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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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Domain-driven co-location mining / Frédéric Flouvat in Geoinformatica, vol 19 n° 1 (January - March 2015)
[article]
Titre : Domain-driven co-location mining Type de document : Article/Communication Auteurs : Frédéric Flouvat, Auteur ; Jean-François N’guyen Van Soc, Auteur ; Elise Desmier, Auteur ; Nazha Selmaoui-Folcher, Auteur Année de publication : 2015 Article en page(s) : pp 147 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] co-positionnement
[Termes IGN] érosion
[Termes IGN] exploration de données géographiques
[Termes IGN] géologie
[Termes IGN] PostGIS
[Termes IGN] sol
[Termes IGN] système expert
[Termes IGN] visualisation cartographiqueRésumé : (auteur) Co-location mining is a classical problem in spatial pattern mining. Considering a set of boolean spatial features, the goal is to find subsets of features frequently located together. It has wide applications in environmental management, public safety, transportation or tourism. These last years, many algorithms have been proposed to extract frequent co-locations. However, most solutions do a “data-centered knowledge discovery” instead of a “expert-centered knowledge discovery”. Successfully providing useful and interpretable patterns to experts is still an open problem. In this setting, we propose a domain-driven co-location mining approach that combines constraint-based mining and cartographic visualization. Experts can push new domain constraints into the mining algorithm, resulting in more relevant patterns and more efficient extraction. Then, they can visualize solutions using a new concise and intuitive cartographic visualization of co-locations. Using this original visualization approach, they identify new interesting patterns, and use uninteresting ones to define new constraints and refine their analysis. These proposals have been integrated into a prototype based on PostGIS geographic information system. Experiments have been done using a real geological datasets studying soil erosion, and results have been validated by a domain expert. Numéro de notice : A2015-487 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0209-3 En ligne : https://doi.org/10.1007/s10707-014-0209-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77250
in Geoinformatica > vol 19 n° 1 (January - March 2015) . - pp 147 - 183[article]
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
en open access
Traffic prediction and analysisAdobe Acrobat PDF A polygon-based clustering and analysis framework for mining spatial datasets / Sujing Wang in Geoinformatica, vol 18 n° 3 (July 2014)
[article]
Titre : A polygon-based clustering and analysis framework for mining spatial datasets Type de document : Article/Communication Auteurs : Sujing Wang, Auteur ; Christoph F. Eick, Auteur Année de publication : 2014 Article en page(s) : pp 569 - 594 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] exploration de données géographiques
[Termes IGN] polygone
[Termes IGN] regroupement de donnéesRésumé : (Auteur) Polygons provide natural representations for many types of geospatial objects, such as countries, buildings, and pollution hotspots. Thus, polygon-based data mining techniques are particularly useful for mining geospatial datasets. In this paper, we propose a polygon-based clustering and analysis framework for mining multiple geospatial datasets that have inherently hidden relations. In this framework, polygons are first generated from multiple geospatial point datasets by using a density-based contouring algorithm called DCONTOUR. Next, a density-based clustering algorithm called Poly-SNN with novel dissimilarity functions is employed to cluster polygons to create meta-clusters of polygons. Finally, post-processing analysis techniques are proposed to extract interesting patterns and user-guided summarized knowledge from meta-clusters. These techniques employ plug-in reward functions that capture a domain expert's notion of interestingness to guide the extraction of knowledge from meta-clusters. The effectiveness of our framework is tested in a real-world case study involving ozone pollution events in Texas. The experimental results show that our framework can reveal interesting relationships between different ozone hotspots represented by polygons; it can also identify interesting hidden relations between ozone hotspots and several meteorological variables, such as outdoor temperature, solar radiation, and wind speed. Numéro de notice : A2014-501 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0190-2 Date de publication en ligne : 30/08/2013 En ligne : https://doi.org/10.1007/s10707-013-0190-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74093
in Geoinformatica > vol 18 n° 3 (July 2014) . - pp 569 - 594[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Combining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)
[article]
Titre : Combining Geo-SOM and hierarchical clustering to explore geospatial data Type de document : Article/Communication Auteurs : Chen-Chieh Feng, Auteur ; Yi-Chen Wang, Auteur ; Chih-Yuan Chen, Auteur Année de publication : 2014 Article en page(s) : pp 125 - 146 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse combinatoire (maths)
[Termes IGN] analyse de groupement
[Termes IGN] carte de Kohonen
[Termes IGN] données localisées
[Termes IGN] exploration de données géographiques
[Termes IGN] visualisationRésumé : (Auteur) Geo-SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo-SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo-SOM and hierarchical clustering to tackle this problem. Geo-SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo-SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo-SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data. Numéro de notice : A2014-068 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12025 Date de publication en ligne : 16/09/2013 En ligne : https://doi.org/10.1111/tgis.12025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32973
in Transactions in GIS > vol 18 n° 1 (February 2014) . - pp 125 - 146[article]
Titre : Automatische Interpretation von Semantik aus digitalen Karten im World Wide Web Type de document : Thèse/HDR Auteurs : Fen Luo, Auteur ; Dieter Fritsch, Directeur de thèse ; Ralf Bill, Directeur de thèse Editeur : Stuttgart : Institüt für Photogrammetrie der Universität Stuttgart Année de publication : 2014 Note générale : bibliographie
Von der Fakultät Luft- und Raumfahrttechnik und Geodäsie der Universität Stuttgart zur Erlangung der Würde eines Doktors der Ingenieurwissenschaften (Dr.-Ing.) genehmigte AbhandlungLangues : Allemand (ger) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] carte de Kohonen
[Termes IGN] données maillées
[Termes IGN] données vectorielles
[Termes IGN] échelle cartographique
[Termes IGN] exploration de données géographiques
[Termes IGN] objet géographique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] toile d'araignée mondialeRésumé : (auteur) On the Internet there are innumerable spatial data representing different sections of the world in form of raster and vector maps. The information contained in these maps is not automatically discoverable, since it is encoded by means of certain map elements. Its semantics is not explicit unless interpreted by an observer. However, the map information can be interpreted by not only humans but also ma-chines. This already requires the large amount of data to be interpreted. We are going to summarize the automatic derivation of semantics from the maps in terms of automatic map interpretation. It involves a process of making the implicit information of a map inventory explicit. For this purpose we present the map interpretation as solutions.
The map interpretation of the current study is done with vector maps what can be found on the internet. For the targeted search of vector maps of the internet, a web crawler is specially developed. The web crawler is a search engine that specifically looks for vector maps. For this, exclusively the shapefile format is sought, which has become a standard format in the GIS environment and in which the vector maps are usually stored. In order to find shapefiles as many as possible, the search is carried out on servers where the probability of finding shapefiles is high. These servers were previously found through the keyword “shapefile download” by Google search.
The maps interpretation includes methods of interpretation of the map objects, of the map types, and of the map scale. First, we will introduce the method of interpreting the map objects. Our aim is to automatically detect the objects based on their specific characteristics. The object recognition is based on self-organizing map (SOM) that is borrowed from artificial intelligence. The map objects are clas-sified into, for example, building floor plan and road network. Its own characteristics should be found for each class and brought in one of the accessible forms of SOM, in this case, a parameter vector. The parameter vectors form the input patterns that are learned in the training phase of SOM. After the input patterns of all object classes of SOM have been learned, the parameter vector is evaluated for each of the present objects on the map and given to the SOM. By the previously successful learning of the input pattern, the objects can be assigned based on each of their calculated parameter vectors of the corresponding object class.
The interpretation of map type is presented as another method. Maps are categorized into different types according to their substantive content and purpose, such as river maps, road maps, contour maps, etc. As for the interpretation of objects, SOM is used here. Hence the input patterns will also be learned which represent the geometric characteristics of the map types. The characteristics arise from both the structure of individual objects and the topology between objects on a map. Now, with a given map in the SOM, the SOM recognizes the appropriate map type according to the learned input pattern. In addition, one obtains the filenames of the maps as well as the content of the website where the map was found. In the present thesis we also investigated how this additional information can help in the interpretation of map type.
The automatic interpretation of the map scale is a further method in addition to the interpretation of the map objects and map types, which is discussed in the present thesis. The interpretation of the map scale is implemented in two ways: the multi-representation and the details grade. In the former case, the scale of the relevant representation can be derived, where an identical object in different realistic representations on the map is shown; while in the latter case, the scale is derived from the details grade, on the basis of the fact that maps with different scales are displayed on different levels of details.Numéro de notice : 17347 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère DOI : sans En ligne : http://doi.org/10.18419/opus-3960 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83706 PermalinkUsing spatial data support for reducing uncertainty in geospatial applications / T. Hong in Geoinformatica, vol 18 n° 1 (January 2014)PermalinkContent zooming and information exploration for web and mobile maps. Adaptation of real-time map generalisation to the information seeking strategies of web and mobile users / Pia Bereuter in Revue internationale de géomatique, vol 23 n° 3 - 4 (septembre 2013 - février 2014)PermalinkA data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level / Fuqun Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 84 (October 2013)PermalinkA spatial-based KDD process to better manage the river water quality / Hugo Alatrista-Salas in Revue internationale de géomatique, vol 23 n° 3 - 4 (septembre 2013 - février 2014)PermalinkApproche exploratoire SIG pour l'identification des piézomètres représentatifs d'une relation nappe/rivière / Alexandre Brugeron in Géomatique expert, n° 94 (01/09/2013)PermalinkA methodological framework for researching the usability of the space-time cube / Irma Kveladze in Cartographic journal (the), vol 50 n° 3 (August 2013)PermalinkTemporal uncertainty in a small area open geodemographic classification / Christopher G. Gale in Transactions in GIS, vol 17 n° 4 (August 2013)PermalinkChange detection from remotely sensed images: From pixel-based to object-based approaches / Masroor Hussain in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)PermalinkEvaluating the “geographical awareness” of individuals: an exploratory analysis of Twitter data / Chen Xu in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)PermalinkMotifs spatio-temporels de trajectoires, de l'extraction à la détection de comportements inhabituels / Laurent Etienne in Cartes & Géomatique, n° 215 (mars 2013)PermalinkSupport vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)PermalinkPermalinkClassification et évolution des tissus urbains à partir de données vectorielles / Anne Puissant in Revue internationale de géomatique, vol 21 n° 4 (décembre 2011 – février 2012)PermalinkRecherche de motifs et cartographie des surfaces agricoles : Des relevés de terrain aux données satellitaires : application au Mali / E. Vintrou in Revue internationale de géomatique, vol 21 n° 4 (décembre 2011 – février 2012)PermalinkThe development of a web-based demographic data extraction tool for population monitoring / T. Chow in Transactions in GIS, vol 15 n° 4 (August 2011)PermalinkAutomatic interpretation of digital maps / Volker Walter in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)PermalinkControlling patterns of geospatial phenomena / Tomasz F. Stepinski in Geoinformatica, vol 15 n° 3 (July 2011)PermalinkWeka-STPM: a software architecture and prototype for semantic trajectory data mining and visualization / Vania Bogorny in Transactions in GIS, vol 15 n° 2 (April 2011)PermalinkUtilisation d'une ontologie du domaine pour la découverte du contenu de bases de données géographiques / Ammar Mechouche in Revue des Nouvelles Technologies de l'Information, E.20 ([28/01/2011])PermalinkAide à l’exploration des propriétés structurelles d’un réseau de transport. Conception d’un modèle pour l’analyse, la visualisation et l’exploration d’un réseau de transport / Eric Mermet (2011)PermalinkA framework for regional association rule mining and scoping in spatial datasets / W. Ding in Geoinformatica, vol 15 n° 1 (January 2011)PermalinkA hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)Permalinkvol 15 n° 1 - January 2011 - Spatial and spatio-temporal data mining (Bulletin de Geoinformatica) / Ashok SamalPermalinkTIDES, a new descriptor for time series oscillation behavior / L. Mariote in Geoinformatica, vol 15 n° 1 (January 2011)Permalinkvol 24 n° 10 - october 2010 - Geospatial visual analytics : focus on time. Special issue of the ICA commission on geovisualization (Bulletin de International journal of geographical information science IJGIS) / Gennady AdrienkoPermalinkSpace-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)PermalinkUsing clustering methods in geospatial information systems / X. Wang in Geomatica, vol 64 n° 3 (September 2010)PermalinkTerrestrial laser scanning and exploratory spatial data analysis for the mapping of weathering forms on rock art panels / B. Vogt in Geocarto international, vol 25 n° 5 (August 2010)PermalinkSpatio-temporal trajectory analysis of mobile objects following the same itinerary / Laurent Etienne (26/05/2010)PermalinkPersonalizing map content to improve task completion efficiency / D. Wilson in International journal of geographical information science IJGIS, vol 24 n° 5-6 (may 2010)PermalinkSemantic-based pruning of redundant and uninteresting frequent geographic patterns / Vania Bogorny in Geoinformatica, vol 14 n° 2 (April 2010)PermalinkExploration et représentation d'une matrice de flux / Marie Piron in Le monde des cartes, n° 203 (mars 2010)PermalinkAutomatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm / T. Oyana in Geocarto international, vol 25 n° 1 (February 2010)PermalinkFuzzy image segmentation for urban land-cover classification / I. Lizarazo in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 2 (February 2010)PermalinkA general framework for using aggregation in visual exploration of movement data / Gennady Adrienko in Cartographic journal (the), vol 47 n° 1 (January 2010)PermalinkGeoGraphLab: a tool for exploring structural characteristics of transportation network / Eric Mermet (2010)PermalinkIC 2010, Ingénierie des Connaissances 2010, 21es journées francophones, 9 - 10 juin 2010, Nîmes, France / Sylvie Desprès (2010)PermalinkPermalinkSAGEO '10, conférence internationale de géomatique et d'analyse spatiale, Toulouse, 17, 18 et 19 novembre 2010 / Claude Monteil (2010)PermalinkWebGIS for evaluating walkability environment in urban center of Tsukuba / R. Thapa in Tsukuba geoenvironmental sciences, vol 5 (01/12/2009)PermalinkSt-DMQL: a semantic trajectory data mining query language / Vania Bogorny in International journal of geographical information science IJGIS, vol 23 n°9-10 (september 2009)PermalinkA data-mining approach for assessing consistency between multiple representations in spatial databases / David Sheeren in International journal of geographical information science IJGIS, vol 23 n° 7-8 (july 2009)PermalinkApport des règles d'association spatiales pour l'alimentation automatique des bases de données géographiques / S.Y. Turki in Revue internationale de géomatique, vol 19 n° 1 (mars – mai 2009)Permalinkvol 45 n° 1 - mars 2009 - Géomatique et environnement urbain, [actes], Rennes, 28 juin 2007 (Bulletin de Photo interprétation, European journal of applied remote sensing)Permalink