Qualitative Representation of Dynamic Attributes of Trajectories

International audienceTrajectory dynamic characteristics may be a very relevant source of information to analyze the behaviour of moving objects. However, most of existing works on trajectory representation deal only with basic parameters of trajectories, namely space and time. In this paper, we show how some derivatives of the spatio-temporal dimension, e.g. speed, acceleration, direction, may be integrated in trajectory modelling. We address the problem of representing trajectories in a way that qualitative descriptions of trajectories are stored and easily accessed through an ontology called QualiTraj which is also flexible enough to support relevant raw data representation. We validate our proposal with real GPS traces collected from a well-known sports tracking mobile application

Un Framework pour l'annotation automatique des trajectoires sémantiques

Location data is ubiquitous in many aspects of our lives. We are witnessing an increasing usage of this kind of data by a variety of applications. As a consequence, information systems are required to deal with large datasets containing raw data in order to build high level abstractions. Semantic Web technologies offers powerful representation tools for pervasive applications. The convergence of location-based services and Semantic Web standards allows an easier interlinking and annotation of trajectories.In this thesis, we focus in modeling mobile object trajectories in the context of the Semantic Web. First, we propose an ontology that allows the representation of generic episodes. Our model also handles contextual elements that may be related to trajectories. Second, we propose a framework containing three algorithms for automatic annotation of trajectories. The first one detects moves, stops, and noisy data; the second one is able to compress generic time series and create episodes that resumes the evolution of trajectory characteristics; the third one exploits the linked data cloud to annotate trajectories with geographic elements that intersects it with data from OpenStreetMap.As results of this thesis, we have a new ontology that can represent spatiotemporal phenomena at different levels of granularity. Moreover, our framework offers three novel algorithms for trajectory annotation. The move-stop-noise detection method is able to deal with irregularly sampled traces and do not depend on external data of the underlying geography; our time series compression method is able to find values that summarize a series at the same time that too small segments are avoided; and our spatial annotation algorithm explores linked data and the relationships among concepts to find relevant types of spatial features to describe the environment where the trajectory took place.Les données de localisation sont présentes dans plusieurs aspects de notre vie. Nous assistons à une utilisation croissante de ce type de données par une variété d'applications. En conséquence, les systèmes d'information sont demandés à traiter des grands ensembles de données brutes afin de construire des abstractions de haut niveau. La convergence des services de localisation et des standards de la Web sémantique rendent plus faciles les taches d’interconnexion et d’annotation des trajectoires.Dans cette thèse, nous nous concentrons sur la modélisation de trajectoires dans le contexte de la Web sémantique. Nous proposons une ontologie pour représenter des épisodes génériques. Notre modèle couvre aussi des éléments contextuels qui peuvent être liés à des trajectoires. Nous proposons aussi un framework contenant trois algorithmes d'annotation des trajectoires. Le premier détecte les mouvements, les arrêts et les données manquants; le second est capable de compresser des séries temporelles et de créer des épisodes qui reprennent l'évolution des caractéristiques de la trajectoire; le troisième exploite les données liées pour annoter des trajectoires avec des éléments géographiques qui l’intersecte a partir des données d'OpenStreetMap.Comme résultats, nous avons une nouvelle ontologie qui peut représenter des phénomènes spatiotemporels dans différents niveaux de granularité. En outre, notre méthode de détection de mouvement-arrêt-bruit est capable de traiter des traces échantillonnées irrégulièrement et ne dépend pas des données externes; notre méthode de compression des séries temporelles est capable de trouver des valeurs qui la résume en même temps que des segments trop courts sont évités; et notre algorithme d'annotation spatiale explore des données liées et des relations entre concepts pour trouver des types pertinents d'entités spatiales qui peuvent décrire l'environnement où la trajectoire a eu lieu

FrameSTEP: A framework for annotating semantic trajectories based on episodes

International audienc

An Ontology-based Approach to Represent Trajectory Characteristics

International audienceThe behavior of moving objects has been a relevant source of information to intelligent mobile systems. However, most of existing works on trajectory representation deal only with basic characteristics of trajectories, such as space and time, while these attributes may be not enough to provide the required information to intelligent systems. We observe that the analysis of other characteristics (e.g. speed and acceleration) of mobile objects enriches the trajectory description as well as open opportunities to novel applications. However, the dynamic nature of these characteristics brings several challenges related to the preprocessing and analysis of raw data. In this paper, we show how these additional characteristics may be integrated in trajectory modeling. We address the problem of representing trajectories with qualitative descriptions of movement modeled as an ontology. We validate our approach with real data from a sport tracking application

python-visualization/folium: v0.3.0

0.3.0 Switched to leaflet 1.0.1 (juoceano #531 and ocefpaf #535) Added continuous_world, world_copy_jump, and no_wrap options (ocefpaf #508) Update font-awesome to 4.6.3 (ocefpaf #478) Added text path (talespaiva #451 and ocefpaf #474) More options added to LayerControl (qingkaikong #473) More options added to fullscreen plugin (qingkaikong #468) Added ColorLine object (bibmartin #449) Added highlight function to GeoJSON, and Chrorpleth (JoshuaCano #341) Added fullscreen plugin (sanga #437) Added smooth_factoroption to GeoJSON, TopoJSON and Choropleth (JamesGardiner #428) Map object now accepts Leaflet global switches (sgvandijk #424) Added weight option to CircleMarker (palewire #581) Bug Fixes Fixed image order (juoceano #536) Fixed Icon rotation (juoceano #530 and sseemayer #527) Fixed MIME type (text/plain) is not executable (talespaiva #440) Update Travis-CI testing to incorporate branca and fix notebook tests (ocefpaf #436) Removed MultiPolyLine and MultiPolygon, both are handled by PolyLine and PolyLine in leaflet 1.0.* (ocefpaf #554) Removed deprecated MapQuest tiles (HashCode55 #562