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Retrieving α-Shapes and Schematic Polygonal Approximations for Sets of Points within Queried Temporal Ranges

Published: 05 November 2019 Publication History

Abstract

The interactive exploration of data requires data structures that can be repeatedly queried to obtain simple visualizations of parts of the data. We consider the scenario that the data is a set of points each associated with a time stamp and that the result of each query is visualized by an α-shape, which generalizes the concept of convex hulls. Instead of computing each shape independently, we suggest and analyze a simple data structure that aggregates the α-shapes of all possible queries. Once the data structure is built, it particularly allows us to query single α-shapes without retrieving the actual (possibly large) point set and thus to rapidly produce small previews of the queried data. We discuss the data structure for the original α-shapes as well as for a schematized version of α-shapes, which further simplifies the visualization. We evaluate the data structure on real-world data. The experiments indicate linear memory consumption with respect to the number of points, which makes the data structure applicable in practice, although the size is quadratic for a theoretic worst case example.

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cover image ACM Conferences
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2019
648 pages
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Publication History

Published: 05 November 2019

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Author Tags

  1. α-shape
  2. data structure
  3. point set
  4. temporal range queries

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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  • (2023)Shortcut Hulls: Vertex-restricted Outer Simplifications of PolygonsComputational Geometry10.1016/j.comgeo.2023.101983(101983)Online publication date: Jan-2023
  • (2022)Geospatial Information Research: State of the Art, Case Studies and Future PerspectivesPFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science10.1007/s41064-022-00217-990:4(349-389)Online publication date: 19-Sep-2022
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  • (2020)A Time-Windowed Data Structure for Spatial Density MapsProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422242(15-24)Online publication date: 3-Nov-2020
  • (2020)Efficient computation of minimum-area rectilinear convex hull under rotation and generalizationsJournal of Global Optimization10.1007/s10898-020-00953-5Online publication date: 7-Oct-2020

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