Automated thinning of road networks and road labels for multiscale design of the National Map of the United States

Cynthia A. Brewer; Lawrence V. Stanislawski; Barbara P. Buttenfield; Kevin A. Sparks; Jason McGilloway; Michael A. Howard

doi:10.1080/15230406.2013.799735

Automated thinning of road networks and road labels for multiscale design of the National Map of the United States

Cynthia A. Brewer, Lawrence V. Stanislawski, Barbara P. Buttenfield, Kevin A. Sparks, Jason McGilloway, Michael A. Howard

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

This paper reports on progress in generalization and selective feature removal for a subset of fundamental base map layers that enables competent mapping through scales ranging from 1:24,000 to 1:1,000,000. Thinning and partitioning methods are applied to road features and labels for The National Map of the United States. Roads are thinned adaptively using the ArcGIS Thin Road Network geoprocessing tool, which removes features by feature hierarchy and network connectivity, yet preserves characteristic urban/rural local density patterns that can be lost through simple category removals. The paper describes thinning for label hierarchies within road categories, improved preference in placement for more important road labels, and selective removal of labels through scale. Use of the Radical Law to guide matches between thinning parameters and suitable scales of representation also is shown. Inspection of graphic results of these treatments can help to establish parameters for automated base map design for US topographic mapping.

Original language	English
Pages (from-to)	259-270
Number of pages	12
Journal	Cartography and Geographic Information Science
Volume	40
Issue number	4
DOIs	https://doi.org/10.1080/15230406.2013.799735
State	Published - Sep 1 2013
Externally published	Yes

Funding

Research funding provided by the US Geological Survey’s Center of Excellence for Geospatial Information Science (USGS-CEGIS) through the Department of Interior Cooperative Ecosystem Studies Unit (CESU). The work of Dr. Brewer with Jay McGilloway and Kevin Sparks is funded by grant # 06HQAG0131, “Symbol and Label Design Solutions for Electronic Topographic Mapping for The National Map of the United States.” Paulo Raposo is a primary collaborator on this larger project and we appreciate his advice. The work of Dr. Buttenfield is supported by USGS-CEGIS grant #04121HS029, “Generalization and Data Modeling for New Generation Topographic Mapping.” We appreciate the support of E. Lynn Usery, Director of CEGIS. Thanks to Edie Punt, Marc-Olivier Briat, and David Watkins at Esri for advice on uses of thinning generalization tools and partitioning. The Gould Center in the Department of Geography at Penn State supported our work with computing and facilities.

Keywords

generalization
map design
road labeling
road thinning
topographic mapping

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10.1080/15230406.2013.799735

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abstract = "This paper reports on progress in generalization and selective feature removal for a subset of fundamental base map layers that enables competent mapping through scales ranging from 1:24,000 to 1:1,000,000. Thinning and partitioning methods are applied to road features and labels for The National Map of the United States. Roads are thinned adaptively using the ArcGIS Thin Road Network geoprocessing tool, which removes features by feature hierarchy and network connectivity, yet preserves characteristic urban/rural local density patterns that can be lost through simple category removals. The paper describes thinning for label hierarchies within road categories, improved preference in placement for more important road labels, and selective removal of labels through scale. Use of the Radical Law to guide matches between thinning parameters and suitable scales of representation also is shown. Inspection of graphic results of these treatments can help to establish parameters for automated base map design for US topographic mapping.",

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AU - McGilloway, Jason

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Automated thinning of road networks and road labels for multiscale design of the National Map of the United States

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Keywords

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