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A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling / Ángel M. Felicísimo in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 4 (July 2018) . - pp 329 - 338 Titre : A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling Type de document : Article/Communication Auteurs : Ángel M. Felicísimo, Auteur ; Miguel A. Martín-Tardío, Auteur Année de publication : 2018 Article en page(s) : pp 329 - 338 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] carte climatique [Termes IGN] carte thématique [Termes IGN] distribution spatiale [Termes IGN] estompage analytique [Termes IGN] image Landsat-8 [Termes IGN] modèle numérique de terrain [Termes IGN] rayonnement solaire [Termes IGN] température Résumé : (Auteur) Climate maps have been widely used for the construction of species distribution models. These maps derive from interpolation of data collected by meteorological stations. The sparse distribution of stations generates maps with coarse spatial resolution that are unable to detect microclimates or areas that can serve as plant or animal refuges. This work proposes a method for downscaling temperature maps using the solar radiation falling upon hillsides as predictor for the influence of relief on local variability. Solar irradiance is estimated from a digital elevation model of the study area using a routine based on analytical hillshading. Some examples of downscaling from 1 km to 25 m spatial resolution are shown. The results are compared with the surface temperature maps from Landsat 8 satellite imagery. Numéro de notice : A2018-134 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1338620 Date de publication en ligne : 19/06/2017 En ligne : https://doi.org/10.1080/15230406.2017.1338620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89666 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The effects of visual realism, spatial abilities, and competition on performance in map-based route learning in men / Arzu Çöltekin in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 4 (July 2018) . - pp 339 - 353 Titre : The effects of visual realism, spatial abilities, and competition on performance in map-based route learning in men Type de document : Article/Communication Auteurs : Arzu Çöltekin, Auteur ; Rebecca Francelet ; Kai-Florian Richter, Auteur ; John Thoresen, Auteur ; Sara Irina Fabrikant, Auteur Année de publication : 2018 Article en page(s) : pp 339 - 353 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte routière [Termes IGN] lecture de carte [Termes IGN] rendu réaliste [Termes IGN] représentation spatiale [Termes IGN] spatiocarte [Termes IGN] test de performance [Termes IGN] utilisateur civil [Vedettes matières IGN] Cartologie Résumé : (Auteur) We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users. Numéro de notice : A2018-135 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1344569 Date de publication en ligne : 04/07/2017 En ligne : https://doi.org/10.1080/15230406.2017.1344569 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89675 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment / Bernd Resch in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 4 (July 2018) . - pp 362 - 376 Titre : Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment Type de document : Article/Communication Auteurs : Bernd Resch, Auteur ; Florian Usländer, Auteur ; Clemens Havas, Auteur Année de publication : 2018 Article en page(s) : pp 362 - 376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] catastrophe naturelle [Termes IGN] dommage matériel [Termes IGN] données issues des réseaux sociaux [Termes IGN] empreinte [Termes IGN] gestion de crise Résumé : (Auteur) Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time. Numéro de notice : A2018-136 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1356242 Date de publication en ligne : 03/08/2017 En ligne : https://doi.org/10.1080/15230406.2017.1356242 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89678 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible