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Auteur S.W. Laffan |
Documents disponibles écrits par cet auteur (4)
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Sensivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model / Zhi Huang in International journal of geographical information science IJGIS, vol 23 n°11-12 (november 2009)
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[article]
Titre : Sensivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model Type de document : Article/Communication Auteurs : Zhi Huang, Auteur ; S.W. Laffan, Auteur Année de publication : 2009 Article en page(s) : pp 1433 - 1452 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de sensibilité
[Termes IGN] carte de la végétation
[Termes IGN] classification par arbre de décision
[Termes IGN] erreur de classification
[Termes IGN] erreur de positionnement
[Termes IGN] image Landsat-TM
[Termes IGN] incertitude des données
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle numérique de terrainRésumé : (Auteur) We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one-at-a-time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random following a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, than to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non-linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as +2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs that have distinctive characteristics from that of numerical outputs. Copyright Taylor & Francis Numéro de notice : A2009-515 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/13658810802634949 En ligne : https://doi.org/10.1080/13658810802634949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30144
in International journal of geographical information science IJGIS > vol 23 n°11-12 (november 2009) . - pp 1433 - 1452[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-09071 RAB Revue Centre de documentation En réserve L003 Disponible 079-09072 RAB Revue Centre de documentation En réserve L003 Disponible Sparse grids: a new predictive modelling method for the analysis of geographic data / S.W. Laffan in International journal of geographical information science IJGIS, vol 19 n° 3 (march 2005)
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Titre : Sparse grids: a new predictive modelling method for the analysis of geographic data Type de document : Article/Communication Auteurs : S.W. Laffan, Auteur ; O.M. Nielsen, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 267 - 292 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] Australie
[Termes IGN] classification par réseau neuronal
[Termes IGN] exploration de données géographiques
[Termes IGN] géomorphométrie
[Termes IGN] grille aérée
[Termes IGN] morphologie mathématique
[Termes IGN] prédictionRésumé : (Auteur) We introduce in this paper a new predictive modelling method to analyse geographic data known as sparse grids. The sparse grids method has been developed for data-mining applications. It is a machine-learning approach to data analysis and has great applicability to the analysis and understanding of geographic data and processes. Sparse grids are a subset of grid-based predictive modelling approaches. The advantages they have over other grid-based methods are that they use fewer parameters and are less susceptible to the curse of dimensionality. These mean that they can be applied to many geographic problems and are readily adapted to the analysis of geographically local samples. We demonstrate the utility of the sparse grids system using a large and spatially extensive data set of regolith samples from Weipa, Australia. We apply both global and local analyses to find relationships between the regolith data and a set of geomorphometric, hydrologic and spectral variables. The results of the global analyses are much better than those generated using an artificial neural network, and the local analysis results are better than those generated using moving window regression for the same analysis window size. The sparse grids system provides a potentially powerful tool for the analysis and understanding of geographic processes and relationships. Numéro de notice : A2005-076 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810512331319118 En ligne : https://doi.org/10.1080/13658810512331319118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27214
in International journal of geographical information science IJGIS > vol 19 n° 3 (march 2005) . - pp 267 - 292[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-05031 RAB Revue Centre de documentation En réserve L003 Disponible 079-05032 RAB Revue Centre de documentation En réserve L003 Disponible Gambling with randomness: the use of pseudo-random number generators in GIS / K. Van Niel in International journal of geographical information science IJGIS, vol 17 n° 1 (february 2003)
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Titre : Gambling with randomness: the use of pseudo-random number generators in GIS Type de document : Article/Communication Auteurs : K. Van Niel, Auteur ; S.W. Laffan, Auteur Année de publication : 2003 Article en page(s) : pp 49 - 68 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] processus stochastique
[Termes IGN] simulationRésumé : (Auteur) Analyses within the field of GIS are increasingly applying stochastic methods and systems that make use of pseudo-random number generators (PRNGs). Examples include Monte Carlo techniques, dynamic modelling, stochastic simulation, artificial life and simulated data development. PRNGs have inherent biases, and this will in turn bias any analyses using them. Therefore, the validity of stochastic analyses is reliant on the PRNG employed. Despite this, the effect of PRNGs in spatial analyses has never been completely explored, particularly a comparison of different PRNGs. Exacerbating the problem is that GIS articles applying Monte Carlo or other stochastic methods rarely report which PRNG is employed. It thus appears likely that GIS researchers rarely, if ever, cheek the suitability of the PRNG employed for their analyses or simulations. This paper presents a discussion of some of the characteristics of PRNGs and specific issues from a geospatial standpoint, including a demonstration of the differences in the results of a Monte Carlo analysis obtained using two different PRNGs. It then makes recommendations for the application of PRNGs in spatial analyses, including recommending specific PRNGs that have attributes appropriate for geospatial analysis. The paper concludes with a call for more research into the application of PRNGs to spatial analyses to fully understand the impact of biases, especially before they are routinely used in the wider spatial analysis community. Numéro de notice : A2003-104 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/713811743 En ligne : https://doi.org/10.1080/713811743 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22400
in International journal of geographical information science IJGIS > vol 17 n° 1 (february 2003) . - pp 49 - 68[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-03011 RAB Revue Centre de documentation En réserve L003 Disponible 079-03012 RAB Revue Centre de documentation En réserve L003 Disponible Using process models to improve spatial analysis / S.W. Laffan in International journal of geographical information science IJGIS, vol 16 n° 3 (april 2002)
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[article]
Titre : Using process models to improve spatial analysis Type de document : Article/Communication Auteurs : S.W. Laffan, Auteur Année de publication : 2002 Article en page(s) : pp 245 - 257 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse spatiale
[Termes IGN] ligne de partage des eaux
[Termes IGN] processus
[Termes IGN] variogrammeNuméro de notice : A2002-051 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810110099107 En ligne : https://doi.org/10.1080/13658810110099107 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21968
in International journal of geographical information science IJGIS > vol 16 n° 3 (april 2002) . - pp 245 - 257[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-02031 RAB Revue Centre de documentation En réserve L003 Disponible