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Combining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)  

Public [article]  inTransactions in GIS > vol 18 n° 1 (February 2014) . - pp 125 - 146 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 descripteurs IGN] analyse combinatoire (maths) [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] visualisation Résumé : (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 [article]

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Abstracting geographic information in a data rich world, ch. 3. Modelling geographic relationships in automated environments / Guillaume Touya (2014)  

Public contenu dans Abstracting geographic information in a data rich world / Dirk Burghardt (2014) Titre de série : Abstracting geographic information in a data rich world, ch. 3 Titre : Modelling geographic relationships in automated environments Type de document : Chapitre/Contribution Auteurs : Guillaume Touya , Auteur ; Bénédicte Bucher , Auteur ; Gilles Falquet, Auteur ; Kusay Jaara, Auteur ; Stefan Steiniger, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2014 Collection : Lecture notes in Geoinformation and Cartography, ISSN 1863-2246 Importance : pp 53 - 82 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] CityGML [Termes descripteurs IGN] généralisation automatique de données [Termes descripteurs IGN] modèle relationnel [Termes descripteurs IGN] relation spatiale [Termes descripteurs IGN] représentation multiple Mots-clés libres : spatial-relations Résumé : (auteur) Automated processes such as cartographic generalisation require formal abstraction of the geographic space in order to analyse, process and transform it. Spatial relations are key to understanding geographic space and their modelling is a critical issue. This chapter reports on existing classifications and modelling frameworks for spatial relations. A generic model is proposed for building an ontology of spatial relations for automatic processes such as generalisation or on-demand mapping, with a focus on so-called multiple representation relations. Propositions to use such ontology in an automated environment are reported. The three use cases of the chapter describe recent research that uses relations modelling. The first use case is the extension of CityGML with relations for 3D city models. The second use case presents the use of spatial relations for automatic spatial analysis, and particularly the grouping of natural features such as lakes or islands. Finally, the third use case is a data migration model guided by relations that govern the positioning of thematic data upon changing reference data. Numéro de notice : H2014-004 Affiliation des auteurs : COGIT+Ext (1988-2011) Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1007/978-3-319-00203-3_3 date de publication en ligne : 01/04/2014 En ligne : http://dx.doi.org/10.1007/978-3-319-00203-3_3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78667

Scale-specific automated line simplification by vertex clustering on a hexagonal tessellation / Paulo Raposo in Cartography and Geographic Information Science, vol 40 n° 5 (November 2013)  

Public [article]  inCartography and Geographic Information Science > vol 40 n° 5 (November 2013) . - pp 427 - 443 Titre : Scale-specific automated line simplification by vertex clustering on a hexagonal tessellation Type de document : Article/Communication Auteurs : Paulo Raposo, Auteur Année de publication : 2013 Article en page(s) : pp 427 - 443 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] distance de Hausdorff [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] noeud [Termes descripteurs IGN] simplification de contour [Termes descripteurs IGN] tessellation Résumé : (Auteur) A new method of cartographic line simplification is presented. Regular hexagonal tessellations are used to sample lines for simplification, where hexagon width, reflecting sampling fidelity, is varied in proportion to target scale and drawing resolution. Tesserae constitute loci at which new sets of vertices are defined by vertex clustering quantization, and these vertices are used to compose simplified lines retaining only visually resolvable detail at target scale. Hexagon scaling is informed by the Nyquist–Shannon sampling theorem. The hexagonal quantization algorithm is also compared to an implementation of the Li–Openshaw raster-vector algorithm, which undertakes a similar process using square raster cells. Lines produced by either algorithm using like tessera widths are compared for fidelity to the original line in two ways: Hausdorff distances to the original lines are statistically analyzed, and simplified lines are presented against input lines for visual inspection. Results show that hexagonal quantization offers advantages over square tessellations for vertex clustering line simplification in that simplified lines are significantly less displaced from input lines. Visual inspection suggests lines produced by hexagonal quantization retain informative geographical shapes for greater differences in scale than do those produced by quantization in square cells. This study yields a scale-specific cartographic line simplification algorithm, following Li and Openshaw's natural principle, which is readily applicable to cartographic linework. Open-source Java code implementing the hexagonal quantization algorithm is available online. Numéro de notice : A2013-764 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/15230406.2013.803707 En ligne : https://doi.org/10.1080/15230406.2013.803707 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32900 [article]

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Footprint generation using fuzzy-neighborhood clustering / Jonathon K. Parker in Geoinformatica, vol 17 n° 2 (April 2013)  

Public [article]  inGeoinformatica > vol 17 n° 2 (April 2013) . - pp 285 - 299 Titre : Footprint generation using fuzzy-neighborhood clustering Type de document : Article/Communication Auteurs : Jonathon K. Parker, Auteur ; Joni A. Downs, Auteur Année de publication : 2013 Article en page(s) : pp 285 - 299 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] empreinte Résumé : (Auteur) Geometric footprints, which delineate the region occupied by a spatial point pattern, serve a variety of functions in GIScience. This research explores the use of two density-based clustering algorithms for footprint generation. First, the Density-Based Spatial Clustering with Noise (DBSCAN) algorithm is used to classify points as core points, non-core points, or statistical noise; then a footprint is created from the core and non-core points in each cluster using convex hulls. Second, a Fuzzy-Neighborhood (FN)-DBSCAN algorithm, which incorporates fuzzy set theory, is used to assign points to clusters based on membership values. Then, two methods are presented for delineating footprints with FN-DBSCAN: (1) hull-based techniques and (2) contouring methods based on interpolated membership values. The latter approach offers increased flexibility for footprint generation, as it provides a continuous surface of membership values from which precise contours can be delineated. Then, a heuristic parameter selection method is described for FN-DBSCAN, and the approach is demonstrated in the context of wildlife home range estimation, where the goal is to a generate footprint of an animal’s movements from tracking data. Additionally, FN-DBSCAN is applied to produce crime footprints for a county in Florida. The results are used to guide a discussion of the relative merits of the new techniques. In summary, the fuzzy clustering approach offers a novel method of footprint generation that can be applied to characterize a variety of point patterns in GIScience. Numéro de notice : A2013-160 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-012-0152-0 date de publication en ligne : 06/03/2012 En ligne : https://doi.org/10.1007/s10707-012-0152-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32298 [article]

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Spatio-temporal polygonal clustering with space and time as first-class citizens / Deepti Joshi in Geoinformatica, vol 17 n° 2 (April 2013)  

Public [article]  inGeoinformatica > vol 17 n° 2 (April 2013) . - pp 387 - 412 Titre : Spatio-temporal polygonal clustering with space and time as first-class citizens Type de document : Article/Communication Auteurs : Deepti Joshi, Auteur ; Ashok Samal, Auteur ; Leen-Kiat Soh, Auteur Année de publication : 2013 Article en page(s) : pp 387 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] polygonale Résumé : (Auteur) Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application. Numéro de notice : A2013-164 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-012-0157-8 date de publication en ligne : 15/03/2012 En ligne : https://doi.org/10.1007/s10707-012-0157-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32302 [article]

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Trajectories of moving objects on a network: detection of similarities, visualization of relations, and classification of trajectories / Yukio Sadahiro in Transactions in GIS, vol 17 n° 1 (February 2013)  

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Semisupervised learning of hyperspectral data with unknown land-cover classes / G. Jun in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

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Cluster recognition in spatial-temporal sequences: the case of forest fires / C. Vega Orozco in Geoinformatica, vol 15 n° 4 (October 2012)  

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Semisupervised classification of remote sensing images with active queries / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)  

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Hyperspectral band clustering and band selection for urban land cover classification / H. Su in Geocarto international, vol 27 n° 5 (August 2012)  

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Memory-based cluster sampling for remote sensing image classification / Michele Volpi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

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Satellite image time series analysis under time warping / F. Petitjean in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

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Discovering spatial patterns in origin-destination mobility data / D. Guo in Transactions in GIS, vol 16 n° 3 (June 2012)  

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Efficient parallel algorithm for pixel classification in remote sensing imagery / U. Maulik in Geoinformatica, vol 16 n° 2 (April 2012)  

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Fuzzy analysis for modeling regional delineation and development: The case of the Sardinian mining geopark / G. Manca in Transactions in GIS, vol 16 n° 1 (February 2012)  

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