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Efficient processing of raster and vector data

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  • Fernando Silva-Coira
  • José R Paramá
  • Susana Ladra
  • Juan R López
  • Gilberto Gutiérrez

Abstract

In this work, we propose a framework to store and manage spatial data, which includes new efficient algorithms to perform operations accepting as input a raster dataset and a vector dataset. More concretely, we present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.

Suggested Citation

  • Fernando Silva-Coira & José R Paramá & Susana Ladra & Juan R López & Gilberto Gutiérrez, 2020. "Efficient processing of raster and vector data," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-35, January.
    • Handle: RePEc:plo:pone00:0226943
    DOI: 10.1371/journal.pone.0226943
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    References listed on IDEAS

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    1. Hasso Plattner & Alexander Zeier, 2012. "In-Memory Data Management," Springer Books, Springer, edition 2, number 978-3-642-29575-1, January.
    2. Hasso Plattner, 2013. "A Course in In-Memory Data Management," Springer Books, Springer, edition 127, number 978-3-642-36524-9, January.
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    Cited by:

    1. A’kif Al-Fugara & Ali Nouh Mabdeh & Saad Alayyash & Awni Khasawneh, 2023. "Hydrological and Hydrodynamic Modeling for Flash Flood and Embankment Dam Break Scenario: Hazard Mapping of Extreme Storm Events," Sustainability, MDPI, vol. 15(3), pages 1-28, January.

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