Centre de documentation
scientifique

Trajectory Box Plot: a new pattern to summarize movements / Laurent Etienne in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 5-6 (May - June 2016) . - pp 835 - 853 Titre : Trajectory Box Plot: a new pattern to summarize movements Type de document : Article/Communication Auteurs : Laurent Etienne, Auteur ; Thomas Devogele , Auteur ; M. Buchin, Auteur ; Gavin McArdle, Auteur Année de publication : 2016 Article en page(s) : pp 835 - 853 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] boîte à moustaches [Termes IGN] calcul d'itinéraire [Termes IGN] distance de Fréchet [Termes IGN] modèle numérique de déplacement [Termes IGN] objet mobile [Termes IGN] trajet (mobilité) Résumé : (Auteur) Nowadays, an abundance of sensors are used to collect very large datasets of moving objects. The movement of these objects can be analysed by identifying common routes. For this, a cluster of trajectories must be defined and the pattern of each cluster discovered. In this article, we introduce a new pattern, called the Trajectory Box Plot (TBP), to summarize a set of trajectories following the same route. The TBP is an extension of the well-known descriptive statistics Box Plot concept. Each TBP is described by a median trajectory, a 3D box and a 3D fence. The median trajectory depicts the typical movement of mobile objects. The box and the fences (whiskers) describe the spatial and temporal spreading around the central tendency. TBPs are useful to summarize and analyse trajectory streams, understand their spatio-temporal density and detect outliers. In this article, visual analysis highlights how the TBP pattern effectively describes how the density of trajectory clusters change over time. Numéro de notice : A2016-285 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1081205 En ligne : https://doi.org/10.1080/13658816.2015.1081205 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80864 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2016032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Processing aggregated data: the location of clusters in health data / Kevin Buchin in Geoinformatica, vol 16 n° 3 (July 2012)  

Public [article]  inGeoinformatica > vol 16 n° 3 (July 2012) . - pp 197 - 521 Titre : Processing aggregated data: the location of clusters in health data Type de document : Article/Communication Auteurs : Kevin Buchin, Auteur ; M. Buchin, Auteur ; Marc Van Kreveld, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 197 - 521 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agrégation spatiale [Termes IGN] base de données spatiotemporelles [Termes IGN] base de données thématiques [Termes IGN] géopositionnement [Termes IGN] regroupement de données [Termes IGN] santé Résumé : (Auteur) Spatially aggregated data is frequently used in geographical applications. Often spatial data analysis on aggregated data is performed in the same way as on exact data, which ignores the fact that we do not know the actual locations of the data. We here propose models and methods to take aggregation into account. For this we focus on the problem of locating clusters in aggregated data. More specifically, we study the problem of locating clusters in spatially aggregated health data. The data is given as a subdivision into regions with two values per region, the number of cases and the size of the population at risk. We formulate the problem as finding a placement of a cluster window of a given shape such that a cluster function depending on the population at risk and the cases is maximized. We propose area-based models to calculate the cases (and the population at risk) within a cluster window. These models are based on the areas of intersection of the cluster window with the regions of the subdivision. We show how to compute a subdivision such that within each cell of the subdivision the areas of intersection are simple functions. We evaluate experimentally how taking aggregation into account influences the location of the clusters found. Numéro de notice : A2012-108 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-011-0143-6 En ligne : https://doi.org/10.1007/s10707-011-0143-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31556 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2012031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Constrained free space diagrams: a tool for trajectory analysis / Kevin Buchin in International journal of geographical information science IJGIS, vol 24 n°7-8 (july 2010)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n°7-8 (july 2010) . - pp 1101 - 1125 Titre : Constrained free space diagrams: a tool for trajectory analysis Type de document : Article/Communication Auteurs : Kevin Buchin, Auteur ; M. Buchin, Auteur ; J. Gudmundsson, Auteur Année de publication : 2010 Article en page(s) : pp 1101 - 1125 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] courbe [Termes IGN] diagramme [Termes IGN] direction [Termes IGN] objet mobile [Termes IGN] similitude [Termes IGN] trajet (mobilité) [Termes IGN] vitesse Résumé : (Auteur) Time plays an important role in the analysis of moving object data. For many applications it is not sufficient to only compare objects at exactly the same times, or to consider only the geometry of their trajectories. We show how to leverage between these two approaches by extending a tool from curve analysis, namely the free space diagram. Our approach also allows us to take further attributes of the objects like speed or direction into account. We demonstrate the usefulness of the new tool by applying it to the problem of detecting single file movement. A single file is a set of moving entities, which are following each other, one behind the other. Our algorithm is the first one developed for detecting such movement patterns. For this application, we analyse demonstrate the performance of our tool both theoretically experimentally. Numéro de notice : A2010-322 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810903569598 En ligne : https://doi.org/10.1080/13658810903569598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30516 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2010041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2010042	RAB	Revue	Centre de documentation	En réserve L003	Disponible