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 journalArticle

8 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 languageEnglish (US)
Pages (from-to)259-270
Number of pages12
JournalCartography and Geographic Information Science
Volume40
Issue number4
DOIs
StatePublished - Sep 1 2013

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Management of Technology and Innovation

Fingerprint Dive into the research topics of 'Automated thinning of road networks and road labels for multiscale design of the National Map of the United States'. Together they form a unique fingerprint.

  • Cite this