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Titre : Traffic prediction and analysis using a big data and visualisation approach Type de document : Article/Communication Auteurs : Declan McHugh, Auteur Editeur : Leeds [Royaume-Uni] : University of Leeds Année de publication : 2015 Conférence : GISRUK 2015, 23th GIS Research UK annual conference 15/04/2015 17/04/2015 Leeds Royaume-Uni open access proceedings Importance : pp 408 - 420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] Dublin (Irlande ; ville)
[Termes IGN] exploration de données géographiques
[Termes IGN] modèle de simulation
[Termes IGN] prévision
[Termes IGN] régression multiple
[Termes IGN] trafic routier
[Termes IGN] Twitter
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This abstract illustrates an approach of using big data, visualisation and data mining techniques used to predict and analyse traffic. The objective is to understand Traffic patterns in Dublin City. The prediction model was used as an estimator to identify unusual traffic patterns. The generic model was designed using data mining techniques, multivariate regression algorithms, ARIMA and visually correlated with real-time traffic tweets. Using the prediction model and tweet event detection. The result is a high-performance web application containing over 500,000,000,000 traffic observations that produce analytical dashboard providing traffic prediction and analysis. Numéro de notice : C2015-049 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83863 Documents numériques
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Traffic prediction and analysisAdobe Acrobat PDF Temporal accuracy in urban growth forecasting : a study using the SLEUTH model / Gargi Chaudhuri in Transactions in GIS, vol 18 n° 2 (April 2014)
[article]
Titre : Temporal accuracy in urban growth forecasting : a study using the SLEUTH model Type de document : Article/Communication Auteurs : Gargi Chaudhuri, Auteur ; Keith C. Clarke, Auteur Année de publication : 2014 Article en page(s) : pp 302 - 320 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aide à la décision
[Termes IGN] changement d'utilisation du sol
[Termes IGN] croissance urbaine
[Termes IGN] Italie
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle de simulation
[Termes IGN] précision des données
[Termes IGN] prévisionRésumé : (Auteur) This study attempts to establish multi-temporal accuracy of the predicted maps produced by a land use change simulation model over time. Validation of the forecasted results is an essential part of predictive modeling and it becomes even more important when the models are used for decision making purposes. The present study uses a popular land use change model called SLEUTH to investigate the temporal trend of accuracy of the predicted maps. The study first investigates the trend of accuracy of the predicted maps from the immediate future to the distant future. Secondly, it investigates the impact of the prediction date range on the accuracy of the predicted maps. The objectives are tested for the city of Gorizia (Italy) using three sets of map comparison techniques, Kappa coefficients, Kappa Simulation and quantity disagreement and allocation disagreement. Results show that, in addition to the model's performance, the decrease in the accuracy of the predicted maps is dependent on factors such as urban history, uncertainty of input data and accuracy of reference maps. Numéro de notice : A2014-169 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12047 Date de publication en ligne : 12/09/2013 En ligne : https://doi.org/10.1111/tgis.12047 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33074
in Transactions in GIS > vol 18 n° 2 (April 2014) . - pp 302 - 320[article]A Temporal variant-invariant validation approach for agent-based models of landscape dynamics / Christopher Bone in Transactions in GIS, vol 18 n° 2 (April 2014)
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Titre : A Temporal variant-invariant validation approach for agent-based models of landscape dynamics Type de document : Article/Communication Auteurs : Christopher Bone, Auteur ; Bart Johnson, Auteur ; Max Nielsen-Pincus, Auteur ; Eric Sproles, Auteur ; John Bolte, Auteur Année de publication : 2014 Article en page(s) : pp 161 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de variance
[Termes IGN] modèle de simulation
[Termes IGN] Oregon (Etats-Unis)
[Termes IGN] simulation
[Termes IGN] système multi-agentsRésumé : (Auteur) Agent-based modeling provides a means for addressing the way human and natural systems interact to change landscapes over time. Until recently, evaluation of simulation models has focused on map comparison techniques that evaluate the degree to which predictions match real-world observations. However, methods that change the focus of evaluation from patterns to processes have begun to surface; that is, rather than asking if a model simulates a correct pattern, models are evaluated on their ability to simulate a process of interest. We build on an existing agent-based modeling validation method in order to present a temporal variant-invariant analysis (TVIA). The enhanced method, which focuses on analyzing the uncertainty in simulation results, examines the degree to which outcomes from multiple model runs match some reference to how land use parcels make the transition from one land use class to another over time. We apply TVIA to results from an agent-based model that simulates the relationships between landowner decisions and wildfire risk in the wildland-urban interface of the southern Willamette Valley, Oregon, USA. The TVIA approach demonstrates a novel ability to examine uncertainty across time to provide an understanding of how the model emulates the system of interest. Numéro de notice : A2014-163 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12016 Date de publication en ligne : 09/06/2013 En ligne : https://doi.org/10.1111/tgis.12016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33068
in Transactions in GIS > vol 18 n° 2 (April 2014) . - pp 161 - 182[article]Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
[article]
Titre : Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding Type de document : Article/Communication Auteurs : Junwei Han, Auteur ; Peicheng Zhou, Auteur ; Dingwen Zhang, Auteur ; Gong Cheng, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 37 - 48 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage automatique
[Termes IGN] détection de cible
[Termes IGN] données localisées
[Termes IGN] image optique
[Termes IGN] matrice d'information de Fischer
[Termes IGN] modèle de simulation
[Termes IGN] reliefRésumé : (Auteur) Automatic detection of geospatial targets in cluttered scenes is a profound challenge in the field of aerial and satellite image analysis. In this paper, we propose a novel practical framework enabling efficient and simultaneous detection of multi-class geospatial targets in remote sensing images (RSI) by the integration of visual saliency modeling and the discriminative learning of sparse coding. At first, a computational saliency prediction model is built via learning a direct mapping from a variety of visual features to a ground truth set of salient objects in geospatial images manually annotated by experts. The output of this model can predict a small set of target candidate areas. Afterwards, in contrast with typical models that are trained independently for each class of targets, we train a multi-class object detector that can simultaneously localize multiple targets from multiple classes by using discriminative sparse coding. The Fisher discrimination criterion is incorporated into the learning of a dictionary, which leads to a set of discriminative sparse coding coefficients having small within-class scatter and big between-class scatter. Multi-class classification can be therefore achieved by the reconstruction error and discriminative coding coefficients. Finally, the trained multi-class object detector is applied to those target candidate areas instead of the entire image in order to classify them into various categories of target, which can significantly reduce the cost of traditional exhaustive search. Comprehensive evaluations on a satellite RSI database and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness and efficiency of the proposed work. Numéro de notice : A2014-123 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33028
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 37 - 48[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014031 RAB Revue Centre de documentation En réserve L003 Disponible A real-time MODIS vegetation product for land surface and numerical weather prediction models / Jonathan L. Case in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)
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Titre : A real-time MODIS vegetation product for land surface and numerical weather prediction models Type de document : Article/Communication Auteurs : Jonathan L. Case, Auteur ; Frank J. Lafontaine, Auteur ; Jordan R. Bell, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 1772 - 1786 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Etats-Unis
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] modèle de simulation
[Termes IGN] modèle météorologiqueRésumé : (Auteur) A technique is presented to produce real-time, daily vegetation composites at 0.01° resolution (~1 km) over the Conterminous United States (CONUS) for use in the NASA Land Information System (LIS) and weather prediction models. Green vegetation fraction (GVF) is derived from direct-broadcast swaths of normalized difference vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observing System satellites. The real-time data and increased resolution compared to the 0.144° (~16 km) resolution monthly GVF climatology in community models result in an improved representation of vegetation in high-resolution models, especially in complex terrain. The MODIS GVF fields show seasonal variations that are similar to the community model climatology, and respond realistically to temperature and precipitation anomalies. The wet spring and summer 2010 over the U.S. Plains led to higher regional GVF than in the climatology. The GVF substantially decreased over the U.S. Southern Plains from 2010 to 2011, consistent with the transition to extreme drought in summer 2011. LIS simulations depict substantial sensitivity to the MODIS GVF, with regional changes in heat fluxes around 100 Wm-2 over the northern U.S. in June 2010. CONUS LIS simulations during the 2010 warm season indicate that the larger MODIS GVF in the western U.S. led to higher latent heat fluxes and initially lower sensible heat fluxes, with a net drying effect on the soil. With time, the drier soil eventually lead to higher mean sensible heat fluxes such that the total surface energy output increased by late summer 2010 over the western U.S. A sensitivity simulation of a severe weather event using real-time MODIS GVF data results in systematic changes to low-level temperature, moisture, and instability fields, and improves the evolution of simulated precipitation. Numéro de notice : A2014-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2255059 En ligne : https://doi.org/10.1109/TGRS.2013.2255059 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33018
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 3 (March 2014) . - pp 1772 - 1786[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Objets géographiques et processus de changement / Hélène Mathian (2014)PermalinkCity shrinkage simulation: A case study of Katsuura City, Japan / Jianping Gu in Revue internationale de géomatique, vol 23 n° 3 - 4 (septembre 2013 - février 2014)PermalinkUsing virtual reality and percolation theory to visualize fluid flow in porous media / Carlos Magno De Lima in Geoinformatica, vol 17 n° 4 (October 2013)PermalinkAssessing the performance of linear feature models : An approach to computational inference / Eufenio Y. Arima in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 9 (September 2013)PermalinkNumerical modelling of post-seismic rupture propagation after the Sumatra 26.12.2004 earthquake constrained by GRACE gravity data / Valentin O. Mikhailov in Geophysical journal international, vol 194 n° 2 (August 2013)PermalinkEmpirical evidence on agricultural land-use change in Sardinia, Italy, from GIS-based analysis and a Tobit model / Corrado Zoppi in Cartographica, vol 47 n° 4 (December 2012)PermalinkWaiting to know the future: A SLEUTH model forecast of urban growth with real data / G. Manca in Cartographica, vol 47 n° 4 (December 2012)PermalinkLe cycle de l'eau dans le système de mousson d'Afrique de l'Ouest / Christophe Peugeot in La Météorologie, n° spéc (octobre 2012)PermalinkUn modèle de prévision de rendement de la canne à sucre basé sur des images satellitaires SPOT : l'exemple de la Réunion / N. Boyer in Revue Française de Photogrammétrie et de Télédétection, n° 197 (Juin 2012)PermalinkLa modélisation d’accompagnement : une forme particulière de géoprospective / Matthieu Etienne in Espace géographique, vol 41 n° 2 (avril - juin 2012)Permalink