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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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n° 10 (october 2010) . - pp 1527 - 1542 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 [article]

Exemplaires(2)

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

A spatio-temporal population model to support risk assessment and damage analysis for decision-making / T. Ahola in International journal of geographical information science IJGIS, vol 21 n° 8 (september 2007)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 21 n° 8 (september 2007) . - pp 935 - 953 Titre : A spatio-temporal population model to support risk assessment and damage analysis for decision-making Type de document : Article/Communication Auteurs : T. Ahola, Auteur ; K. Virrantaus, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 935 - 953 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données spatiotemporelles [Termes IGN] Finlande [Termes IGN] migration humaine [Termes IGN] mobilité territoriale [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] outil d'aide à la décision [Termes IGN] protection civile [Termes IGN] risque majeur [Termes IGN] visualisation dynamique [Termes IGN] zone urbaine Résumé : (Auteur) The aim of this research is to develop and implement a simple spatio-temporal model of population location that might improve risk assessment and damage analysis for decision-making in both the Finnish Fire and Rescue Services and the Finnish Defence Forces. The motivation for the research is that present risk models do not take into account the temporal variation in population location during different times of the day. We use spatio-temporal modelling methods to model the population dynamics, and visualization techniques to represent the model outcomes. In addition, we apply the developed model to a damage-analysis application. The case study site is located in the centre of Helsinki. The model uses a basic population and workplace dataset maintained by the Helsinki Metropolitan Area Council. By means of this model, we intend to advance risk assessment, which considers the consequences of accidents. This model has the potential to help decision-makers evaluate their plans in several application areas - such as achieving better preparedness by having more reliable evacuation plans and resource allocation. In addition to the application-related technological research, a more generic framework about decision-making supported by spatio-temporal knowledge and visualization is presented. Copyright Taylor & Francis Numéro de notice : A2007-326 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810701349078 En ligne : https://doi.org/10.1080/13658810701349078 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28689 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-07051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-07052	RAB	Revue	Centre de documentation	En réserve L003	Disponible