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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 432 - 451 Titre : Comparing pedestrians’ gaze behavior in desktop and in real environments Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Hua Liao, Auteur ; Bing Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 432 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] comportement [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] lecture de carte [Termes descripteurs IGN] monde virtuel [Termes descripteurs IGN] navigation pédestre [Termes descripteurs IGN] oculométrie [Termes descripteurs IGN] piéton [Termes descripteurs IGN] test statistique [Termes descripteurs IGN] travail [Termes descripteurs IGN] vision par ordinateur [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This research is motivated by the widespread use of desktop environments in the lab and by the recent trend of conducting real-world eye-tracking experiments to investigate pedestrian navigation. Despite the existing significant differences between the real world and the desktop environments, how pedestrians’ visual behavior in real environments differs from that in desktop environments is still not well understood. Here, we report a study that recorded eye movements for a total of 82 participants while they were performing five common navigation tasks in an unfamiliar urban environment (N = 39) and in a desktop environment (N = 43). By analyzing where the participants allocated their visual attention, what objects they fixated on, and how they transferred their visual attention among objects during navigation, we found similarities and significant differences in the general fixation indicators, spatial fixation distributions and attention to the objects of interest. The results contribute to the ongoing debate over the validity of using desktop environments to investigate pedestrian navigation by providing insights into how pedestrians allocate their attention to visual stimuli to accomplish navigation tasks in the two environments. Numéro de notice : A2020-488 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.176251 date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/15230406.2020.1762513 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95658 [article]

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Recognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 400 - 417 Titre : Recognition of building group patterns using graph convolutional network Type de document : Article/Communication Auteurs : Rong Zhao, Auteur ; Tinghua Ai, Auteur ; Wenhao Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 400 - 417 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données topographiques [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] généralisation du bâti [Termes descripteurs IGN] graphe [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] reconnaissance de formes Résumé : (auteur) Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods. Numéro de notice : A2020-510 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1757512 date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1080/15230406.2020.1757512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95663 [article]

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A name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 4 (August 2020) . - 22 p. Titre : A name‐led approach to profile urban places based on geotagged Twitter data Type de document : Article/Communication Auteurs : Juntao Lai, Auteur ; Guy Lansley, Auteur ; James Haworth, Auteur ; Tao Cheng, Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] Londres [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] site urbain [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] Twitter Résumé : (auteur) Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space. Numéro de notice : A2020-670 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12599 date de publication en ligne : 05/12/2019 En ligne : https://doi.org/10.1111/tgis.12599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96155 [article]

Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 16 p. Titre : Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method Type de document : Article/Communication Auteurs : Zhenzhong Peng, Auteur ; Ru Wang, Auteur ; Lingbo Liu, Auteur ; Hao Wu, Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] bati [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] diagramme de Voronoï [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données démographiques [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] petite échelle [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] téléphone intelligent Résumé : (auteur) Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data. Numéro de notice : A2020-315 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060344 date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060344 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95170 [article]

A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 80 (March 2020) Titre : A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data Type de document : Article/Communication Auteurs : Sheng Hu, Auteur ; Zhanjun He, Auteur ; Liang Wu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] données massives [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] traitement du langage naturel [Termes descripteurs IGN] Wuhan (Chine) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed. Numéro de notice : A2020-191 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101442 date de publication en ligne : 15/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101442 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94853 [article]

Pour la recherche urbaine / Félix Ardisson (2020)  

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Cartographic delimitation of the city centre using mental sketches / Kamil Nieścioruk in Cartographic journal (the), Vol 56 n° 4 (November 2019)  

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A graph-based approach for the structural analysis of road and building layouts / Mathieu Domingo in Geo-spatial Information Science, vol 22 n° 1 (March 2019)  

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Spatialities, social Media and sentiment analysis: Exploring the potential of the detection tool SentiStrength / Christina Reithmeier in GI Forum, vol 2018 n° 2 ([01/09/2018])  

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Saillance visuelle en imagerie urbaine / Kamel Guissous (2018) 

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Utilisation de véhicules traceurs pour la détection et la localisation de l'infrastructure routière par apprentissage automatique / Yann Méneroux (2018) 

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Urban building reconstruction from raw LiDAR point data / Cheng Yi in Computer-Aided Design, vol 9x (2017)  

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Le commentaire de carte topographique / Camille Tiano (2017)

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L'empire des zonages / Françoise de Blomac in DécryptaGéo le mag, n° 154 (janvier 2014)

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Towards urban 3D modeling using mobile LiDAR and images / Ruisheng Wang (2011)  

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