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Spatial Gems 2022 Workshop Report: The 4th ACM SIGSPATIAL International Workshop on Spatial Gems

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Published:07 November 2023Publication History
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Abstract

Researchers and practitioners working with spatial data often develop fundamental new techniques they would like to share with their community. These are not necessarily new research results, not yet in any textbook, but they are interesting, self-contained techniques for doing something useful in the domain of spatial data. We call these techniques "spatial gems".

References

  1. W. R. Franklin and S. V. G. de Magalhães. Implementing simulation of simplicity for geometric degeneracies. In 4th ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2022). ACM, 2022.Google ScholarGoogle Scholar
  2. W. R. Franklin and S. V. G. de Magalhães. Implementing simulation of simplicity for geometric degeneracies, 2022.Google ScholarGoogle Scholar
  3. J. Krumm. Statistics for all walks on a lattice graph. In 4th ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2022). ACM, 2022.Google ScholarGoogle Scholar
  4. J. Krumm, A. Züfle, and C. Shahabi. Spatial Gems, Volume 1. Morgan & Claypool, 2022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Y. Y. Liu and M. Allen-Dumas. Online heatmap generation with both high and low weights. In 4th ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2022). ACM, 2022.Google ScholarGoogle Scholar
  6. Y. Y. Liu and M. Allen-Dumas. Online heatmap generation with both high and low weights. arXiv preprint arXiv:2212.07820, 2022.Google ScholarGoogle Scholar
  7. Z. Shang and A. Eldawy. Object delineation in satellite images. In 4th ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2022). ACM, 2022.Google ScholarGoogle Scholar
  8. Z. Shang and A. Eldawy. Object delineation in satellite images. arXiv preprint arXiv:2212.07020, 2022.Google ScholarGoogle Scholar
  9. A. Züfle. Probabilistic counting in uncertain spatial databases using generating functions. arXiv preprint arXiv:2112.06344, 2021.Google ScholarGoogle Scholar
  10. A. Züfle. Probabilistic counting in uncertain spatial databases using generating functions. In 4th ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2022). ACM, 2022.Google ScholarGoogle Scholar

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  • Published in

    cover image SIGSPATIAL Special
    SIGSPATIAL Special  Volume 14, Issue 1
    November 2022
    55 pages
    EISSN:1946-7729
    DOI:10.1145/3632268
    Issue’s Table of Contents

    Copyright © 2023 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 7 November 2023

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