License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2021.I.7
URN: urn:nbn:de:0030-drops-130428
URL: https://drops.dagstuhl.de/opus/volltexte/2020/13042/
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Kannangara, Sameera ; Xie, Hairuo ; Tanin, Egemen ; Harwood, Aaron ; Karunasekera, Shanika

Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks

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LIPIcs-GIScience-2021-I-7.pdf (0.6 MB)


Abstract

Neighbourhood graphs are useful for inferring the travel network between locations posted in the Location Based Social Networks (LBSNs). Existing neighbourhood graphs, such as the Stepping Stone Graph lack the ability to process a high volume of LBSN data in real time. We propose a neighbourhood graph named Diversion Graph, which uses an efficient edge filtering method from the Delaunay triangulation mechanism for fast processing of LBSN data. This mechanism enables Diversion Graph to achieve a similar accuracy level as Stepping Stone Graph for inferring travel networks, but with a reduction of the execution time of over 90%. Using LBSN data collected from Twitter and Flickr, we show that Diversion Graph is suitable for travel network processing in real time.

BibTeX - Entry

@InProceedings{kannangara_et_al:LIPIcs:2020:13042,
  author =	{Sameera Kannangara and Hairuo Xie and Egemen Tanin and Aaron Harwood and Shanika Karunasekera},
  title =	{{Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{7:1--7:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Krzysztof Janowicz and Judith A. Verstegen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/13042},
  URN =		{urn:nbn:de:0030-drops-130428},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.7},
  annote =	{Keywords: moving objects, shortest path, graphs}
}

Keywords: moving objects, shortest path, graphs
Collection: 11th International Conference on Geographic Information Science (GIScience 2021) - Part I
Issue Date: 2020
Date of publication: 25.09.2020


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