research-article

A road generalization method using layered stroke networks

Authors:

Hiroaki Fukuyasu,

Daisuke Yamamoto,

Naohisa TakahashiAuthors Info & Claims

LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

Article No.: 2, Pages 1 - 10

https://doi.org/10.1145/3356994.3365499

Published: 05 November 2019 Publication History

Abstract

A sketch map is a generalized simple map using abstractive and graphical symbols to provide the information for a specific purpose, e.g., tourist information or a guidance for commercial facilities, and it becomes popular and be widely used due to its good readability. A sketch map consists of the only necessary (or requested) facilities, such as restaurants or parking lots, and their adjacent necessary roads. This implies that to generate a sketch map, we have to select the necessary roads and remove the others, which is called a road generalization. However, it is a difficult problem to automatically select all the necessary roads when the facilities are given.

In this paper, we propose a road generalization method based on the strokes [18, 19] to automatically select all the necessary roads in order to support a generation of a sketch map when the facilities are given. The proposed method has several features as follows: (1) classifies the stroke networks into two layers: global stroke networks and local stroke networks to improve the perceptibility (i.e., readability) of a sketch map, (2) guarantees the reachability to the facilities, and (3) avoids a generation of a redundant detour path to the facilities.

We implement the prototype system based on the proposed method and compare with the previous method. From the experiment evaluations, we show that the proposed method decreases the average distance to the given facilities by about 60%. Moreover, by using local stroke networks, the reachability to the facilities is improved from about 0.888 to over 0.979 (up to 1.0).

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Cited By

Yamamoto DHiura YKim Y(2023)k-Fewest Turn and Shortest Path Algorithm based on Stroke GraphProceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/3615895.3628170(32-41)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615895.3628170
Wu PYin YWu CBai XZhang CDai Z(2022)An Automatic Generalization Method for a Dense Road Network Area Considering Spatial Structural Features as ConstraintsISPRS International Journal of Geo-Information10.3390/ijgi1112059911:12(599)Online publication date: 28-Nov-2022
https://doi.org/10.3390/ijgi11120599
Bouros PDasu TKanza YRenz MSacharidis D(2020)LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3383653.338366511:3(30-33)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383665
Show More Cited By

Index Terms

A road generalization method using layered stroke networks
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Conferences

LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

November 2019

92 pages

ISBN:9781450369633

DOI:10.1145/3356994

Editors:
Panagiotis Bouros
Johannes Gutenberg University Mainz, Germany
,
Tamraparni Dasu
AT&T Labs - Research
,
Yaron Kanza
AT&T Labs - Research
,
Matthias Renz
Christian-Albrechts-Universität zu Kiel, Germany
,
Dimitris Sacharidis
Technische Universität Wien, Austria

Copyright © 2019 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2019

Illinois, Chicago

Acceptance Rates

LocalRec '19 Paper Acceptance Rate 6 of 12 submissions, 50%;

Overall Acceptance Rate 17 of 26 submissions, 65%

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Cited By

Yamamoto DHiura YKim Y(2023)k-Fewest Turn and Shortest Path Algorithm based on Stroke GraphProceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/3615895.3628170(32-41)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615895.3628170
Wu PYin YWu CBai XZhang CDai Z(2022)An Automatic Generalization Method for a Dense Road Network Area Considering Spatial Structural Features as ConstraintsISPRS International Journal of Geo-Information10.3390/ijgi1112059911:12(599)Online publication date: 28-Nov-2022
https://doi.org/10.3390/ijgi11120599
Bouros PDasu TKanza YRenz MSacharidis D(2020)LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3383653.338366511:3(30-33)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383665
Huynh HSelvakumar R(2020)Extracting Backbone Structure of a Road Network from Raw DataComputational Science – ICCS 202010.1007/978-3-030-50371-0_43(582-594)Online publication date: 15-Jun-2020
https://doi.org/10.1007/978-3-030-50371-0_43

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