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A spatiotemporal data model and an index structure for computational time geography / Bi Yu Chen in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 550 - 583 Titre : A spatiotemporal data model and an index structure for computational time geography Type de document : Article/Communication Auteurs : Bi Yu Chen, Auteur ; Yu-Bo Luo, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 550 - 583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche hiérarchique [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] requête spatiotemporelle [Termes IGN] stockage de données [Termes IGN] Time-geography Résumé : (auteur) The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. Numéro de notice : A2023-153 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2128192 Date de publication en ligne : 03/10/2023 En ligne : https://doi.org/10.1080/13658816.2022.2128192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102836 [article]

A lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1849 - 1872 Titre : A lightweight ensemble spatiotemporal interpolation model for geospatial data Type de document : Article/Communication Auteurs : Shifen Cheng, Auteur ; Peng Peng, Auteur ; Feng Lu, Auteur Année de publication : 2020 Article en page(s) : pp 1849 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] coefficient de corrélation [Termes IGN] distance pondérée [Termes IGN] données localisées [Termes IGN] erreur absolue [Termes IGN] interpolation spatiale [Termes IGN] lissage de données [Termes IGN] modélisation spatio-temporelle [Termes IGN] requête spatiotemporelle Résumé : (auteur) Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations. Numéro de notice : A2020-484 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725016 Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95651 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica, vol 21 n° 2 (April - June 2017)  

Public [article]  inGeoinformatica > vol 21 n° 2 (April - June 2017) . - pp 175 - 208 Titre : Panda∗: A generic and scalable framework for predictive spatio-temporal queries Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; Mohamed Ali, Auteur ; Mohamed F. Mokbel, Auteur Année de publication : 2017 Article en page(s) : pp 175 - 208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] environnement de développement [Termes IGN] espace euclidien [Termes IGN] gestion de trafic [Termes IGN] objet mobile [Termes IGN] plus proche voisin, algorithme du [Termes IGN] prédiction [Termes IGN] requête spatiotemporelle Résumé : (Auteur) Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda∗, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda∗ distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda∗ is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda∗ employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects’ historical trajectories. (3) Panda∗ is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda∗ deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda∗ provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda∗ in evaluating a massive volume of predictive queries over large numbers of moving objects. Numéro de notice : A2017-068 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0284-8 En ligne : http://dx.doi.org/10.1007/s10707-016-0284-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84295 [article]

A virtual globe-oriented visualization method for 3D meteorological fields / Jing Chen in Geomatics and Information Science of Wuhan University, vol 41 n° 12 (December 2016)  

Public [article]  inGeomatics and Information Science of Wuhan University > vol 41 n° 12 (December 2016) Titre : A virtual globe-oriented visualization method for 3D meteorological fields Type de document : Article/Communication Auteurs : Jing Chen, Auteur ; Cheng Zou, Auteur ; Wumeng Huang, Auteur ; Boyang Liu, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données localisées 3D [Termes IGN] données météorologiques [Termes IGN] globe virtuel [Termes IGN] requête spatiotemporelle [Termes IGN] visualisation de données [Termes IGN] voxel [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Meteorological fields represent a multidimensional dynamic environment; therefore visualization is a means study inherent regularities in these phenomena. In this paper, aiming to visualize the worldwide, multi-dimensional, multi-scale, massive characteristics of meteorological field data, a voxel object-oriented data model and a voxel based multi-level indexing mechanism were designed. A visualization method for 3D meteorological fields is proposed, including an arrow model based visualization method for vector fields and improved 3D texture-mapping based volume rendering method for scalar fields. Visualization and spatio-temporal retrieval experiments on a Virtual Globe platform were carried out to demonstrate the feasibility and effectiveness of the proposed methods. Numéro de notice : A2016--123 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.13203/j.whugis20140520 En ligne : https://doi.org/10.13203/j.whugis20140520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84962 [article]

Kinematic interpolation of movement data / Jed A. Long 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 854 - 868 Titre : Kinematic interpolation of movement data Type de document : Article/Communication Auteurs : Jed A. Long, Auteur Année de publication : 2016 Article en page(s) : pp 854 - 868 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] interpolation [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] objet mobile [Termes IGN] positionnement cinématique [Termes IGN] requête spatiotemporelle Résumé : (Auteur) Mobile tracking technologies are facilitating the collection of increasingly large and detailed data sets on object movement. Movement data are collected by recording an object’s location at discrete time intervals. Often, of interest is to estimate the unknown position of the object at unrecorded time points to increase the temporal resolution of the data, to correct erroneous or missing data points, or to match the recorded times between multiple data sets. Estimating an object’s unknown location between known locations is termed path interpolation. This paper introduces a new method for path interpolation termed kinematic interpolation. Kinematic interpolation incorporates object kinematics (i.e. velocity and acceleration) into the interpolation process. Six empirical data sets (two types of correlated random walks, caribou, cyclist, hurricane and athlete tracking data) are used to compare kinematic interpolation to other interpolation algorithms. Results showed kinematic interpolation to be a suitable interpolation method with fast-moving objects (e.g. the cyclist, hurricane and athlete tracking data), while other algorithms performed best with the correlated random walk and caribou data. Several issues associated with path interpolation tasks are discussed along with potential applications where kinematic interpolation can be useful. Finally, code for performing path interpolation is provided (for each method compared within) using the statistical software R. Numéro de notice : A2016-286 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1081909 En ligne : https://doi.org/10.1080/13658816.2015.1081909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80865 [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

Towards fusing uncertain location data from heterogeneous sources / Bing Zhang in Geoinformatica, vol 20 n° 2 (April - June 2016)  

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An advanced systematic literature review on spatiotemporal analyses of twitter-data / Enrico Steiger in Transactions in GIS, vol 19 n° 6 (December 2015)  

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Les états spatiotemporels d’existence et de présence : Vers une définition des relations entre objets absents ou inexistants / Pierre Hallot in Revue internationale de géomatique, vol 25 n° 2 (juin - août 2015)  

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Group spatiotemporal pattern queries / M. Sakr in Geoinformatica, vol 18 n° 4 (October 2014)  

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Thinking about space-time connections : spatiotemporal scheduling of individual activities / Kathleen Stewart in Transactions in GIS, vol 17 n° 6 (December 2013)  

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Parallel indexing technique for spatio-temporal data / Zhenwen He in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)  

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Continuous aggregate nearest neighbor queries / H. Elmongui in Geoinformatica, vol 17 n° 1 (January 2013)  

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Une approche ontologique pour la structuration de données spatio-temporelles de trajectoires : Application à l’étude des déplacements de mammifères marins / W. Mefteh in Revue internationale de géomatique, vol 22 n° 1 (mars - mai 2012)  

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A data model and query language for spatio-temporal decision support / L. Gomez in Geoinformatica, vol 15 n° 3 (July 2011)  

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Spatiotemporal pattern queries / M. Sakr in Geoinformatica, vol 15 n° 3 (July 2011)  

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