A Study of Visual Preferences for Map Label Placement

Abstract

Digital maps are important for many decision-making tasks that require situational awareness, navigation, or location-specific data. Often,digital mapping tools must generate a map that displays labels near associated features in a visually appealing manner, without occluding important information. Automated label placement systems generally accomplish this nontrivial task through a combination of heuristic algorithms and cartography rules, but the resulting maps often do not reflect the preferences and needs of the map user. To achieve higher quality map views,research is needed to identify cognitive and computational approaches for generating high-quality maps that meet user needs and expectations.In this paper, we present a study designed to better understand the visual preferences of map users and support the development of models for digital map displays. In particular, we found that participants demonstrated preferences for how labels are placed near their point of interest, and that when making trade-offs between alignment and distance of a label, they were more consistent in choosing positions that prioritized alignment over distance. We also investigated if context effects, such as similarity, attraction, or compromise, could be observed when participants chose between equally valued alternatives.

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Document Details

Document Type
Technical Report
Publication Date
Mar 16, 2023
Accession Number
AD1196303

Entities

People

  • Chris J. Michael
  • Dina M. Acklin
  • Jaelle Sceuerman
  • Jason Harman

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Cartography
  • Computational Complexity
  • Computational Science
  • Computer Programs
  • Computer Science
  • Consistency
  • Digital Maps
  • Human Behavior
  • Instructions
  • Learning
  • Machine Learning
  • Maps
  • Military Research
  • Minority Groups
  • Pattern Recognition
  • Situational Awareness
  • Spatial Distribution

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Systems Analysis and Design