TY - JOUR
T1 - Automated thinning of road networks and road labels for multiscale design of the National Map of the United States
AU - Brewer, Cynthia A.
AU - Stanislawski, Lawrence V.
AU - Buttenfield, Barbara P.
AU - Sparks, Kevin A.
AU - McGilloway, Jason
AU - Howard, Michael A.
N1 - Funding Information:
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.
PY - 2013/9/1
Y1 - 2013/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84882979291&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882979291&partnerID=8YFLogxK
U2 - 10.1080/15230406.2013.799735
DO - 10.1080/15230406.2013.799735
M3 - Article
AN - SCOPUS:84882979291
SN - 1523-0406
VL - 40
SP - 259
EP - 270
JO - Cartography and Geographic Information Science
JF - Cartography and Geographic Information Science
IS - 4
ER -