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Visualizing spatial interaction characteristics with direction-based pattern maps

  • Xin Yao
  • Lun Wu
  • Di Zhu
  • Yong Gao
  • Yu Liu
Regular Paper

Abstract

In the era of big data, large amounts of detailed spatial interaction data are readily available due to the increasing pervasiveness of location-aware devices and techniques. Such data are often applied in the research of spatial structures, land use characteristics, and human activity regularities. However, visualizing such data on maps is a great challenge. Existing approaches either encounter overlapping and intersection problems, which may cause information loss or distortion, or are unable to present interaction characteristics. Consequently, the functional and positional features of places and human mobility trends are not fully demonstrated. We propose direction-based pattern maps, a new visualization method to display the spatial interaction pattern of every place by aggregating spatial interaction data in cardinal directions. This approach can well represent local interaction characteristics and is applicable to different spatial scales. Owing to regular hexagonal tessellations, it can avoid cluttering problems and maintain a geographical context. A case study using Beijing taxi trip data is conducted to validate the usefulness of our approach.

Graphical Abstract

Keywords

Spatial interaction Geovisualization Data abstraction Spatial pattern 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 41830645, 41625003, and 41771425), the National Key R&D Program of China (Grant No. 2017YFB0503602), and the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems (Grant No. 011177220010020).

Supplementary material

12650_2018_543_MOESM1_ESM.mp4 (12.7 mb)
Supplementary material 1 (MP4 12999 kb)
12650_2018_543_MOESM2_ESM.txt (1 kb)
Supplementary material 2 (TXT 0 kb)

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Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space SciencesPeking UniversityBeijingPeople’s Republic of China

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