Descripteur
Termes IGN > informatique > base de données > base de données orientée objet > base de données d'objets mobiles
base de données d'objets mobilesVoir aussi |
Documents disponibles dans cette catégorie (169)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Introducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)
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] TwitterRésumé : (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 Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
[article]
Titre : Group diagrams for representing trajectories Type de document : Article/Communication Auteurs : Maike Buchin, Auteur ; Bernhard Kilgus, Auteur ; Andrea Kölzsch, Auteur Année de publication : 2020 Article en page(s) : pp 2401 - 2433 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] approximation
[Termes IGN] base de données d'objets mobiles
[Termes IGN] diagramme
[Termes IGN] distance de Fréchet
[Termes IGN] données GPS
[Termes IGN] géomètrie algorithmique
[Termes IGN] itinéraire
[Termes IGN] migration animale
[Termes IGN] objet mobileRésumé : (auteur) Given the trajectories of one or several moving groups, we propose a new framework, the group diagram (GD) for representing these. Specifically, we seek a minimal GD as a concise representation of the groups maintaining the spatio-temporal structure of the groups’ movement. A GD is specified by three input values, namely a distance threshold, a similarity measure and a minimality criterion. For several variants of the GD, we give a comprehensive analysis of their computational complexity and present efficient approximation algorithms for their computation. Furthermore, we experimentally evaluate our algorithms on GPS data of migrating geese. Applying the proposed methods on these data sets reveals how the GD concisely represents the movement of the groups. This representation can be used for further analysis and for the formulation of new hypotheses for further ecological research, such as differences in movement patterns of groups on different surfaces or the shift of migration routes over several years. We use different similarity measures to summarize the migration routes of (i) a goose family for one migration period and to summarize (ii) the migration routes of one individual for several migration periods or (iii) the migration routes of several independent individuals for one migration period. Numéro de notice : A2020-690 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1684498 Date de publication en ligne : 25/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1684498 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96227
in International journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2401 - 2433[article]Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
[article]
Titre : Semantic trajectory segmentation based on change-point detection and ontology Type de document : Article/Communication Auteurs : Yuan Gao, Auteur ; Longfei Huang, Auteur ; Jun Feng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2361 - 2394 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données spatiotemporelles
[Termes IGN] détection de changement
[Termes IGN] enrichissement sémantique
[Termes IGN] modèle dynamique
[Termes IGN] objet mobile
[Termes IGN] ontologie
[Termes IGN] point d'intérêt
[Termes IGN] segmentation sémantique
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) Trajectory segmentation is a fundamental issue in GPS trajectory analytics. The task of dividing a raw trajectory into reasonable sub-trajectories and annotating them based on moving subject’s intentions and application domains remains a challenge. This is due to the highly dynamic nature of individuals’ patterns of movement and the complex relationships between such patterns and surrounding points of interest. In this paper, we present a framework called SEMANTIC-SEG for automatic semantic segmentation of trajectories from GPS readings. For the decomposition component of SEMANTIC-SEG, a moving pattern change detection (MPCD) algorithm is proposed to divide the raw trajectory into segments that are homogeneous in their movement conditions. A generic ontology and a spatiotemporal probability model for segmentation are then introduced to implement a bottom-up ontology-based reasoning for semantic enrichment. The experimental results on three real-world datasets show that MPCD can more effectively identify the semantically significant change-points in a pattern of movement than four existing baseline methods. Moreover, experiments are conducted to demonstrate how the proposed SEMANTIC-SEG framework can be applied. Numéro de notice : A2020-689 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1798966 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1798966 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96226
in International journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2361 - 2394[article]A framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica, vol 24 n° 4 (October 2020)
[article]
Titre : A framework for group converging pattern mining using spatiotemporal trajectories Type de document : Article/Communication Auteurs : Bin Zhao, Auteur ; Xintao Liu, Auteur ; Jinping Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 745 - 776 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données spatiotemporelles
[Termes IGN] comportement
[Termes IGN] convergence
[Termes IGN] exploration de données géographiques
[Termes IGN] jointure spatiale
[Termes IGN] objet mobile
[Termes IGN] reconnaissance de formesRésumé : (Auteur) A group event such as human and traffic congestion can be very roughly divided into three stages: converging stage before congestion, gathered stage when congestion happens, and dispersing stage that congestion disappears. It is of great interest in modeling and identifying converging behaviors before gathered events actually happen, which helps to proactively predict and handle potential public incidents such as serious stampedes. However, most of existing literature put too much emphasis on the second stage, only a few of them is dedicated to the first stage. In this paper, we propose a novel group pattern, namely converging, which refers to a group of moving objects converging from different directions during a certain period before gathered. To discover efficiently such converging patterns, we develop a framework for converging pattern mining (CPM) by examining how moving objects form clusters and the process of the “cluster containment”. The framework consists of three phases: snapshot cluster discovery phase, cluster containment join phase, and converging detection phase. As cluster containment mining is the key step, we develop three algorithms to discover cluster containment matches: a containment-join-algorithm, called SSCCJ, by using spatial proximity; a signature tree-based cluster-containment-join-algorithm, called STCCJ, which takes advantage of the cluster containment relations and signature techniques to filter enormous unqualified candidates in an efficient and effective way; and third, to keep the advantages of the above algorithms while avoiding their flaws, we further propose a signature quad-tree based cluster-containment-join algorithm, called SQTCCJ, which can identify efficiently matches by considering cluster spatial proximity as well as containment relations simultaneously. To assess the proposed methods, we redefine two evaluation metrics based on the concept of “Precision and Recall” in the field of information retrieval and the characteristics of converging patterns. We also propose a new indicator for measuring the duration of the converging stage in a group event. Finally, the effectiveness of the CPM and the efficiency of the mining algorithms are evaluated using three types of trajectory datasets, and the results show that the SQTCCJ algorithm demonstrates a superior performance. Numéro de notice : A2020-494 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00404-z Date de publication en ligne : 25/04/2020 En ligne : https://doi.org/10.1007/s10707-020-00404-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96114
in Geoinformatica > vol 24 n° 4 (October 2020) . - pp 745 - 776[article]Geovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data / Jacques Gautier in Geomatica, vol 74 n° 3 (September 2020)
[article]
Titre : Geovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data Type de document : Article/Communication Auteurs : Jacques Gautier , Auteur ; Paule-Annick Davoine, Auteur ; Claire Cunty, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 131 - 153 Note générale : bibliographie
This research was funded by the Region Auvergne-Rhône-Alpes.Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse harmonique
[Termes IGN] base de données spatiotemporelles
[Termes IGN] distribution spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] événement
[Termes IGN] exploration de données géographiques
[Vedettes matières IGN] GéovisualisationMots-clés libres : GrAPHiST Résumé : (auteur) Many geovisualization environments integrate graphical representations of time. Some of them include representation of both linear and cyclic aspects of time, providing an exploratory analysis of spatio-temporal data through several temporal cyclic scales. However, few of them provide an exploratory analysis of localized cyclic recurrences in spatio-temporal data. Ad hoc temporal diagrams, representing both linear and cyclic aspects of time, provide a visual search for cyclic recurrences in temporal data when the possibility is left to the user to perform a gradual modification of the represented cyclic scale’s duration. The combination of these graphic representations of time, with cartographic representations, displaying the spatial distribution of such cyclic recurrences, could provide an exploratory analysis of localized cyclic recurrences in spatio-temporal data. Mathematical tools coming from other scientific fields, such as the harmonic analysis, offer another way to identify cyclic behaviors in temporal data. Combining the visual approach offered by specifically designed geovisualization environments, with a harmonic analysis that suggests searching paths to the user during its exploratory analysis, can then improve the visual search for localized cyclic recurrences. We propose a geovisualization environment, which combines, on one hand, a visual analysis of localized cyclic recurrences in spatio-temporal data, using ad hoc temporal diagrams, cartographic representations, and specific semiologic rules, and on the other hand, mathematical tools, such as harmonic analysis and spatial clustering, that provide searching paths to the user for its visual analysis. This approach is supported by a geovisualization environment, GrAPHiST, which provides an exploratory analysis of spatio-temporal event data. Numéro de notice : A2020-821 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1139/geomat-2020-0004 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.1139/geomat-2020-0004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97245
in Geomatica > vol 74 n° 3 (September 2020) . - pp 131 - 153[article]Incorporating behavior into animal movement modeling: a constrained agent-based model for estimating visit probabilities in space-time prisms / Rebecca W. Loraamm in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)PermalinkAn empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkUber movement data: a proxy for average one-way commuting times by car / Yeran Sun in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkPermalinkPermalinkMoving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)PermalinkA polyhedra-based model for moving regions in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)PermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)PermalinkHalf a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkScene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)Permalink