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Termes IGN > informatique > intelligence artificielle > ingénierie des connaissances > découverte de connaissances > exploration de données > exploration de données géographiques
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A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)
[article]
Titre : A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data Type de document : Article/Communication Auteurs : Qunying Huang, Auteur ; Guido Cervone, Auteur ; Guiming Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 23 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] caractérisation
[Termes IGN] catastrophe naturelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] exploration de données géographiques
[Termes IGN] exploration de texte
[Termes IGN] image numérique
[Termes IGN] informatique en nuage
[Termes IGN] inondation
[Termes IGN] intégration de données
[Termes IGN] interface web
[Termes IGN] prototype
[Termes IGN] tempête
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Social media streams and remote sensing data have emerged as new sources for tracking disaster events, and assessing their damages. Previous studies focus on a case-by-case approach, where a specific event was first chosen and filtering criteria (e.g., keywords, spatiotemporal information) are manually designed and used to retrieve relevant data for disaster analysis. This paper presents a framework that synthesizes multi-sourced data (e.g., social media, remote sensing, Wikipedia, and Web), spatial data mining and text mining technologies to build an architecturally resilient and elastic solution to support disaster analysis of historical and future events. Within the proposed framework, Wikipedia is used as a primary source of different historical disaster events, which are extracted to build an event database. Such a database characterizes the salient spatiotemporal patterns and characteristics of each type of disaster. Additionally, it can provide basic semantics, such as event name (e.g., Hurricane Sandy) and type (e.g., flooding) and spatiotemporal scopes, which are then tuned by the proposed procedures to extract additional information (e.g., hashtags for searching tweets), to query and retrieve relevant social media and remote sensing data for a specific disaster. Besides historical event analysis and pattern mining, the cloud-based framework can also support real-time event tracking and monitoring by providing on-demand and elastic computing power and storage capabilities. A prototype is implemented and tested with data relative to the 2011 Hurricane Sandy and the 2013 Colorado flooding. Numéro de notice : a2017-430 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2017.06.004 En ligne : https://doi.org/10.1016/j.compenvurbsys.2017.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86330
in Computers, Environment and Urban Systems > vol 66 (November 2017) . - pp 23 - 37[article]Social Distance metric: from coordinates to neighborhoods / Vagan Terziyan in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)
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Titre : Social Distance metric: from coordinates to neighborhoods Type de document : Article/Communication Auteurs : Vagan Terziyan, Auteur Année de publication : 2017 Article en page(s) : pp 2401 - 2426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] base de données localisées
[Termes IGN] classification
[Termes IGN] distance
[Termes IGN] exploration de données géographiques
[Termes IGN] géographie sociale
[Termes IGN] interpolation
[Termes IGN] métrique
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] système d'information géographique
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) Choice of a distance metric is a key for the success in many machine learning and data processing tasks. The distance between two data samples traditionally depends on the values of their attributes (coordinates) in a data space. Some metrics also take into account the distribution of samples within the space (e.g. local densities) aiming to improve potential classification or clustering performance. In this paper, we suggest the Social Distance metric that can be used on top of any traditional metric. For a pair of samples x and y, it averages the two numbers: the place (rank), which sample y holds in the list of ordered nearest neighbors of x; and vice versa, the rank of x in the list of the nearest neighbors of y. Average is a contraharmonic Lehmer mean, which penalizes the difference between the numbers by giving values greater than the Arithmetic mean for the unequal arguments. We consider normalized average as a distance function and we prove it to be a metric. We present several modifications of such metric and show that their properties are useful for a variety of classification and clustering tasks in data spaces or graphs in a Geographic Information Systems context and beyond. Numéro de notice : A2017-701 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1367796 En ligne : https://doi.org/10.1080/13658816.2017.1367796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88082
in International journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2401 - 2426[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017062 RAB Revue Centre de documentation En réserve L003 Disponible An iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls / A. Mozas-Calvache in Survey review, vol 49 n° 355 (October 2017)
[article]
Titre : An iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls Type de document : Article/Communication Auteurs : A. Mozas-Calvache, Auteur ; Francisco Javier Ariza-López, Auteur Année de publication : 2017 Article en page(s) : pp 277 - 284 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] axe médian
[Termes IGN] classification par nuées dynamiques
[Termes IGN] exploration de données géographiques
[Termes IGN] incertitude géométrique
[Termes IGN] itération
[Termes IGN] précision de localisation
[Termes IGN] trace GPS
[Termes IGN] traitement de données localiséesRésumé : (Auteur) This paper describes a new method of data mining for determining a 3D mean axis from a set of surveyed Global Navigation Satellite Systems traces. The purpose is to obtain a mean axis and its uncertainty in order for them to be used in line-based positional controls. The method is based on an iterative process of condensation. The final mean axis is selected when a determined level of accuracy is achieved. So the method provides a relative positional accuracy value of the final solution. The example developed in this study demonstrates the viability of this method and allows analysis of the initial size of the set needed in order to achieve a final accuracy. Using real data, the proposed methodology has also been compared with the K-means methodology in order to analyse its advantages and conditions of use. The results have demonstrated an improvement in accuracy and geometrical definition of the axis obtained. Numéro de notice : A2017-551 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/00396265.2016.1171956 En ligne : https://doi.org/10.1080/00396265.2016.1171956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86612
in Survey review > vol 49 n° 355 (October 2017) . - pp 277 - 284[article]Discovering non-compliant window co-occurrence patterns / Reem Y. Ali in Geoinformatica, vol 21 n° 4 (October - December 2017)
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Titre : Discovering non-compliant window co-occurrence patterns Type de document : Article/Communication Auteurs : Reem Y. Ali, Auteur ; Venkata M. V. Gunturi, Auteur ; Andrew J. Kotz, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 829 - 866 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] exploration de données géographiques
[Termes IGN] géostatistique
[Termes IGN] matrice de co-occurrence
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] transportRésumé : (Auteur) Given a set of trajectories annotated with measurements of physical variables, the problem of Non-compliant Window Co-occurrence (NWC) pattern discovery aims to determine temporal signatures in the explanatory variables which are highly associated with windows of undesirable behavior in a target variable. NWC discovery is important for societal applications such as eco-friendly transportation (e.g. identifying engine signatures leading to high greenhouse gas emissions). Challenges of designing a scalable algorithm for NWC discovery include the non-monotonicity of popular spatio-temporal statistical interest measures of association such as the cross-K function which renders the anti-monotone pruning based algorithms (e.g. Apriori) inapplicable for such interest measures. In our preliminary work, we proposed two upper bounds for the cross-K function and a top-down multi-parent tracking approach that uses these bounds for filtering out uninteresting candidate patterns and then applies a minimum support (i.e. frequency) threshold as a post-processing step to filter out chance patterns. In this paper, we propose a novel bi-directional pruning approach (BDNMiner) that combines top-down pruning based on the cross-K function threshold with bottom-up pruning based on the minimum support threshold to efficiently mine NWC patterns. Case studies with real world engine data demonstrates the ability of the proposed approach to discover patterns which are interesting to engine scientists. Experimental evaluation on real-world data show that the proposed approach yields substantial computational savings compared to prior work. Numéro de notice : A2017-605 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0289-3 En ligne : https://doi.org/10.1007/s10707-016-0289-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86915
in Geoinformatica > vol 21 n° 4 (October - December 2017) . - pp 829 - 866[article]Aggregation-based information retrieval system for geospatial data catalogs / Javier Lacasta in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)
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Titre : Aggregation-based information retrieval system for geospatial data catalogs Type de document : Article/Communication Auteurs : Javier Lacasta, Auteur ; Javier Lopez-Pellicer, Auteur ; Borja Espejo-García, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1583 - 1605 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] accès aux données
[Termes IGN] catalogue de données localisées
[Termes IGN] Espagne
[Termes IGN] exploration de données géographiques
[Termes IGN] infrastructure régionale de données localisées
[Termes IGN] métadonnées
[Termes IGN] métadonnées géographiques
[Termes IGN] recherche d'information géographique
[Termes IGN] requête (informatique)
[Termes IGN] service web géographiqueRésumé : (Auteur) Geospatial data catalogs enable users to discover and access geographical information. Prevailing solutions are document oriented and fragment the spatial continuum of the geospatial data into independent and disconnected resources described through metadata. Due to this, the complete answer for a query may be scattered across multiple resources, making its discovery and access more difficult. This paper proposes an improved information retrieval process for geospatial data catalogs that aggregates the search results by identifying the implicit spatial/thematic relations between the metadata records of the resources. These aggregations are constructed in such a way that they match better the user query than each resource individually. Numéro de notice : A2017-313 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1319949 En ligne : http://dx.doi.org/10.1080/13658816.2017.1319949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85367
in International journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1583 - 1605[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017042 RAB Revue Centre de documentation Revues en salle Disponible PerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkExploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkEvaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem / Jonathan K. Nelson in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)PermalinkA review of volunteered geographic information quality assessment methods / Hansi Senaratne in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)PermalinkPermalinkMining spatiotemporal co-occurrence patterns in non-relational databases / Berkay Aydin in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkOn discovering co-location patterns in datasets : a case study of pollutants and child cancers / Jundong Li in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkModeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkUnsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning / Jari Vauhkonen in Forestry, an international journal of forest research, vol 89 n° 4 (August 2016)PermalinkGrid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)PermalinkIntegrating geo web services for a user driven exploratory analysis / Simon Moncrieff in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkImage based geo-localization in the Alps / Olivier Saurer in International journal of computer vision, vol 116 n° 3 (February 2016)PermalinkPersonal mobility pattern mining and anomaly detection in the GPS era / Dong-He Shih in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)PermalinkQGIS 2 cookbook / Alex Mandel (2016)PermalinkRevisiting cartography : towards identifying and developing a modern and comprehensive framework / Melih Basaraner in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkCo-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)PermalinkSENTERRITOIRE pour la détection d’opinions liées à l’aménagement d’un territoire / Eric Kergosien in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)PermalinkGeo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan / Mohamed Bakillah in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)PermalinkMining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference / Jun Li in Transactions in GIS, vol 19 n° 1 (February 2015)Permalink