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Introducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)  

Public Titre : Introducing diversion graph for real-time spatial data analysis with location based social networks Type de document : Article/Communication Auteurs : Sameera Kannangara, Auteur ; Hairuo Xie, Auteur ; Egemen Tanin, Auteur ; Aaron Harwood, Auteur ; Shanika Karunasekera, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] chemin le plus court, algorithme du [Termes IGN] graphe [Termes IGN] image Flickr [Termes IGN] objet mobile [Termes IGN] réseau social géodépendant [Termes IGN] temps réel [Termes IGN] triangulation de Delaunay [Termes IGN] Twitter Résumé : (auteur) 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. Numéro de notice : C2021-079 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.I.7 Date de publication en ligne : 25/09/2020 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.I.7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100930

Map matching for semi-restricted trajectories / Timon Behr (2021)  

Public Titre : Map matching for semi-restricted trajectories Type de document : Article/Communication Auteurs : Timon Behr, Auteur ; Thomas van Dijk, Auteur ; Axel Forsch, Auteur ; Jan‐Henrik Haunert, Auteur ; Sabine Storandt, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Importance : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] appariement de cartes [Termes IGN] cycliste [Termes IGN] information sémantique [Termes IGN] OpenStreetMap [Termes IGN] piéton [Termes IGN] positionnement par GPS [Termes IGN] réseau routier [Termes IGN] trajet (mobilité) Résumé : (auteur) We consider the problem of matching trajectories to a road map, giving particular consideration to trajectories that do not exclusively follow the underlying network. Such trajectories arise, for example, when a person walks through the inner part of a city, crossing market squares or parking lots. We call such trajectories semi-restricted. Sensible map matching of semi-restricted trajectories requires the ability to differentiate between restricted and unrestricted movement. We develop in this paper an approach that efficiently and reliably computes concise representations of such trajectories that maintain their semantic characteristics. Our approach utilizes OpenStreetMap data to not only extract the network but also areas that allow for free movement (as e.g. parks) as well as obstacles (as e.g. buildings). We discuss in detail how to incorporate this information in the map matching process, and demonstrate the applicability of our method in an experimental evaluation on real pedestrian and bicycle trajectories. Numéro de notice : C2021-081 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.II.12 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.II.12 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100939

Reproducible research and GIScience: An evaluation using GIScience conference papers / Franck O. Ostermann (2021)  

Public Titre : Reproducible research and GIScience: An evaluation using GIScience conference papers Type de document : Article/Communication Auteurs : Franck O. Ostermann, Auteur ; Daniel Nüst, Auteur ; Carlos Granell, Auteur ; Barbara Hofer, Auteur ; Markus Konkol, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Importance : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information [Termes IGN] code source libre [Termes IGN] données ouvertes [Termes IGN] information géographique [Termes IGN] recherche scientifique [Termes IGN] reproductibilité Résumé : (auteur) GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875. Numéro de notice : C2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.II.2 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.II.2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100938