Large Scale Analytics of Vector+Raster Big Spatial Data?

Ahmed Eldawy, David Haynes, Lyuye Niu, Zhiba Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big raster data, e.g., remote sensing data or medical images, or big vector data, e.g., geotagged tweets or trajectories. However, when raster and vector data mix, one dataset must be converted to the other representation requiring vector-to-raster or raster-to-vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both vector and raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a raster layer for each polygon in a vector layer. We propose a novel method, called Scanline method, which does not require a conversion between raster and vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.

Original languageEnglish (US)
Title of host publicationGIS
Subtitle of host publicationProceedings of the ACM International Symposium on Advances in Geographic Information Systems
EditorsSiva Ravada, Erik Hoel, Roberto Tamassia, Shawn Newsam, Goce Trajcevski, Goce Trajcevski
PublisherAssociation for Computing Machinery
ISBN (Print)9781450354905
DOIs
StatePublished - Nov 7 2017
Event25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017 - Redondo Beach, United States
Duration: Nov 7 2017Nov 10 2017

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Volume2017-November

Other

Other25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017
CountryUnited States
CityRedondo Beach
Period11/7/1711/10/17

Bibliographical note

Funding Information:
?This work is supported in part by the University of California, Riverside and by the National Institutes of Health under grant NIH 5T32CA163184

Keywords

  • Big Spatial Data
  • Clipping
  • Raster
  • Satellite
  • Vector

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