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Auteur Marco Helbich |
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A geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
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Titre : A geographically weighted artificial neural network Type de document : Article/Communication Auteurs : Julian Haguenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2022 Article en page(s) : pp 215 - 235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] Autriche
[Termes IGN] coût
[Termes IGN] évaluation foncière
[Termes IGN] hétérogénéité spatiale
[Termes IGN] logement
[Termes IGN] régression géographiquement pondérée
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal artificielRésumé : (auteur) While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting. Numéro de notice : A2022-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1871618 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1871618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99785
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 215 - 235[article]SPAWNN: A toolkit for SPatial Analysis With Self-Organizing Neural Networks / Julian Hagenauer in Transactions in GIS, vol 20 n° 5 (October 2016)
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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] GéovisualisationRésumé : (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
in Transactions in GIS > vol 20 n° 5 (October 2016) . - pp 755 – 774[article]An exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index / Jamal Jokar Arsanjani in Transactions in GIS, vol 19 n° 6 (December 2015)
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Titre : An exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index Type de document : Article/Communication Auteurs : Jamal Jokar Arsanjani, Auteur ; Peter Mooney, Auteur ; Marco Helbich, Auteur ; Alexander Zipf, Auteur Année de publication : 2015 Article en page(s) : pp 896 – 914 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Allemagne
[Termes IGN] approche participative
[Termes IGN] automate cellulaire
[Termes IGN] base de données spatiotemporelles
[Termes IGN] cartographie collaborative
[Termes IGN] données localisées des bénévoles
[Termes IGN] indexation sémantique
[Termes IGN] OpenStreetMap
[Termes IGN] StuttgartRésumé : (auteur) OpenStreetMap (OSM) represents one of the most well-known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM's evolution across space and time has barely been noted. This study aims to analyze spatio-temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dynamism of OSM. The CI is based on a per cell analysis of the node quantity, interactivity, semantics, and attractivity (the ability to attract contributors). Additionally this research explores whether OSM has been constantly attracting new users and contributions or if OSM has experienced a decline in its ability to attract continued contributions. Using the Stuttgart region of Germany as a case study the empirical findings of the CI over time confirm that since 2007, OSM has been constantly attracting new users, who create new features, edit the existing spatial objects, and enrich them with attributes. This rate has been dramatically growing since 2011. The utilization of a Cellular Automata-Markov (CA-Markov) model provides evidence that by the end of 2016 and 2020, the rise of CI will spread out over the study area and only a few cells without OSM features will remain. Numéro de notice : A2016-437 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12139 En ligne : http://dx.doi.org/10.1111/tgis.12139 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81347
in Transactions in GIS > vol 19 n° 6 (December 2015) . - pp 896 – 914[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)
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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] urbanisationRésumé : (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
in International journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623[article]Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis / Marco Helbich in Cartography and Geographic Information Science, Vol 42 n° 2 (April 2015)
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Titre : Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis Type de document : Article/Communication Auteurs : Marco Helbich, Auteur ; Jamal Jokar Arsanjani, Auteur Année de publication : 2015 Article en page(s) : pp 134 - 148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] carte thématique
[Termes IGN] données spatiotemporelles
[Termes IGN] filtrage statistique
[Termes IGN] Houston (Texas)
[Termes IGN] infraction
[Termes IGN] valeur propreRésumé : (auteur) Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecification, i.e., residual spatial autocorrelation, using a nonviolent crime dataset for the metropolitan area of Houston, Texas, over the period 2005–2010. The results suggest that local and regional geography significantly contributes to the explanation of crime patterns. Furthermore, common space-time eigenvectors selected on an annual basis indicate striking spatiotemporal patterns persisting over time. The findings about the driving forces behind Houston’s crime show that linear and nonlinear, spatially filtered, NBMs successfully absorb latent autocorrelation and, therefore, prevent parameter estimation bias. The consideration of a spatial filter also increases the explanatory power of the regressions. It is concluded that ESF can be highly recommended for the integration in spatial and spatiotemporal modeling toolboxes of law enforcement agencies. Numéro de notice : A2015-238 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2014.893839 En ligne : https://doi.org/10.1080/15230406.2014.893839 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76495
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