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Exploring 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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1154 - 1177 Titre : Exploring spatiotemporal clusters based on extended kernel estimation methods Type de document : Article/Communication Auteurs : Jay Lee, Auteur ; Junfang Gong, Auteur ; Shengwen Li, Auteur Année de publication : 2017 Article en page(s) : pp 1154 - 1177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] données spatiotemporelles [Termes IGN] estimation par noyau [Termes IGN] exploration de données géographiques [Termes IGN] groupe [Termes IGN] implémentation (informatique) [Termes IGN] infraction [Termes IGN] Ohio (Etats-Unis) [Termes IGN] système d'information géographique Résumé : (auteur) We examined three different ways to integrate spatial and temporal data in kernel density estimation methods (KDE) to identify space–time clusters of geographic events. Spatial data and time data are typically measured in different units along respective dimensions. Therefore, spatial KDE methods require special extensions when incorporating temporal data to detect spatiotemporal clusters of geographical event. In addition to a real-world data set, we applied the proposed methods to simulated data that were generated through random and normal processes to compare results of different kernel functions. The comparison is based on hit rates and values of a compactness index with considerations of both spatial and temporal attributes of the data. The results show that the spatiotemporal KDE (STKDE) can reach higher hit rates while keeping identified hotspots compact. The implementation of these STKDE methods is tested using the 2012 crime event data in Akron, Ohio, as an example. The results show that STKDE methods reveal new perspectives from the data that go beyond what can be extracted by using the conventional spatial KDE. Numéro de notice : A2017-243 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287371 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287371 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85179 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Polygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 5 (October 2015) . - pp 716 – 736 Titre : Polygonal clustering analysis using multilevel graph-partition Type de document : Article/Communication Auteurs : Wanyi Wang, Auteur ; Shihong Du, Auteur ; Zhou Guo, Auteur ; Liqun Luo, Auteur Année de publication : 2015 Article en page(s) : pp 716 – 736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] connexité (graphes) [Termes IGN] distance [Termes IGN] données spatiotemporelles [Termes IGN] figure géométrique [Termes IGN] groupe [Termes IGN] partition des données [Termes IGN] polygone [Termes IGN] similitude Résumé : (auteur) Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non-overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph-partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. Numéro de notice : A2015-684 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12124 En ligne : http://dx.doi.org/10.1111/tgis.12124 Format de la ressource électronique : Url artticle Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78325 [article]

Density-based clustering for data containing two types of points / Tao Pei in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 2 (February 2015) . - pp 175 - 193 Titre : Density-based clustering for data containing two types of points Type de document : Article/Communication Auteurs : Tao Pei, Auteur ; Weiyi Wang, Auteur ; Hengcai Zhang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 175 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification par seuillage sur la limite la plus proche [Termes IGN] densité d'information [Termes IGN] échelle d'intensité [Termes IGN] groupe [Termes IGN] taxi [Termes IGN] transport routier Résumé : (Auteur) When only one type of point is distributed in a region, clustered points can be seen as an anomaly. When two different types of points coexist in a region, they overlap at different places with various densities. In such cases, the meaning of a cluster of one type of point may be altered if points of the other type show different densities within the same cluster. If we consider the origins and destinations (OD) of taxicab trips, the clustering of both in the morning may indicate a transportation hub, whereas clustered origins and sparse destinations (a hot spot where taxis are in short supply) could suggest a densely populated residential area. This cannot be identified by previous clustering methods, so it is worthwhile studying a clustering method for two types of points. The concept of two-component clustering is first defined in this paper as a group containing two types of points, at least one of which exhibits clustering. We then propose a density-based method for identifying two-component clusters. The method is divided into four steps. The first estimates the clustering scale of the point data. The second transforms the point data into the 2D density domain, where the x and y axes represent the local density of each type of point around each point, respectively. The third determines the thresholds for extracting the clusters, and the fourth generates two-component clusters using a density-connectivity mechanism. The method is applied to taxicab trip data in Beijing. Three types of two-component clusters are identified: high-density origins and destinations, high-density origins and low-density destinations, and low-density origins and high-density destinations. The clustering results are verified by the spatial relationship between the cluster locations and their land-use types over different periods of the day. Numéro de notice : A2015-577 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.955027 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.955027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77839 [article]

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data / Wei Zhuang in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 95 (September 2014) . - pp 81 – 92 Titre : An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data Type de document : Article/Communication Auteurs : Wei Zhuang, Auteur ; Giorgos Mountrakis, Auteur Année de publication : 2014 Article en page(s) : pp 81 – 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] empreinte [Termes IGN] filtrage numérique d'image [Termes IGN] forêt [Termes IGN] forme d'onde [Termes IGN] groupe [Termes IGN] identification automatique [Termes IGN] onde lidar [Termes IGN] surface du sol [Termes IGN] télémétrie laser aéroporté [Termes IGN] traitement de données Résumé : (Auteur) Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors. Numéro de notice : A2014-474 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74051 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An efficient approach to load balancing of vector maps in cyberGIS cluster environment / Mingqiang Guo in Geomatica, vol 68 n° 2 (June 2014)  

Public [article]  inGeomatica > vol 68 n° 2 (June 2014) . - pp 129 - 134 Titre : An efficient approach to load balancing of vector maps in cyberGIS cluster environment Type de document : Article/Communication Auteurs : Mingqiang Guo, Auteur ; Ying Huang, Auteur ; Zhong Xie, Auteur Année de publication : 2014 Article en page(s) : pp 129 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données vectorielles [Termes IGN] groupe [Termes IGN] Répartiteur de charge [Termes IGN] représentation vectorielle [Termes IGN] temps réel [Termes IGN] visualisation cartographique [Termes IGN] WebSIG Résumé : (auteur) La visualisation en temps réel de cartes vectorielles est la fonction la plus commune dans le domaine des cyberSIG. Elle est très coûteuse en temps, surtout lorsque le volume de données augmente. L’amélioration du rendement en visualisation de grandes cartes vectorielles demeure un axe de recherche important pour les scientifiques oeuvrant dans le domaine des SIG. Dans le cadre du présent article de recherche, nous signalons que l’optimisation parallèle est convenable pour la visualisation en temps réel de grandes cartes vectorielles. Le but principal de cette recherche est de trouver une méthode de décomposition équilibrée qui peut bien répartir la charge de chaque noeud de serveur dans un environnement de « cluster » de cyberSIG. Le répartiteur de charges analyse les caractéristiques spatiales des requêtes cartographiques et décompose le cône visuel en temps réel en sous-cônes visuels multiples et équilibrés, afin d’équilibrer la charge de tous les noeuds de serveurs dans l’environnement de « cluster ». Les résultats des essais montrent que la méthode proposée dans cette recherche est capable d’équilibrer les charges dans un environnement de « cluster » de cyberSIG. Numéro de notice : A2014-662 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/cig2014-204 En ligne : https://doi.org/10.5623/cig2014-204 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75358 [article]

A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes / Tao Pei in International journal of geographical information science IJGIS, vol 20 n° 2 (february 2006)  

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A simple and effective radiometric correction method to improve landscape change detection across sensors and across time / X. Chen in Remote sensing of environment, vol 98 n° 1 (30/09/2005)  

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Mapping the 21st century: the 20th International Cartographic Conference, ICC 2001, Beijing, China, August 6 - 10, 2001, vol 3. Proceedings / L. Li (2001)  

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GBI : a generalized R-tree bulk-insertion strategy / R. Choubey (20/07/1999)  

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Introduction to remote sensing / J.B. Campbell (1996)

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Traitement et reconnaissance des formes par un ordinateur / A. Checroun (1972)

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