation tests for assessing adherence to cartographic constraints for nautical charts, namely functionality, legibility, spatial, and shape. We describe the limitations of current radius-and grid-based approaches with respect to these constraints, and detail our algorithm. Each approach is implemented in Python, and we use our validation tests to compare the results of our approach with the results of current approaches. Utilizing four datasets, it is shown that our label-based generalization performs the best regarding the cartographic constraints of functionality and legibility, and is equal to the other approaches in adhering to the spatial constraint.

Full text

Sounding Labels and Scale for Bathymetric Data Generalization in Nautical Cartography
Noel Dyer1,3, Christos Kastrisios2, Leila De Floriani3
1 Office of Coast Survey, National Oceanic and Atmospheric Administration, Silver Spring, USA
2 Center for Coastal & Ocean Mapping/Joint Hydrographic Center, University of New Hampshire, Durham, USA
3 Department of Geographical Sciences, University of Maryland, College Park, USA
Noel.Dyer@noaa.gov
This work presents a bathymetric data generalization algorithm based on depth labels rendered at
scale. It aims to facilitate the final cartographic sounding selection for chart portrayal through the process
referred to as hydrographic sounding selection. Currently, automated algorithms for hydrographic
soundings selection rely on radius- and grid-based approaches; however, their outputs contain a dense set
of soundings with a significant number of cartographic constraint violations, thus increasing the burden
and cost of the subsequent, mostly manual, cartographic sounding selection. As technology improves and
bathymetric data are collected at higher resolutions, the need for automated generalization algorithms that
respect the constraints of nautical cartography increases, where errors in the hydrographic sounding
selection phase are carried over to the final product. Thus, we propose a novel label-based and shoal-
biased, generalization algorithm that utilizes the physical dimensions of the symbolized depth values at
scale to avoid the over-plot of depth labels. Moreover, we define validation tests for assessing adherence
to cartographic constraints for nautical charts, namely functionality, legibility, spatial, and shape. We
describe the limitations of current radius- and grid-based approaches with respect to these constraints, and
detail our algorithm. Each approach is implemented in Python, and we use our validation tests to compare
the results of our approach with the results of current approaches. Utilizing four datasets, it is shown that
our label-based generalization performs the best regarding the cartographic constraints of functionality
and legibility, and is equal to the other approaches in adhering to the spatial constraint.
Figure: Sounding label distributions of generalization approaches: A) fixed radius, Charleston Harbor; B)
variable radius, Narragansett Bay; C) grid-based, Tampa Bay; and D) label-based, Strait of Juan de Fuca.