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Analysis of multi-dimensional space-filling curves / M.F. Mokbel in Geoinformatica, vol 7 n° 3 (September - November 2003)  

Public [article]  inGeoinformatica > vol 7 n° 3 (September - November 2003) . - pp 179 - 209 Titre : Analysis of multi-dimensional space-filling curves Type de document : Article/Communication Auteurs : M.F. Mokbel, Auteur ; W.G. Aref, Auteur ; I. Kamel, Auteur Année de publication : 2003 Conférence : ACM GIS 2002, 10th ACM International Symposium on Advances in Geographic Information Systems 08/11/2002 09/11/2002 McLean Virginie - Etats-Unis Selected papers Article en page(s) : pp 179 - 209 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse comparative [Termes IGN] analyse fractale [Termes IGN] données localisées 3D [Termes IGN] espace de Hilbert [Termes IGN] intelligence artificielle Résumé : (Auteur) A spacefilling curve is a way of mapping the multidimensional space into the 1D space. It acts like a thread that passes through every cell element (or pixel) in the Ddimensional space so that every cell is visited exactly once. There are numerous kinds of spacefilling curves. The difference between such curves is in their way of mapping to the 1D space. Selecting the appropriate curve for any application requires knowledge of the mapping scheme provided by each spacefilling curve. A spacefilling curve consists of a set of segments. Each segment connects two consecutive multidimensional points. Five different types of segments are distinguished, namely, Jump, Contiguity, Reverse, Forward, and Still. A description vector V = (J, C, R, F, S), where J, C, R, F, and S are the percentages of Jump, Contiguity, Reverse, Forward, and Still segments in the spacefilling curve, encapsulates all the properties of a spacefilling curve. The knowledge of V facilitates the process of selecting the appropriate spacefilling curve for different applications. Closed formulas are developed to compute the description vector V for any Ddimensional space and grid size N for different spacefilling curves. A comparative study of different spacefilling curves with respect to the description vector is conducted and results are presented and discussed. Numéro de notice : A2003-199 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1023/A:1025196714293 En ligne : https://doi.org/10.1023/A:1025196714293 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22495 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-03031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Efficient estimation of qualitative topological relations based on the weighted walkthroughs model / S. Cicerone in Geoinformatica, vol 7 n° 3 (September - November 2003)  

Public [article]  inGeoinformatica > vol 7 n° 3 (September - November 2003) . - pp 211 - 227 Titre : Efficient estimation of qualitative topological relations based on the weighted walkthroughs model Type de document : Article/Communication Auteurs : S. Cicerone, Auteur ; Eliseo Clementini, Auteur Année de publication : 2003 Conférence : ACM GIS 2002, 10th ACM International Symposium on Advances in Geographic Information Systems 08/11/2002 09/11/2002 McLean Virginie - Etats-Unis Selected papers Article en page(s) : pp 211 - 227 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'images [Termes IGN] données maillées [Termes IGN] estimation statistique [Termes IGN] raisonnement spatial [Termes IGN] relation topologique Résumé : (Auteur) Weighted walkthroughs are a quantitative model for representing the spatial relation between two raster features in image databases. In this paper, we establish a correspondence between the weighted walkthroughs and qualitative models for spatial reasoning. We provide rules for estimating qualitative geometric properties and topological relations from the quantitative data that are computed for each pair of pixel sets. The approach has been tested through experiments with raster regions. Numéro de notice : A2003-200 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1023/A:1025148831131 En ligne : https://doi.org/10.1023/A:1025148831131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22496 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-03031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

ICEAGE: interactive clustering and exploration of large and high-dimensional geodata / D. Guo in Geoinformatica, vol 7 n° 3 (September - November 2003)  

Public [article]  inGeoinformatica > vol 7 n° 3 (September - November 2003) . - pp 229 - 253 Titre : ICEAGE: interactive clustering and exploration of large and high-dimensional geodata Type de document : Article/Communication Auteurs : D. Guo, Auteur ; Donna J. Peuquet, Auteur ; M. Gahegan, Auteur Année de publication : 2003 Conférence : ACM GIS 2002, 10th ACM International Symposium on Advances in Geographic Information Systems 08/11/2002 09/11/2002 McLean Virginie - Etats-Unis Selected papers Article en page(s) : pp 229 - 253 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification automatique d'objets [Termes IGN] découverte de connaissances [Termes IGN] évaluation [Termes IGN] interactivité [Termes IGN] segmentation [Termes IGN] visualisation de données Résumé : (Auteur) The unprecedented large size and high dimensionality of existing geographic datasets make the complex patterns that potentially lurk in the data hard to find. Clustering is one of the most important techniques for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods focus on the specific characteristics of distributions in 2or 3D space, while general-purpose highdimensional clustering methods have limited power in recognizing spatial patterns that involve neighbors. Second, clustering methods in general are not geared toward allowing the humancomputer interaction needed to effectively teaseout complex patterns. In the current paper, an approach is proposed to open up the "black box" of the clustering process for easy understanding, steering, focusing and interpretation, and thus to support an effective exploration of large and high dimensional geographic data. The proposed approach involves building a hierarchical spatial cluster structure ithin the highdimensional feature space, and using this combined space for discovering multidimensional (combined spatial and nonspatial) patterns with efficient computational clustering methods and highly interactive visualization techniques. More specifically, this includes the integration of: (1) a hierarchical spatial clustering method to generate a 1D spatial cluster ordering that preserves the hierarchical cluster structure, and (2) a density and gridbased technique to effectively support the interactive identification of interesting subspaces and subsequent searching for clusters in each subspace. The implementation of the proposed approach is in a fully open and interactive manner supported by various visualization techniques. Numéro de notice : A2003-201 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1023/A:1025101015202 En ligne : https://doi.org/10.1023/A:1025101015202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22497 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-03031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A road network embedding technique for k-nearest neighbor search in moving object databases / M.R. Kolahdouzan in Geoinformatica, vol 7 n° 3 (September - November 2003)  

Public [article]  inGeoinformatica > vol 7 n° 3 (September - November 2003) . - pp 255 - 273 Titre : A road network embedding technique for k-nearest neighbor search in moving object databases Type de document : Article/Communication Auteurs : M.R. Kolahdouzan, Auteur ; C. Shahabi, Auteur ; M. Sharifzadeh, Auteur Année de publication : 2003 Conférence : ACM GIS 2002, 10th ACM International Symposium on Advances in Geographic Information Systems 08/11/2002 09/11/2002 McLean Virginie - Etats-Unis Selected papers Article en page(s) : pp 255 - 273 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données localisées [Termes IGN] classification barycentrique [Termes IGN] distance euclidienne [Termes IGN] espace euclidien [Termes IGN] mise à jour de base de données [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] topologie Résumé : (Auteur) A very important class of queries in GIS applications is the class of Knearest neighbor queries. Most of the current studies on the Knearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In realworld road networks, however, the shortest distance between two points depends on the actual path connecting the points and cannot be computed accurately using one of the Minkowski metrics. Thus, the Euclidean distance may not properly approximate the real distance. In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. Subsequently, we extend our approach to dynamically transform new points into the embedding space. Finally, we propose an efficient technique that can find the actual shortest path between two points in the original road network using only the embedding space. Our empirical experiments indicate that the Chessboard distance metric (L,) in the embedding space preserves the ordering of the distances between a point and its neighbors more precisely as compared to the Euclidean distance in the original road network. Numéro de notice : A2003-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1023/A:1025153016110 En ligne : https://doi.org/10.1023/A:1025153016110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22498 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-03031	RAB	Revue	Centre de documentation	En réserve L003	Disponible