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SPAWNN: A toolkit for SPatial Analysis With Self-Organizing Neural Networks / Julian Hagenauer in Transactions in GIS, vol 20 n° 5 (October 2016)  

Public [article]  inTransactions in GIS > vol 20 n° 5 (October 2016) . - pp 755 – 774 Titre : SPAWNN: A toolkit for SPatial Analysis With Self-Organizing Neural Networks Type de document : Article/Communication Auteurs : Julian Hagenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2016 Article en page(s) : pp 755 – 774 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse combinatoire (maths) [Termes IGN] carte de Kohonen [Termes IGN] données massives [Termes IGN] données socio-économiques [Termes IGN] intelligence artificielle [Termes IGN] logiciel libre [Termes IGN] outil logiciel [Termes IGN] Philadelphie [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This article introduces the SPAWNN toolkit, an innovative toolkit for spatial analysis with self-organizing neural networks, which is published as free and open-source software (http://www.spawnn.org). It extends existing toolkits in three important ways. First, the SPAWNN toolkit distinguishes between self-organizing neural networks and spatial context models with which the networks can be combined to incorporate spatial dependence and provides implementations for both. This distinction maintains modularity and enables a multitude of useful combinations for analyzing spatial data with self-organizing neural networks. Second, SPAWNN interactively links different self-organizing networks and data visualizations in an intuitive manner to facilitate explorative data analysis. Third, it implements cutting-edge clustering algorithms for identifying clusters in the trained networks. Toolkits such as SPAWNN are particularly needed when researchers and practitioners are confronted with large amounts of complex and high-dimensional data. The computational performance of the implemented algorithms is empirically demonstrated using high-dimensional synthetic data sets, while the practical functionality highlighting the distinctive features of the toolkit is illustrated with a case study using socioeconomic data of the city of Philadelphia, Pennsylvania. Numéro de notice : A2016-998 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12180 En ligne : http://dx.doi.org/10.1111/tgis.12180 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83778 [article]

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study / Hossein Shafizadeh-Moghadam in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623 Titre : Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Julian Hagenauer, Auteur ; Manuchehr Farajzadeh, Auteur ; Marco Helbich, Auteur Année de publication : 2015 Article en page(s) : pp 606 - 623 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Bombay [Termes IGN] croissance urbaine [Termes IGN] fonction de base radiale [Termes IGN] milieu urbain [Termes IGN] modèle de simulation [Termes IGN] Perceptron multicouche [Termes IGN] performance [Termes IGN] test de performance [Termes IGN] urbanisation Résumé : (Auteur) The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling. Numéro de notice : A2015-589 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.993989 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.993989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77875 [article]