Central Time-Space Map Construction Using the Snake Model

WEI Zhiwei; LIU Yuangang; XU Wenjia; WANG Yang

doi:10.13203/j.whugis20220553

Volume 47 Issue 12

Dec. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(12): 2105-2112. > DOI: 10.13203/j.whugis20220553

WEI Zhiwei, LIU Yuangang, XU Wenjia, WANG Yang. Central Time-Space Map Construction Using the Snake Model[J]. Geomatics and Information Science of Wuhan University, 2022, 47(12): 2105-2112. DOI: 10.13203/j.whugis20220553

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Central Time-Space Map Construction Using the Snake Model

WEI Zhiwei^{1, 2,},
LIU Yuangang³,
XU Wenjia⁴,
WANG Yang^{1, 2}

1.
Key Laboratory of Network Information System Technology, Institute of Electronic, Chinese Academy of Sciences, Beijing 100830, China
2.
The Aerospace Information Research Institute, Chinese Academic of Sciences, Beijing 100830, China
3.
School of Geosciences, Yangtze University, Wuhan 430100, China
4.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds:

The National Natural Science Foundation of China 41871378

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Author Bio:
WEI Zhiwei, PhD, assistant researcher, specializes in intelligent handling and visualization of geospatial data. E-mail: 2011301130108@whu.edu.cn
Received Date: September 04, 2022
Available Online: January 06, 2023
Published Date: December 04, 2022

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Abstract

Abstract

Objectives The central time-space maps deform the map space to visualize the one-to-many time distance, which can intuitively reflect the distance changes due to the influence of a traffic system. The core of the central time-space map construction is to calculate the non-control points' locations based on the control points' displacements. Displacement is a basic operation in cartographic generalization and many approaches have been developed.
Methods We model the central time-space map construction as a problem of displacement in cartographic generalization, use the relative neighborhood graph to express the proximity relations between the points, and the initial forces on the points are computed based on the control points' displacements.And the Snake model is applied iteratively based on the built graph to obtain the new locations of all points, post operations are also applied to avoid topology errors in the iterative process.
Results and Conclusions Compared to the existing approaches, the proposed method can reduce the topology errors and improve the shape similarity for the deformed boundaries, and can better avoid topology errors and maintain the local morphologies.
- central time-space map,
- spatialtemporal visualization,
- time-space map,
- spatial deformation,
- displacement algorithm

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References

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