2026-08-06T22:26:07Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:13042 2024-03-06T10:51:25Z ddc:004 open_access

Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks Kannangara, Sameera Xie, Hairuo Tanin, Egemen Harwood, Aaron Karunasekera, Shanika moving objects shortest path graphs 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sameera Kannangara and Hairuo Xie and Egemen Tanin and Aaron Harwood and Shanika Karunasekera 2020 Is Part Of LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2021.I.7 urn:nbn:de:0030-drops-130428 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.7 eng https://creativecommons.org/licenses/by/3.0/legalcode