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Space-time density of trajectories : exploring spatio-temporal patterns in movement data / Urška Demšar in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
[article]
Titre : Space-time density of trajectories : exploring spatio-temporal patterns in movement data Type de document : Article/Communication Auteurs : Urška Demšar, Auteur ; K. Virrantaus, Auteur Année de publication : 2010 Article en page(s) : pp 1527 - 1542 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] densité
[Termes IGN] données localisées 3D
[Termes IGN] données spatiotemporelles
[Termes IGN] estimation par noyau
[Termes IGN] exploration de données géographiques
[Termes IGN] Finlande
[Termes IGN] navire
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (Auteur) Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space-time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. The space-time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space-time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation. Numéro de notice : A2010-466 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2010.511223 En ligne : https://doi.org/10.1080/13658816.2010.511223 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30659
in International journal of geographical information science IJGIS > vol 24 n° 10 (october 2010) . - pp 1527 - 1542[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2010061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010062 RAB Revue Centre de documentation En réserve L003 Disponible Spatio-temporal trajectory analysis of mobile objects following the same itinerary / Laurent Etienne (26/05/2010)
Titre : Spatio-temporal trajectory analysis of mobile objects following the same itinerary Type de document : Article/Communication Auteurs : Laurent Etienne, Auteur ; Thomas Devogele , Auteur ; Alain Bouju, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 26/05/2010 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 38-2 Conférence : SDH 2010, 14th International Symposium on Spatial Data Handling, joint conference with ISPRS 26/05/2010 28/05/2010 Hong-Kong Hong-Kong Proceedings Springer Importance : 6 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] comportement
[Termes IGN] exploration de données géographiques
[Termes IGN] navire
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (Auteur) More and more mobile objects are now equipped with sensors allowing real time monitoring of their movements. Nowadays, the data produced by these sensors can be stored in spatio-temporal databases. The main goal of this article is to perform a data mining on a huge quantity of mobile object's positions moving in an open space in order to deduce its behaviour. New tools must be defined to ease the detection of outliers. First of all, a zone graph is set up in order to define itineraries. Then, trajectories of mobile objects following the same itinerary are extracted from the spatio-temporal database and clustered. A statistical analysis on this set of trajectories lead to spatio-temporal patterns such as the main route and spatio-temporal channel followed by most of trajectories of the set. Using these patterns, unusual situations can be detected. Furthermore, a mobile object's behaviour can be defined by comparing its positions with these spatio-temporal patterns. In this article, this technique is applied to ships' movements in an open maritime area. Unusual behaviours such as being ahead of schedule or delayed or veering to the left or to the right of the main route are detected. A case study illustrates these processes based on ships' positions recorded during two years around the Brest area. This method can be extended to almost all kinds of mobile objects (pedestrians, aircrafts, hurricanes, ...) moving in an open area. Numéro de notice : C2010-047 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : sans En ligne : https://www.isprs.org/proceedings/XXXVIII/part2/Papers/50_Paper.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64319 Documents numériques
en open access
14253_ctap_isprs_vol38part2_50_paper_devogele.pdfAdobe Acrobat PDF St-DMQL: a semantic trajectory data mining query language / Vania Bogorny in International journal of geographical information science IJGIS, vol 23 n°9-10 (september 2009)
[article]
Titre : St-DMQL: a semantic trajectory data mining query language Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; Bart Kuijpers, Auteur ; L. Alvares, Auteur Année de publication : 2009 Article en page(s) : pp 1245 - 1276 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] attribut sémantique
[Termes IGN] découverte de connaissances
[Termes IGN] données localisées
[Termes IGN] exploration de données géographiques
[Termes IGN] langage de programmation
[Termes IGN] langage de requête
[Termes IGN] navigation
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] requête spatiotemporelleRésumé : (Auteur) Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery. Copyright Taylor & Francis Numéro de notice : A2009-388 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810802231449 En ligne : https://doi.org/10.1080/13658810802231449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30018
in International journal of geographical information science IJGIS > vol 23 n°9-10 (september 2009) . - pp 1245 - 1276[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-09061 RAB Revue Centre de documentation En réserve L003 Disponible Mining long, sharable patterns in trajectories of moving objects / G. Gidofalvi in Geoinformatica, vol 13 n° 1 (March 2009)
[article]
Titre : Mining long, sharable patterns in trajectories of moving objects Type de document : Article/Communication Auteurs : G. Gidofalvi, Auteur ; T.B. Pedersen, Auteur Année de publication : 2009 Article en page(s) : pp 27 - 55 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données spatiotemporelles
[Termes IGN] exploration de données géographiques
[Termes IGN] itinéraire
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'itinéraire ou de trajectoireRésumé : (Auteur) The efficient analysis of spatio-temporal data, generated by moving objects, is an essential requirement for intelligent location-based services. Spatio-temporal rules can be found by constructing spatio-temporal baskets, from which traditional association rule mining methods can discover spatio-temporal rules. When the items in the baskets are spatio-temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable frequent routes. The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of sub-routes of long routes. Considering alternative modelling options for trajectories, leads to the development of two effective variants of the method. SQL-based implementations are described, and extensive experiments on both real life- and large-scale synthetic data show the effectiveness of the method and its variants. Copyright Springer Numéro de notice : A2009-004 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-007-0042-z En ligne : https://doi.org/10.1007/s10707-007-0042-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29634
in Geoinformatica > vol 13 n° 1 (March 2009) . - pp 27 - 55[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-09011 RAB Revue Centre de documentation En réserve L003 Disponible Reporting leaders and followers among trajectories of moving point objects / M. Andersson in Geoinformatica, vol 12 n° 4 (December 2008)
[article]
Titre : Reporting leaders and followers among trajectories of moving point objects Type de document : Article/Communication Auteurs : M. Andersson, Auteur ; J. Gudmundsson, Auteur ; P. Laube, Auteur ; et al., Auteur Année de publication : 2008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'objets mobiles
[Termes IGN] données spatiotemporelles
[Termes IGN] géomètrie algorithmique
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] objet mobile
[Termes IGN] quantité continue
[Termes IGN] quantité discrète
[Termes IGN] reconstruction d'itinéraire ou de trajectoireRésumé : (Auteur) Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern ‘one object is leading others’, which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns. Copyright Springer Numéro de notice : A2008-381 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-007-0037-9 En ligne : https://doi.org/10.1007/s10707-007-0037-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29374
in Geoinformatica > vol 12 n° 4 (December 2008)[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-08041 RAB Revue Centre de documentation En réserve L003 Disponible A relative representation of trajectories in geographical spaces / V. Noyon in Geoinformatica, vol 11 n° 4 (December 2007)PermalinkReconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)PermalinkTrajectory indexing using movement constraints / C.S. Jensen in Geoinformatica, vol 9 n° 2 (June - August 2005)Permalink