Academic journal article Cartography and Geographic Information Science

Automated Thinning of Road Networks and Road Labels for Multiscale Design of the National Map of the United States

Academic journal article Cartography and Geographic Information Science

Automated Thinning of Road Networks and Road Labels for Multiscale Design of the National Map of the United States

Article excerpt

Introduction

Cartographic generalization uses a variety of methods to reduce map content and detail in a manner that legibly portrays desired features and conditions at a reduced map scale (Regnauld and McMaster 2007; Roth, Brewer, and Stryker 2011). Recent road generalization work within the scale range of 1:24,000 (24K) to 1:1,000,000 (1M) indicates that midrange scales (such as 1:100,000 to 1:300,000 (300K) are particularly difficult to represent through common display-based strategies, which eliminate road categories and use simpler, thinner line symbols for road representations (Brewer and Buttenfield 2010). This paper describes some recent efforts to improve road elimination and generalization when producing high-quality cartographic displays for multiscale topographic maps to be distributed through The National Map of the United States. Fundamental to this work is a general goal of fully automated and database-driven multiscale cartography; it is not an option for USGS cartographers to hand-select a few streets that run through the middle of each town to improve each of their maps, when the challenge is to map the whole country with limited staff and continual updates to national databases.

Various methods to thin road or hydrographic networks have been developed to support cartographic generalization, and research continues to refine these methods (Thomson and Brooks 2007; Stanislawski 2009; Savino et al. 2010; Touya 2010; Savino et al. 2011; Buttenfield, Stanislawski, and Brewer 2011; Gulgen and Gokgoz 2011). Invariably, the methods rely on database enrichment to establish selection criteria that quantify one or more characteristics deemed important to the thinning strategy. For instance, enrichment to define paths of best continuation (i.e., "strokes") or river courses have been used to establish selection hierarchies for network features (Thomson and Richardson 1999; Chaudhry and Mackaness 2005; Thomson and Brooks 2007; Touya 2007, 2010; Savino et al. 2011). Zhou and Li (2011) evaluated several stroke-building strategies. Using graph traversal techniques, hydrographic networks have been enriched with stream order, watershed area, or upstream drainage area values, which is subsequently applied for network thinning (Ai, Liu, and Chen 2006; Stanislawski 2009; Savino et al. 2011; Gutman 2012). Network thinning operations may be enabled or enhanced through enrichment computations of local line density (Stanislawski 2009; Buttenfield, Stanislawski, and Brewer 2011; Savino et al. 2011; Stanislawski et al. 2012), pattern (Heinzle, Anders, and Sester 2005; Heinzle, Anders, and Sester 2007; Touya 2007; Savino, Rumor, and Zanon 2011), and road network or block structure (Jiang and Claramunt 2004; Touya 2010; Gulgen and Gokgoz 2011).

The Thin Road Network tool is a relatively new generalization tool offered by Esri (Punt and Watkins 2010; Briat, Monnot, and Punt 2011; ArcGIS Resources 2012a). The tool applies a simulated annealing algorithm to filter road features based on the relative importance, significance, and density of the input features. Relative importance is determined from a hierarchical road classification field (e.g., Interstate, State Route, local road) that should be assigned for the features being processed. Significance is affected by the connectivity of the road network being processed, and is estimated from the number and length of possible itineraries in which each feature participates. An itinerary refers to alternative sequences of road segments one can use to traverse the road network efficiently.

A run of the tool populates a binary invisibility field to mark less significant road features that should not be displayed in the thinned network, while retaining visible features which maintain network connectivity, navigability, and the general morphology of road patterns. The degree of thinning is controlled by a minimum length parameter, which is an estimate of the shortest segment that is visually sensible to show for the desired scale. …

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