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Visual abstraction of large-scale geographical point data with credible spatial interpolation

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Abstract

With the increasing size of geographical point data, scatterplot often suffers from serious overdraw problems, which greatly hinders the visual exploration and analysis of data. At present, a variety of sampling methods considering data features have been proposed to simplify the large-scale geographical point data to alleviate this problem. However, there is still no attempt to simplify data from the perspective of geostatistics in the sampling methods, which will be greatly beneficial to explore the spatial information of unknown points and restore the original data features. In this paper, a sampling model is proposed to generate a representative subset from the large-scale geographical point data to improve the interpolation quality of the sampled points and preserve attribute features of original data, in which a semivariable function is applied to capture geostatistical characteristics of data attributes. A set of visual interfaces are further implemented enabling users to visually evaluate the sampled results of different methods and effectively conduct geospatial analysis. Case studies and quantitative comparisons based on the real-world geographical datasets further demonstrate the validity of our interpolation-driven sampling model in the abstraction and analysis of large-scale geographical point data.

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Acknowledgements

We would like to thank the reviewers for their thoughtful comments. The work is supported in part by the National Natural Science Foundation of China (No.61872314, 41901363 and 41801313), Open Project Program of the State Key Lab of CADCG of Zhejiang University (No. A2001), and the Public Welfare Technology Applied Research Project of Zhejiang Province (No.LGF20G010003).

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Authors and Affiliations

  1. Zhejiang University of Finance and Economics, Hangzhou, China

    Fengling Zheng, Jin Wen, Xiang Zhang, Xinlong Zhang, Yanan Liu & Zhiguang Zhou

  2. College of Environment, Zhejiang University of Technology, Hangzhou, China

    Yuanyuan Chen

  3. The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Ting Xu

  4. Institute of Data and Target Engineering, Information Engineering University China, Zhengzhou, China

    Xiaohui Chen

  5. School of Media and Design, Hangzhou Dianzi University, Hangzhou, China

    Yigang Wang

  6. College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China

    Weihua Su

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  1. Fengling Zheng
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  2. Jin Wen
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  3. Xiang Zhang
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  4. Yuanyuan Chen
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  5. Xinlong Zhang
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  7. Ting Xu
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  8. Xiaohui Chen
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  9. Yigang Wang
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  10. Weihua Su
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  11. Zhiguang Zhou
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Correspondence to Zhiguang Zhou.

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Zheng, F., Wen, J., Zhang, X. et al. Visual abstraction of large-scale geographical point data with credible spatial interpolation. J Vis 24, 1303–1317 (2021). https://doi.org/10.1007/s12650-021-00777-9

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