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Auteur Leen-Kiat Soh |
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Using spatial data support for reducing uncertainty in geospatial applications / T. Hong in Geoinformatica, vol 18 n° 1 (January 2014)
[article]
Titre : Using spatial data support for reducing uncertainty in geospatial applications Type de document : Article/Communication Auteurs : T. Hong, Auteur ; K. Hart, Auteur ; Leen-Kiat Soh, Auteur ; Ashok Samal, Auteur Année de publication : 2014 Article en page(s) : pp 63 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] exploration de données géographiques
[Termes IGN] incertitude des données
[Termes IGN] Nebraska (Etats-Unis)
[Termes IGN] série temporelleRésumé : (Auteur) Widespread use of GPS devices and ubiquity of remotely sensed geospatial images along with cheap storage devices have resulted in vast amounts of digital data. More recently, with the advent of wireless technology, a large number of sensor networks have been deployed to monitor many human, biological and natural processes. This poses a challenge in many data rich application domains now: how to best choose the datasets to solve specific problems? In particular, some of the datasets may be redundant and their inclusion in analysis may not only be time consuming, but also lead to erroneous conclusions. On the other hand, excluding some of the datasets hastily might skew the observations drawn. We propose the concept of data support as the basis for efficient, cost-effective and intelligent use of geospatial data in order to reduce uncertainty in the analysis and consequently in the results. Data support is defined as the process of determining the information utility of a data source to help decide which one to include or exclude to improve cost-effectiveness in existing data analysis. In this paper we use mutual information—a concept popular in information theory as a measure to compute information gain or loss between two datasets—as the basis of computing data support. The flexibility and effectiveness of the approach are demonstrated using an application in the hydrological analysis domain, specifically, watersheds in the state of Nebraska. Numéro de notice : A2014-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0177-z Date de publication en ligne : 12/06/2013 En ligne : https://doi.org/10.1007/s10707-013-0177-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32933
in Geoinformatica > vol 18 n° 1 (January 2014) . - pp 63 - 92[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Spatio-temporal polygonal clustering with space and time as first-class citizens / Deepti Joshi in Geoinformatica, vol 17 n° 2 (April 2013)
[article]
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 IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] données spatiotemporelles
[Termes IGN] exploration de données
[Termes IGN] polygonaleRésumé : (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
in Geoinformatica > vol 17 n° 2 (April 2013) . - pp 387 - 412[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2013021 RAB Revue Centre de documentation En réserve L003 Disponible Techniques for computing fitness of use (FoU) for time series datasets with applications in the geospatial domain / L. Fu in Geoinformatica, vol 12 n° 1 (March - May 2008)
[article]
Titre : Techniques for computing fitness of use (FoU) for time series datasets with applications in the geospatial domain Type de document : Article/Communication Auteurs : L. Fu, Auteur ; Leen-Kiat Soh, Auteur ; Ashok Samal, Auteur Année de publication : 2008 Article en page(s) : pp 91 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] jeu de données localisées
[Termes IGN] pertinence
[Termes IGN] série temporelle
[Termes IGN] théorie de Dempster-ShaferRésumé : (Auteur) Time series data are widely used in many applications including critical decision support systems. The goodness of the dataset, called the Fitness of Use (FoU), used in the analysis has direct bearing on the quality of the information and knowledge generated and hence on the quality of the decisions based on them. Unlike traditional quality of data which is independent of the application in which it is used, FoU is a function of the application. As the use of geospatial time series datasets increase in many critical applications, it is important to develop formal methodologies to compute their FoU and propagate it to the derived information, knowledge and decisions. In this paper we propose a formal framework to compute the FoU of time series datasets. We present three different techniques using the Dempster–Shafer belief theory framework as the foundation. These three approaches investigate the FoU by focusing on three aspects of data: data attributes, data stability, and impact of gap periods, respectively. The effectiveness of each approach is shown using an application in hydrological datasets that measure streamflow. While we use hydrological information analysis as our application domain in this research, the techniques can be used in many other domains as well. Copyright Springer Numéro de notice : A2008-071 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-007-0025-0 En ligne : https://doi.org/10.1007/s10707-007-0025-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29066
in Geoinformatica > vol 12 n° 1 (March - May 2008) . - pp 91 - 115[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-08011 RAB Revue Centre de documentation En réserve L003 Disponible