Establishing Classification and Hierarchy in Populated Place Labeling for Multiscale Mapping for the National Map

Article excerpt

Introduction

Computers and the Internet have an increasing role in the use and production of maps, but we have been alarmed by professionally produced and popular mapping services that leave out seemingly obvious placenames, such as omitting Pittsburgh and Philadelphia on a map of Pennsylvania while many smaller towns are named. Online maps are viewed using a variety of platforms, with varied combinations of features, and at multiple scales on demand. Thus, decisions about what labels to display need to be made dynamically and automatically. As more and more information becomes available for mapping purposes, a production challenge is to incorporate socio-economic attributes at the design stage in order to create better digital maps. For example, we want to have enough data considered by labeling algorithms so that smaller and less significant places are omitted before major cities when some labels are necessarily crowded out at smaller scales. Conversely, there is a potential danger of information overload (or slow processing) when additional data is used to create an overly complicated algorithm when a simple but elegant solution would suffice.

When creating a map product that is meant to be viewed at multiple scales, all of the design issues become increasingly complicated. Labeling map features is an especially burdensome problem, even with advanced label placement tools, such as Maplex (Esri ArcGIS). There are many tips in the cartographic literature on label placement for manual methods, and numerous articles that detail particular placement algorithms (for reviews see Kern and Brewer 2008; Huffman and Cromley 2002; Edmonson et al. 1996), and the topic remains of interest, evident in recent conference presentations, for example, Jordan and Michna's presentations at ICC2009. There are few resources, however, that explain how to marshal federal data attributes to best make use of commercial off the shelf (COTS) tools to map entire nations in an automated fashion. That practical goal is partly addressed by this paper, within a specific context of improving The National Map of the United States.

Labeling populated places provides an excellent case for considering the perceived importance of a given locale based on its position and labeling in given geographic extents and at given scales requested by online map users. In their current form, online tools, such as The National Map Palanterra-based Viewer, (served by the U.S. Geological Survey for viewing and downloading U.S. geographic data at viewer. nationalmap.gov) render the somewhat complicated national point layers of federal placenames when representing populated places. Our work considers the current state of labeling of populated places in The National Map Viewer and comparable consumer navigation mapping (Google maps in this pilot study), considers the polygon alternative to point-feature labeling of places, and whether richer attribute sets add value to labeling populated places.

This pilot project addresses the specific problem of labeling U.S. places through a set range of scales. The broader applicability of this project lies within the challenges of labeling in general. By using comprehensive data sets and adding attributes that enhance place distinctions, cartographers have the ability to make more refined decisions about hierarchy of place which can be incorporated directly by automated labeling decisions. The challenge of using the most suitable data sets for labeling is compounded for us by the inherent limitations of the overarching project--information has to be nationally available (not just prepared for some cities or some states), copyright free, and meet quality expectations of the U.S. federal government so that the maps are authoritative sources of geographic information. These limitations place fairly stringent restrictions on using user contributed data, vernacular geographies, or other local placename inventories. …