research-article

Labeling streets in interactive maps using embedded labels

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SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 517 - 520

https://doi.org/10.1145/2666310.2666494

Published: 04 November 2014 Publication History

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Abstract

We consider the problem of labeling linear objects (such as streets) in interactive maps where the user can pan, zoom, and rotate continuously. Our labels contain text (such as street names). They are embedded into the objects they label, i.e., they follow the curvature of the objects, they do not move with respect to the map background, but they scale in order to maintain constant size on the screen. To the best of our knowledge, this is the first work that deals with curved labels in interactive maps.

Our objective is to label as many streets as possible and to select label positions of high quality while forbidding labels to overlap at street crossings. We present a simple but effective algorithm that takes curvature and crossings into account and produces aesthetical labelings. On average over all interaction types, our implementation reaches interactive frame rates of more than 85 frames per second.

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S. Seibert and W. Unger. The hardness of placing street names in a Manhattan type map. In G. Bongiovanni, G. Gambos, and R. Petreschi, editors, Proc. 4th Italian Conf. Algorithms Complexity (CIAC'00), volume 1767 of LNCS, pages 102--112. Springer, 2000.

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F. Wagner, A. Wolff, V. Kapoor, and T. Strijk. Three rules suffice for good label placement. Algorithmica, 30(2):334--349, 2001.

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Cited By

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Lan TLi ZWang JGong CTi P(2022)An ANNs-Based Method for Automated Labelling of Schematic Metro MapsISPRS International Journal of Geo-Information10.3390/ijgi1101003611:1(36)Online publication date: 5-Jan-2022
https://doi.org/10.3390/ijgi11010036
Gemsa ANiedermann BNöllenburg M(2020)A Unified Model and Algorithms for Temporal Map LabelingAlgorithmica10.1007/s00453-020-00694-7Online publication date: 7-Apr-2020
https://doi.org/10.1007/s00453-020-00694-7
Gemsa ANiedermann BNöllenburg M(2020)Placing Labels in Road Maps: Algorithms and ComplexityAlgorithmica10.1007/s00453-020-00678-7Online publication date: 1-Feb-2020
https://doi.org/10.1007/s00453-020-00678-7
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Index Terms

Labeling streets in interactive maps using embedded labels
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Virtual reality
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Mixed / augmented reality
      2. Virtual reality

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Published In

SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2014

651 pages

ISBN:9781450331319

DOI:10.1145/2666310

General Chairs:
Yan Huang
University of North Texas
,
Markus Schneider
University of Florida
,
Program Chairs:
Michael Gertz
Heidelberg University, Germany
,
John Krumm
Microsoft Research
,
Jagan Sankaranarayanan
NEC Labs

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 04 November 2014

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SIGSPATIAL

SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4 - 7, 2014

Texas, Dallas

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SIGSPATIAL '14 Paper Acceptance Rate 39 of 184 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Lan TLi ZWang JGong CTi P(2022)An ANNs-Based Method for Automated Labelling of Schematic Metro MapsISPRS International Journal of Geo-Information10.3390/ijgi1101003611:1(36)Online publication date: 5-Jan-2022
https://doi.org/10.3390/ijgi11010036
Gemsa ANiedermann BNöllenburg M(2020)A Unified Model and Algorithms for Temporal Map LabelingAlgorithmica10.1007/s00453-020-00694-7Online publication date: 7-Apr-2020
https://doi.org/10.1007/s00453-020-00694-7
Gemsa ANiedermann BNöllenburg M(2020)Placing Labels in Road Maps: Algorithms and ComplexityAlgorithmica10.1007/s00453-020-00678-7Online publication date: 1-Feb-2020
https://doi.org/10.1007/s00453-020-00678-7
Niedermann BNöllenburg M(2016)An Algorithmic Framework for Labeling Road MapsGeographic Information Science10.1007/978-3-319-45738-3_20(308-322)Online publication date: 14-Sep-2016
https://doi.org/10.1007/978-3-319-45738-3_20
Takeuchi SSugiura KAkahoshi YZettsu K(2015)Constrained region selection method based on configuration space for visualization in scientific dataset searchProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7364006(2191-2200)Online publication date: 29-Oct-2015
https://dl.acm.org/doi/10.1109/BigData.2015.7364006
Schwartges NMorgan BHaunert JWolff A(2015)Labeling Streets Along a Route in Interactive 3D Maps Using BillboardsAGILE 201510.1007/978-3-319-16787-9_16(269-287)Online publication date: 24-Apr-2015
https://doi.org/10.1007/978-3-319-16787-9_16

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The Exploratory Labeling Assistant: Mixed-Initiative Label Curation with Large Document Collections

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The Exploratory Labeling Assistant: Mixed-Initiative Label Curation with Large Document Collections

Informative pseudo-labeling for graph neural networks with few labels

Boundary labeling: Models and efficient algorithms for rectangular maps

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